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"_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } } }, "cells": [ { "cell_type": "code", "execution_count": 109, "metadata": { "id": "RZ5fCICBHu5V" }, "outputs": [], "source": [ "#!pip install -q albumentations opencv-python" ] }, { "cell_type": "code", "source": [ "# !pip install -q diffusers transformers accelerate safetensors" ], "metadata": { "id": "jUQ_N-Hmac7x" }, "execution_count": 110, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Import Libraries" ], "metadata": { "id": "SxupTcpzRBrY" } }, { "cell_type": "code", "source": [ "import os\n", "import cv2\n", "import random\n", "import zipfile\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "\n", "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", "from tensorflow.keras.preprocessing import image\n", "\n", "from tensorflow.keras.utils import image_dataset_from_directory\n", "\n", "from sklearn.metrics import (\n", " confusion_matrix,\n", " classification_report,\n", " accuracy_score,\n", " precision_score,\n", " recall_score,\n", " f1_score\n", ")\n", "\n", "import albumentations as A\n", "import seaborn as sns" ], "metadata": { "id": "iX6cEsAUQ-D_" }, "execution_count": 111, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Set Random Seed" ], "metadata": { "id": "6NbWrxRlRGYv" } }, { "cell_type": "code", "source": [ "SEED = 42\n", "\n", "random.seed(SEED)\n", "np.random.seed(SEED)\n", "tf.random.set_seed(SEED)" ], "metadata": { "id": "PwjVkJGGQ-Bn" }, "execution_count": 112, "outputs": [] }, { "cell_type": "code", "source": [ "IMG_HEIGHT = 224\n", "IMG_WIDTH = 224\n", "\n", "BATCH_SIZE = 32" ], "metadata": { "id": "qQQb3jZIQ9_J" }, "execution_count": 113, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Dataset Paths" ], "metadata": { "id": "apBnDbCcTzc8" } }, { "cell_type": "code", "source": [ "train_path = \"/content/drive/MyDrive/Waste Data/DATASET/TRAIN\"\n", "test_path = \"/content/drive/MyDrive/Waste Data/DATASET/TEST\"" ], "metadata": { "id": "DoMdXQn8Q98w" }, "execution_count": 114, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Advanced Data Augmentation" ], "metadata": { "id": "kN6CWe5_T100" } }, { "cell_type": "code", "source": [ "advanced_augmentation = A.Compose([\n", "\n", " # ------------------------\n", " # AutoAugment Approximation\n", " # ------------------------\n", "\n", " A.RandomBrightnessContrast(\n", " brightness_limit=0.20,\n", " contrast_limit=0.20,\n", " p=0.5\n", " ),\n", "\n", " A.HueSaturationValue(\n", " hue_shift_limit=15,\n", " sat_shift_limit=20,\n", " val_shift_limit=15,\n", " p=0.4\n", " ),\n", "\n", " A.Affine(\n", " scale=(0.90,1.10),\n", " translate_percent=(-0.10,0.10),\n", " rotate=(-25,25),\n", " shear=(-10,10),\n", " p=0.7\n", " ),\n", "\n", " A.HorizontalFlip(p=0.5),\n", "\n", " # ------------------------\n", " # Advanced Blur\n", " # ------------------------\n", "\n", " A.OneOf([\n", "\n", " A.GaussianBlur(blur_limit=(3,7), p=1),\n", "\n", " A.MotionBlur(blur_limit=7, p=1),\n", "\n", " A.MedianBlur(blur_limit=5, p=1)\n", "\n", " ], p=0.40),\n", "\n", " # ------------------------\n", " # Advanced Noise\n", " # ------------------------\n", "\n", " A.OneOf([\n", "\n", " A.GaussNoise(\n", " std_range=(0.05,0.15),\n", " p=1\n", " ),\n", "\n", " A.ISONoise(\n", " color_shift=(0.01,0.05),\n", " intensity=(0.10,0.40),\n", " p=1\n", " )\n", "\n", " ], p=0.40),\n", "\n", " # ------------------------\n", " # Random Erasing\n", " # ------------------------\n", "\n", " A.CoarseDropout(\n", "\n", " num_holes_range=(1,4),\n", "\n", " hole_height_range=(20,60),\n", "\n", " hole_width_range=(20,60),\n", "\n", " fill=0,\n", "\n", " p=0.40\n", "\n", " ),\n", "\n", " A.Normalize()\n", "\n", "])" ], "metadata": { "id": "Y46knq-AQ96X" }, "execution_count": 115, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Apply Augmentation" ], "metadata": { "id": "VEBxHzxHUMT4" } }, { "cell_type": "code", "source": [ "def augment_image(img):\n", "\n", " img = img.astype(np.uint8)\n", "\n", " try:\n", " augmented = advanced_augmentation(image=img)\n", " return augmented[\"image\"]\n", " except Exception as e:\n", " print(f\"Warning: Augmentation failed for an image. Returning original image. Error: {e}\")\n", " return img" ], "metadata": { "id": "Gfs75T39Q93_" }, "execution_count": 116, "outputs": [] }, { "cell_type": "markdown", "source": [ "### MixUp Augmentation" ], "metadata": { "id": "OiQ76IG3UOiE" } }, { "cell_type": "code", "source": [ "def mixup(images, labels, alpha=0.2):\n", "\n", " lam = np.random.beta(alpha, alpha)\n", "\n", " batch_size = images.shape[0]\n", "\n", " indices = np.random.permutation(batch_size)\n", "\n", " mixed_images = lam * images + (1 - lam) * images[indices]\n", "\n", " labels1 = labels\n", " labels2 = labels[indices]\n", "\n", " return mixed_images, labels1, labels2, lam" ], "metadata": { "id": "qmH4QdbFQ91Q" }, "execution_count": 117, "outputs": [] }, { "cell_type": "markdown", "source": [ "### CutMix Augmentation" ], "metadata": { "id": "P5ajQ3XPUR8n" } }, { "cell_type": "code", "source": [ "def rand_bbox(size, lam):\n", "\n", " H = size[1]\n", " W = size[2]\n", "\n", " cut_ratio = np.sqrt(1 - lam)\n", "\n", " cut_w = int(W * cut_ratio)\n", " cut_h = int(H * cut_ratio)\n", "\n", " cx = np.random.randint(W)\n", " cy = np.random.randint(H)\n", "\n", " x1 = np.clip(cx - cut_w // 2, 0, W)\n", " y1 = np.clip(cy - cut_h // 2, 0, H)\n", " x2 = np.clip(cx + cut_w // 2, 0, W)\n", " y2 = np.clip(cy + cut_h // 2, 0, H)\n", "\n", " return x1, y1, x2, y2" ], "metadata": { "id": "bg6064vZQ9y5" }, "execution_count": 118, "outputs": [] }, { "cell_type": "code", "source": [ "def cutmix(images, labels, alpha=1.0):\n", "\n", " lam = np.random.beta(alpha, alpha)\n", "\n", " batch_size = images.shape[0]\n", "\n", " rand_index = np.random.permutation(batch_size)\n", "\n", " shuffled_images = images[rand_index]\n", " shuffled_labels = labels[rand_index]\n", "\n", " x1, y1, x2, y2 = rand_bbox(images.shape, lam)\n", "\n", " images[:, y1:y2, x1:x2, :] = shuffled_images[:, y1:y2, x1:x2, :]\n", "\n", " lam = 1 - ((x2 - x1) * (y2 - y1) / (images.shape[1] * images.shape[2]))\n", "\n", " return images, labels, shuffled_labels, lam" ], "metadata": { "id": "WD8hCofFQ9wX" }, "execution_count": 119, "outputs": [] }, { "cell_type": "code", "source": [ "def augment_image_robust(img_tensor):\n", " img = img_tensor.numpy()\n", "\n", " img = img.astype(np.uint8)\n", "\n", " try:\n", " augmented = advanced_augmentation(image=img)[\"image\"]\n", " return augmented.astype(np.float32)\n", " except Exception as e:\n", " print(f\"Warning: Augmentation failed for an image. Returning original image. Error: {e}\")\n", " return img.astype(np.float32)\n", "\n", "def tf_augment(images, labels):\n", " augmented_images = tf.map_fn(\n", " fn=lambda img: tf.py_function(\n", " func=augment_image_robust,\n", " inp=[img],\n", " Tout=tf.float32 # Output dtype\n", " ),\n", " elems=images,\n", " fn_output_signature=tf.TensorSpec(shape=(IMG_HEIGHT, IMG_WIDTH, 3), dtype=tf.float32)\n", " )\n", "\n", " augmented_images.set_shape((None, IMG_HEIGHT, IMG_WIDTH, 3))\n", " return augmented_images, labels" ], "metadata": { "id": "Y7k_EaXzQ9t_" }, "execution_count": 120, "outputs": [] }, { "cell_type": "code", "source": [ "train_dataset = image_dataset_from_directory(\n", "\n", " train_path,\n", "\n", " image_size=(224,224),\n", "\n", " batch_size=BATCH_SIZE,\n", "\n", " label_mode=\"binary\",\n", "\n", " shuffle=True,\n", "\n", " seed=SEED\n", "\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "D9YOdqsRQ9rn", "outputId": "bf1091a0-518a-4341-a88d-cd524e6ba554" }, "execution_count": 121, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Found 22576 files belonging to 2 classes.\n" ] } ] }, { "cell_type": "code", "source": [ "test_dataset = image_dataset_from_directory(\n", "\n", " test_path,\n", "\n", " image_size=(224,224),\n", "\n", " batch_size=BATCH_SIZE,\n", "\n", " label_mode=\"binary\",\n", "\n", " shuffle=False\n", "\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-dUO2SE9Q9pX", "outputId": "96a10042-2352-4ffb-8d9d-b9c467c57d74" }, "execution_count": 122, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Found 2513 files belonging to 2 classes.\n" ] } ] }, { "cell_type": "code", "source": [ "train_dataset = train_dataset.map(\n", " tf_augment,\n", " num_parallel_calls=tf.data.AUTOTUNE\n", ")\n", "\n", "train_dataset = train_dataset.prefetch(\n", " tf.data.AUTOTUNE\n", ")\n", "\n", "test_dataset = test_dataset.prefetch(\n", " tf.data.AUTOTUNE\n", ")" ], "metadata": { "id": "S3kgE2rHQ9mv" }, "execution_count": 123, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Generative AI-based Augmentation" ], "metadata": { "id": "JApYyDaYaf6m" } }, { "cell_type": "code", "source": [ "import os\n", "import torch\n", "\n", "from diffusers import StableDiffusionPipeline" ], "metadata": { "id": "UWWNEepralZ5" }, "execution_count": 124, "outputs": [] }, { "cell_type": "code", "source": [ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "pipe = StableDiffusionPipeline.from_pretrained(\n", "\n", " \"runwayml/stable-diffusion-v1-5\",\n", "\n", " torch_dtype=torch.float16 if device==\"cuda\" else torch.float32\n", "\n", ")\n", "\n", "pipe = pipe.to(device)\n", "\n", "print(\"Stable Diffusion Loaded Successfully\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 130, "referenced_widgets": [ "6376726964e848968bd75f5349ebf108", "71bbf08cfbe349a1b60a7b3f388ad5e9", "1dbcf1645ef442028c5b6dcde00db1b4", "09d65ccc2be4426b814522e15074e9a2", "6c52191257ed46cf82908a5ee641cef3", "d77040130f4b49dca57b860bf0543de7", "b229f7b9a1224f8393c62a541bb9cdb2", "2bc428e5b9d84c988a5e4f5b833feef2", "6e032ee7ae31447a8abd7e228cf0d004", "195907faa1a441819eafac4755df24aa", "02f347e33940437ab829fd478a6e290b", "962c3d6ddbc3488b96b67024fe22587a", "8603d92266914c1bab325283c50d7ef5", "ca3b895f2bff4241b0b5c122680364cd", "4723412d45154a6ca153a96c7bb23e30", "6d5132a3f6a54a71b452564f20a84dad", "bf10200b856c46e5977637e1b8a960aa", "e25464d72d6542a081681fc9bf73ef13", "6145b906c31f46d6be57160b8d573396", "80889c8c04464ac580fda45bdb9524f1", "ee2379ce892b426bbfe3bbe7db43fa0e", "9cde6454adb24a8897a85fa59094dc31", "013cdab9d34d466e92b00b7fd8114f04", "2b9ed2788bb54fda88e4411cdd40f9be", "0484ead239994b16a9361066ccc5ac07", "6cb632cff4c84c1f8eb3e26773c9ef65", "a5f16a42890645d68da307c9a224439b", "d0f9f85a8b7c4078bc990bbc2ea3d471", "1827524aaff845ecb7a3e6f3430d3bae", "2b710a8172c54bf99889a7b14bac6493", "12475010ffbe4ba79a5ce782e62d6d11", "431209af3f0245a7b61613a15d34a93b", "469ff674b1a244418bdab42930092d56" ] }, "id": "EPOzCC4ralch", "outputId": "e0ab9d65-c591-4f52-844a-71859ce6ae49" }, "execution_count": 125, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading pipeline components...: 0%| | 0/7 [00:00" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# Model 1 – Custom CNN" ], "metadata": { "id": "Qp7GkYP-UHTE" } }, { "cell_type": "code", "source": [ "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import (\n", " Conv2D,\n", " MaxPooling2D,\n", " BatchNormalization,\n", " Dense,\n", " Dropout,\n", " Flatten\n", ")\n", "\n", "cnn_model = Sequential([\n", "\n", " Conv2D(32, (3,3), activation='relu',\n", " input_shape=(224,224,3)),\n", " BatchNormalization(),\n", " MaxPooling2D((2,2)),\n", "\n", " Conv2D(64, (3,3), activation='relu'),\n", " BatchNormalization(),\n", " MaxPooling2D((2,2)),\n", "\n", " Conv2D(128, (3,3), activation='relu'),\n", " BatchNormalization(),\n", " MaxPooling2D((2,2)),\n", "\n", " Conv2D(256, (3,3), activation='relu'),\n", " BatchNormalization(),\n", " MaxPooling2D((2,2)),\n", "\n", " Flatten(),\n", "\n", " Dense(512, activation='relu'),\n", " Dropout(0.5),\n", "\n", " Dense(128, activation='relu'),\n", " Dropout(0.3),\n", "\n", " Dense(1, activation='sigmoid')\n", "\n", "])\n", "\n", "cnn_model.summary()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 886 }, "id": "tieShrIsQ9h3", "outputId": "51ea2efa-743a-41e8-f0a8-ad50ad4c8459" }, "execution_count": 131, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1mModel: \"sequential_2\"\u001b[0m\n" ], "text/html": [ "
Model: \"sequential_2\"\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ batch_normalization_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ batch_normalization_12 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ max_pooling2d_9 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ batch_normalization_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ max_pooling2d_10 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m295,168\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ batch_normalization_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ max_pooling2d_11 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m36864\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_15 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m18,874,880\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout_10 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_16 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m65,664\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout_11 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_17 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m129\u001b[0m │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ], "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_8 (Conv2D)               │ (None, 222, 222, 32)   │           896 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_11          │ (None, 222, 222, 32)   │           128 │\n",
              "│ (BatchNormalization)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_8 (MaxPooling2D)  │ (None, 111, 111, 32)   │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_9 (Conv2D)               │ (None, 109, 109, 64)   │        18,496 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_12          │ (None, 109, 109, 64)   │           256 │\n",
              "│ (BatchNormalization)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_9 (MaxPooling2D)  │ (None, 54, 54, 64)     │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_10 (Conv2D)              │ (None, 52, 52, 128)    │        73,856 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_13          │ (None, 52, 52, 128)    │           512 │\n",
              "│ (BatchNormalization)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_10 (MaxPooling2D) │ (None, 26, 26, 128)    │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_11 (Conv2D)              │ (None, 24, 24, 256)    │       295,168 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_14          │ (None, 24, 24, 256)    │         1,024 │\n",
              "│ (BatchNormalization)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_11 (MaxPooling2D) │ (None, 12, 12, 256)    │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_2 (Flatten)             │ (None, 36864)          │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_15 (Dense)                │ (None, 512)            │    18,874,880 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_10 (Dropout)            │ (None, 512)            │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_16 (Dense)                │ (None, 128)            │        65,664 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_11 (Dropout)            │ (None, 128)            │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_17 (Dense)                │ (None, 1)              │           129 │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19,331,009\u001b[0m (73.74 MB)\n" ], "text/html": [ "
 Total params: 19,331,009 (73.74 MB)\n",
              "
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 Trainable params: 19,330,049 (73.74 MB)\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m960\u001b[0m (3.75 KB)\n" ], "text/html": [ "
 Non-trainable params: 960 (3.75 KB)\n",
              "
\n" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "cnn_model.compile(\n", "\n", " optimizer=tf.keras.optimizers.Adam(\n", " learning_rate=0.0001\n", " ),\n", "\n", " loss='binary_crossentropy',\n", "\n", " metrics=['accuracy']\n", "\n", ")" ], "metadata": { "id": "EYmv1LZHQ9fn" }, "execution_count": 132, "outputs": [] }, { "cell_type": "code", "source": [ "from tensorflow.keras.callbacks import (\n", " EarlyStopping,\n", " ReduceLROnPlateau,\n", " ModelCheckpoint\n", ")\n", "\n", "cnn_callbacks = [\n", "\n", " EarlyStopping(\n", " monitor='val_loss',\n", " patience=5,\n", " restore_best_weights=True\n", " ),\n", "\n", " ReduceLROnPlateau(\n", " monitor='val_loss',\n", " factor=0.2,\n", " patience=3,\n", " verbose=1\n", " ),\n", "\n", " ModelCheckpoint(\n", " \"best_custom_cnn.keras\",\n", " monitor=\"val_accuracy\",\n", " save_best_only=True\n", " )\n", "\n", "]" ], "metadata": { "id": "am_HsHkbQ9dA" }, "execution_count": 133, "outputs": [] }, { "cell_type": "code", "source": [ "cnn_history = cnn_model.fit(\n", "\n", " train_dataset,\n", "\n", " validation_data=test_dataset,\n", "\n", " epochs=5,\n", "\n", " callbacks=cnn_callbacks\n", "\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qy4h-RXpSXzW", "outputId": "d06a789c-ee25-43c2-a4d6-9fdbd0039322" }, "execution_count": 134, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 261ms/step - accuracy: 0.7473 - loss: 0.6560 - val_accuracy: 0.8587 - val_loss: 2.7706 - learning_rate: 1.0000e-04\n", "Epoch 2/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 116ms/step - accuracy: 0.7771 - loss: 0.5026 - val_accuracy: 0.6387 - val_loss: 5.8798 - learning_rate: 1.0000e-04\n", "Epoch 3/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 116ms/step - accuracy: 0.7908 - loss: 0.4787 - val_accuracy: 0.7115 - val_loss: 4.1174 - learning_rate: 1.0000e-04\n", "Epoch 4/5\n", "\u001b[1m704/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - accuracy: 0.8067 - loss: 0.4471\n", "Epoch 4: ReduceLROnPlateau reducing learning rate to 1.9999999494757503e-05.\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 116ms/step - accuracy: 0.8055 - loss: 0.4500 - val_accuracy: 0.6188 - val_loss: 14.2952 - learning_rate: 1.0000e-04\n", "Epoch 5/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 117ms/step - accuracy: 0.8234 - loss: 0.4080 - val_accuracy: 0.5690 - val_loss: 22.3724 - learning_rate: 2.0000e-05\n" ] } ] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(8,5))\n", "plt.plot(cnn_history.history['accuracy'],label='Training Accuracy')\n", "plt.plot(cnn_history.history['val_accuracy'],label='Validation Accuracy')\n", "plt.title(\"Custom CNN Accuracy\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Accuracy\")\n", "plt.legend()\n", "plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "Hn65MGpVSbD9", "outputId": "8e64726f-3d64-40c4-9226-2b7a860bf8bf" }, "execution_count": 135, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(8,5))\n", "plt.plot(cnn_history.history['loss'],label='Training Loss')\n", "plt.plot(cnn_history.history['val_loss'],label='Validation Loss')\n", "plt.title(\"Custom CNN Loss\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "plt.legend()\n", "plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "g98zPEG6Ue7W", "outputId": "49a2ac2c-e128-48a5-8b78-b8b285ba280c" }, "execution_count": 136, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "cnn_loss, cnn_accuracy = cnn_model.evaluate(test_dataset)\n", "print(\"Test Loss :\", cnn_loss)\n", "print(\"Test Accuracy :\", cnn_accuracy)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "n84vrNfZUe9-", "outputId": "c714cbd9-71fa-4e08-9f91-af192719299c" }, "execution_count": 137, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 51ms/step - accuracy: 0.8587 - loss: 2.7706\n", "Test Loss : 2.7706286907196045\n", "Test Accuracy : 0.8587346076965332\n" ] } ] }, { "cell_type": "code", "source": [ "y_true = []\n", "y_pred = []\n", "\n", "for images, labels in test_dataset:\n", " predictions = cnn_model.predict(images, verbose=0)\n", " predictions = (predictions > 0.5).astype(int)\n", " y_true.extend(labels.numpy())\n", " y_pred.extend(predictions.flatten())" ], "metadata": { "id": "G-7qQajlUfAS" }, "execution_count": 138, "outputs": [] }, { "cell_type": "code", "source": [ "cm = confusion_matrix(y_true, y_pred)\n", "plt.figure(figsize=(6,5))\n", "sns.heatmap(\n", "\n", " cm,\n", "\n", " annot=True,\n", "\n", " fmt=\"d\",\n", "\n", " cmap=\"Blues\",\n", "\n", " xticklabels=[\"Organic\",\"Recyclable\"],\n", "\n", " yticklabels=[\"Organic\",\"Recyclable\"]\n", "\n", ")\n", "\n", "plt.xlabel(\"Predicted\")\n", "plt.ylabel(\"Actual\")\n", "plt.title(\"Custom CNN Confusion Matrix\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "Hyl4WlyzUfCn", "outputId": "1d0f163b-a454-4aeb-bb86-bb9293311ea9" }, "execution_count": 139, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "print(classification_report(y_true, y_pred,target_names=[\"Organic\",\"Recyclable\"]))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FxzlsE6_UfE7", "outputId": "5b636d59-95ce-4faa-badd-a6a4eeda6399" }, "execution_count": 140, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " Organic 0.84 0.92 0.88 1401\n", " Recyclable 0.89 0.78 0.83 1112\n", "\n", " accuracy 0.86 2513\n", " macro avg 0.86 0.85 0.85 2513\n", "weighted avg 0.86 0.86 0.86 2513\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "cnn_acc = accuracy_score(y_true, y_pred)\n", "cnn_precision = precision_score(y_true, y_pred)\n", "cnn_recall = recall_score(y_true, y_pred)\n", "cnn_f1 = f1_score(y_true, y_pred)\n", "\n", "print(\"Accuracy :\", round(cnn_acc,4))\n", "print(\"Precision :\", round(cnn_precision,4))\n", "print(\"Recall :\", round(cnn_recall,4))\n", "print(\"F1 Score :\", round(cnn_f1,4))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "U7szOLa-UfIC", "outputId": "9fe58570-6082-48ad-97bc-2643ae5174c7" }, "execution_count": 141, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy : 0.8587\n", "Precision : 0.8866\n", "Recall : 0.7806\n", "F1 Score : 0.8302\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Model 2 – MobileNetV2 (Transfer Learning)" ], "metadata": { "id": "thw74DMmVJED" } }, { "cell_type": "code", "source": [ "from tensorflow.keras.applications import MobileNetV2\n", "from tensorflow.keras.layers import (\n", " GlobalAveragePooling2D,\n", " Dense,\n", " Dropout,\n", " BatchNormalization\n", ")\n", "from tensorflow.keras.models import Model" ], "metadata": { "id": "9kpRGbzLVJ8y" }, "execution_count": 142, "outputs": [] }, { "cell_type": "code", "source": [ "base_model = MobileNetV2(\n", "\n", " weights=\"imagenet\",\n", "\n", " include_top=False,\n", "\n", " input_shape=(224,224,3)\n", "\n", ")\n", "\n", "base_model.summary()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "7N1mes1SVL49", "outputId": "462ded2e-0711-4efb-e619-f1dbbbaf7bc8" }, "execution_count": 143, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1mModel: \"mobilenetv2_1.00_224\"\u001b[0m\n" ], "text/html": [ "
Model: \"mobilenetv2_1.00_224\"\n",
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\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n", "│ input_layer_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ - │\n", "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ Conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m864\u001b[0m │ input_layer_6[\u001b[38;5;34m0\u001b[0m]… │\n", "│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ bn_Conv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ Conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ Conv1_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ bn_Conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ expanded_conv_dept… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ Conv1_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ expanded_conv_dept… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ expanded_conv_de… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ expanded_conv_dept… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ expanded_conv_de… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ expanded_conv_proj… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ expanded_conv_de… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m16\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ expanded_conv_proj… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m64\u001b[0m │ expanded_conv_pr… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m16\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ expanded_conv_pr… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ block_1_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_1_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m113\u001b[0m, \u001b[38;5;34m113\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_1_expand_r… │\n", "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m864\u001b[0m │ block_1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ block_1_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_1_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m2,304\u001b[0m │ block_1_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m24\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ block_1_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m24\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ block_1_project_… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ block_2_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_2_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,296\u001b[0m │ block_2_expand_r… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ block_2_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_2_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ block_2_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m24\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ block_2_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m24\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_1_project_… │\n", "│ │ \u001b[38;5;34m24\u001b[0m) │ │ block_2_project_… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ block_2_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ block_3_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_3_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m57\u001b[0m, \u001b[38;5;34m57\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_3_expand_r… │\n", "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,296\u001b[0m │ block_3_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ block_3_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_3_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m4,608\u001b[0m │ block_3_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ block_3_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ block_3_project_… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ block_4_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_4_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,728\u001b[0m │ block_4_expand_r… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ block_4_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_4_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ block_4_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ block_4_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_3_project_… │\n", "│ │ \u001b[38;5;34m32\u001b[0m) │ │ block_4_project_… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ block_4_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ block_5_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_5_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,728\u001b[0m │ block_5_expand_r… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ block_5_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_5_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ block_5_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ block_5_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_4_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ \u001b[38;5;34m32\u001b[0m) │ │ block_5_project_… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ block_5_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ block_6_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_6_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_6_expand_r… │\n", "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,728\u001b[0m │ block_6_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ block_6_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_6_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ block_6_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ block_6_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ block_6_project_… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_7_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_7_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ block_7_expand_r… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_7_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_7_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ block_7_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ block_7_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_6_project_… │\n", "│ │ \u001b[38;5;34m64\u001b[0m) │ │ block_7_project_… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ block_7_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_8_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_8_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ block_8_expand_r… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_8_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_8_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ block_8_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ block_8_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_7_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ \u001b[38;5;34m64\u001b[0m) │ │ block_8_project_… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ block_8_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_9_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_9_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ block_9_expand_r… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_9_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_9_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ block_9_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ block_9_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_8_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ \u001b[38;5;34m64\u001b[0m) │ │ block_9_project_… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ block_9_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_10_expand[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_expand_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_10_expand_… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ block_10_expand_… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_10_depthwi… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_10_depthwi… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ block_10_depthwi… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ block_10_project… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ block_10_project… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,304\u001b[0m │ block_11_expand[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_expand_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_11_expand_… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,184\u001b[0m │ block_11_expand_… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,304\u001b[0m │ block_11_depthwi… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_11_depthwi… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ block_11_depthwi… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ block_11_project… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_10_project… │\n", "│ │ \u001b[38;5;34m96\u001b[0m) │ │ block_11_project… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ block_11_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,304\u001b[0m │ block_12_expand[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_expand_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_12_expand_… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,184\u001b[0m │ block_12_expand_… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,304\u001b[0m │ block_12_depthwi… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_12_depthwi… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ block_12_depthwi… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ block_12_project… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_11_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ │ \u001b[38;5;34m96\u001b[0m) │ │ block_12_project… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ block_12_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,304\u001b[0m │ block_13_expand[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_expand_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_13_expand_… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_13_expand_… │\n", "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m576\u001b[0m) │ \u001b[38;5;34m5,184\u001b[0m │ block_13_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m576\u001b[0m) │ \u001b[38;5;34m2,304\u001b[0m │ block_13_depthwi… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m576\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_13_depthwi… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m92,160\u001b[0m │ block_13_depthwi… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m640\u001b[0m │ block_13_project… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m153,600\u001b[0m │ block_13_project… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m3,840\u001b[0m │ block_14_expand[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_expand_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_14_expand_… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m8,640\u001b[0m │ block_14_expand_… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m3,840\u001b[0m │ block_14_depthwi… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_14_depthwi… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m153,600\u001b[0m │ block_14_depthwi… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m640\u001b[0m │ block_14_project… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_13_project… │\n", "│ │ │ │ block_14_project… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m153,600\u001b[0m │ block_14_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m3,840\u001b[0m │ block_15_expand[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_expand_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_15_expand_… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m8,640\u001b[0m │ block_15_expand_… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m3,840\u001b[0m │ block_15_depthwi… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_15_depthwi… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m153,600\u001b[0m │ block_15_depthwi… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m640\u001b[0m │ block_15_project… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_14_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ │ │ │ block_15_project… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m153,600\u001b[0m │ block_15_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m3,840\u001b[0m │ block_16_expand[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_expand_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_16_expand_… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m8,640\u001b[0m │ block_16_expand_… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m3,840\u001b[0m │ block_16_depthwi… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_16_depthwi… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m307,200\u001b[0m │ block_16_depthwi… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m1,280\u001b[0m │ block_16_project… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ Conv_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m409,600\u001b[0m │ block_16_project… │\n", "│ │ \u001b[38;5;34m1280\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ Conv_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m5,120\u001b[0m │ Conv_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1280\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ out_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ Conv_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ │ \u001b[38;5;34m1280\u001b[0m) │ │ │\n", "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n" ], "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
              "┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_6       │ (None, 224, 224,  │          0 │ -                 │\n",
              "│ (InputLayer)        │ 3)                │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ Conv1 (Conv2D)      │ (None, 112, 112,  │        864 │ input_layer_6[0]… │\n",
              "│                     │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ bn_Conv1            │ (None, 112, 112,  │        128 │ Conv1[0][0]       │\n",
              "│ (BatchNormalizatio… │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ Conv1_relu (ReLU)   │ (None, 112, 112,  │          0 │ bn_Conv1[0][0]    │\n",
              "│                     │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ expanded_conv_dept… │ (None, 112, 112,  │        288 │ Conv1_relu[0][0]  │\n",
              "│ (DepthwiseConv2D)   │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ expanded_conv_dept… │ (None, 112, 112,  │        128 │ expanded_conv_de… │\n",
              "│ (BatchNormalizatio… │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ expanded_conv_dept… │ (None, 112, 112,  │          0 │ expanded_conv_de… │\n",
              "│ (ReLU)              │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ expanded_conv_proj… │ (None, 112, 112,  │        512 │ expanded_conv_de… │\n",
              "│ (Conv2D)            │ 16)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ expanded_conv_proj… │ (None, 112, 112,  │         64 │ expanded_conv_pr… │\n",
              "│ (BatchNormalizatio… │ 16)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_expand      │ (None, 112, 112,  │      1,536 │ expanded_conv_pr… │\n",
              "│ (Conv2D)            │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_expand_BN   │ (None, 112, 112,  │        384 │ block_1_expand[0… │\n",
              "│ (BatchNormalizatio… │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_expand_relu │ (None, 112, 112,  │          0 │ block_1_expand_B… │\n",
              "│ (ReLU)              │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_pad         │ (None, 113, 113,  │          0 │ block_1_expand_r… │\n",
              "│ (ZeroPadding2D)     │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_depthwise   │ (None, 56, 56,    │        864 │ block_1_pad[0][0] │\n",
              "│ (DepthwiseConv2D)   │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_depthwise_… │ (None, 56, 56,    │        384 │ block_1_depthwis… │\n",
              "│ (BatchNormalizatio… │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_depthwise_… │ (None, 56, 56,    │          0 │ block_1_depthwis… │\n",
              "│ (ReLU)              │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_project     │ (None, 56, 56,    │      2,304 │ block_1_depthwis… │\n",
              "│ (Conv2D)            │ 24)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_project_BN  │ (None, 56, 56,    │         96 │ block_1_project[… │\n",
              "│ (BatchNormalizatio… │ 24)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_expand      │ (None, 56, 56,    │      3,456 │ block_1_project_… │\n",
              "│ (Conv2D)            │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_expand_BN   │ (None, 56, 56,    │        576 │ block_2_expand[0… │\n",
              "│ (BatchNormalizatio… │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_expand_relu │ (None, 56, 56,    │          0 │ block_2_expand_B… │\n",
              "│ (ReLU)              │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_depthwise   │ (None, 56, 56,    │      1,296 │ block_2_expand_r… │\n",
              "│ (DepthwiseConv2D)   │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_depthwise_… │ (None, 56, 56,    │        576 │ block_2_depthwis… │\n",
              "│ (BatchNormalizatio… │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_depthwise_… │ (None, 56, 56,    │          0 │ block_2_depthwis… │\n",
              "│ (ReLU)              │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_project     │ (None, 56, 56,    │      3,456 │ block_2_depthwis… │\n",
              "│ (Conv2D)            │ 24)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_project_BN  │ (None, 56, 56,    │         96 │ block_2_project[… │\n",
              "│ (BatchNormalizatio… │ 24)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_add (Add)   │ (None, 56, 56,    │          0 │ block_1_project_… │\n",
              "│                     │ 24)               │            │ block_2_project_… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_expand      │ (None, 56, 56,    │      3,456 │ block_2_add[0][0] │\n",
              "│ (Conv2D)            │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_expand_BN   │ (None, 56, 56,    │        576 │ block_3_expand[0… │\n",
              "│ (BatchNormalizatio… │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_expand_relu │ (None, 56, 56,    │          0 │ block_3_expand_B… │\n",
              "│ (ReLU)              │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_pad         │ (None, 57, 57,    │          0 │ block_3_expand_r… │\n",
              "│ (ZeroPadding2D)     │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_depthwise   │ (None, 28, 28,    │      1,296 │ block_3_pad[0][0] │\n",
              "│ (DepthwiseConv2D)   │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_depthwise_… │ (None, 28, 28,    │        576 │ block_3_depthwis… │\n",
              "│ (BatchNormalizatio… │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_depthwise_… │ (None, 28, 28,    │          0 │ block_3_depthwis… │\n",
              "│ (ReLU)              │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_project     │ (None, 28, 28,    │      4,608 │ block_3_depthwis… │\n",
              "│ (Conv2D)            │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_project_BN  │ (None, 28, 28,    │        128 │ block_3_project[… │\n",
              "│ (BatchNormalizatio… │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_expand      │ (None, 28, 28,    │      6,144 │ block_3_project_… │\n",
              "│ (Conv2D)            │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_expand_BN   │ (None, 28, 28,    │        768 │ block_4_expand[0… │\n",
              "│ (BatchNormalizatio… │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_expand_relu │ (None, 28, 28,    │          0 │ block_4_expand_B… │\n",
              "│ (ReLU)              │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_depthwise   │ (None, 28, 28,    │      1,728 │ block_4_expand_r… │\n",
              "│ (DepthwiseConv2D)   │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_depthwise_… │ (None, 28, 28,    │        768 │ block_4_depthwis… │\n",
              "│ (BatchNormalizatio… │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_depthwise_… │ (None, 28, 28,    │          0 │ block_4_depthwis… │\n",
              "│ (ReLU)              │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_project     │ (None, 28, 28,    │      6,144 │ block_4_depthwis… │\n",
              "│ (Conv2D)            │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_project_BN  │ (None, 28, 28,    │        128 │ block_4_project[… │\n",
              "│ (BatchNormalizatio… │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_add (Add)   │ (None, 28, 28,    │          0 │ block_3_project_… │\n",
              "│                     │ 32)               │            │ block_4_project_… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_expand      │ (None, 28, 28,    │      6,144 │ block_4_add[0][0] │\n",
              "│ (Conv2D)            │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_expand_BN   │ (None, 28, 28,    │        768 │ block_5_expand[0… │\n",
              "│ (BatchNormalizatio… │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_expand_relu │ (None, 28, 28,    │          0 │ block_5_expand_B… │\n",
              "│ (ReLU)              │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_depthwise   │ (None, 28, 28,    │      1,728 │ block_5_expand_r… │\n",
              "│ (DepthwiseConv2D)   │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_depthwise_… │ (None, 28, 28,    │        768 │ block_5_depthwis… │\n",
              "│ (BatchNormalizatio… │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_depthwise_… │ (None, 28, 28,    │          0 │ block_5_depthwis… │\n",
              "│ (ReLU)              │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_project     │ (None, 28, 28,    │      6,144 │ block_5_depthwis… │\n",
              "│ (Conv2D)            │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_project_BN  │ (None, 28, 28,    │        128 │ block_5_project[… │\n",
              "│ (BatchNormalizatio… │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_add (Add)   │ (None, 28, 28,    │          0 │ block_4_add[0][0… │\n",
              "│                     │ 32)               │            │ block_5_project_… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_expand      │ (None, 28, 28,    │      6,144 │ block_5_add[0][0] │\n",
              "│ (Conv2D)            │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_expand_BN   │ (None, 28, 28,    │        768 │ block_6_expand[0… │\n",
              "│ (BatchNormalizatio… │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_expand_relu │ (None, 28, 28,    │          0 │ block_6_expand_B… │\n",
              "│ (ReLU)              │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_pad         │ (None, 29, 29,    │          0 │ block_6_expand_r… │\n",
              "│ (ZeroPadding2D)     │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_depthwise   │ (None, 14, 14,    │      1,728 │ block_6_pad[0][0] │\n",
              "│ (DepthwiseConv2D)   │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_depthwise_… │ (None, 14, 14,    │        768 │ block_6_depthwis… │\n",
              "│ (BatchNormalizatio… │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_depthwise_… │ (None, 14, 14,    │          0 │ block_6_depthwis… │\n",
              "│ (ReLU)              │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_project     │ (None, 14, 14,    │     12,288 │ block_6_depthwis… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_project_BN  │ (None, 14, 14,    │        256 │ block_6_project[… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_expand      │ (None, 14, 14,    │     24,576 │ block_6_project_… │\n",
              "│ (Conv2D)            │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_expand_BN   │ (None, 14, 14,    │      1,536 │ block_7_expand[0… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_expand_relu │ (None, 14, 14,    │          0 │ block_7_expand_B… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_depthwise   │ (None, 14, 14,    │      3,456 │ block_7_expand_r… │\n",
              "│ (DepthwiseConv2D)   │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_depthwise_… │ (None, 14, 14,    │      1,536 │ block_7_depthwis… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_depthwise_… │ (None, 14, 14,    │          0 │ block_7_depthwis… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_project     │ (None, 14, 14,    │     24,576 │ block_7_depthwis… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_project_BN  │ (None, 14, 14,    │        256 │ block_7_project[… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_add (Add)   │ (None, 14, 14,    │          0 │ block_6_project_… │\n",
              "│                     │ 64)               │            │ block_7_project_… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_expand      │ (None, 14, 14,    │     24,576 │ block_7_add[0][0] │\n",
              "│ (Conv2D)            │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_expand_BN   │ (None, 14, 14,    │      1,536 │ block_8_expand[0… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_expand_relu │ (None, 14, 14,    │          0 │ block_8_expand_B… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_depthwise   │ (None, 14, 14,    │      3,456 │ block_8_expand_r… │\n",
              "│ (DepthwiseConv2D)   │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_depthwise_… │ (None, 14, 14,    │      1,536 │ block_8_depthwis… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_depthwise_… │ (None, 14, 14,    │          0 │ block_8_depthwis… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_project     │ (None, 14, 14,    │     24,576 │ block_8_depthwis… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_project_BN  │ (None, 14, 14,    │        256 │ block_8_project[… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_add (Add)   │ (None, 14, 14,    │          0 │ block_7_add[0][0… │\n",
              "│                     │ 64)               │            │ block_8_project_… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_expand      │ (None, 14, 14,    │     24,576 │ block_8_add[0][0] │\n",
              "│ (Conv2D)            │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_expand_BN   │ (None, 14, 14,    │      1,536 │ block_9_expand[0… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_expand_relu │ (None, 14, 14,    │          0 │ block_9_expand_B… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_depthwise   │ (None, 14, 14,    │      3,456 │ block_9_expand_r… │\n",
              "│ (DepthwiseConv2D)   │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_depthwise_… │ (None, 14, 14,    │      1,536 │ block_9_depthwis… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_depthwise_… │ (None, 14, 14,    │          0 │ block_9_depthwis… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_project     │ (None, 14, 14,    │     24,576 │ block_9_depthwis… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_project_BN  │ (None, 14, 14,    │        256 │ block_9_project[… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_add (Add)   │ (None, 14, 14,    │          0 │ block_8_add[0][0… │\n",
              "│                     │ 64)               │            │ block_9_project_… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_expand     │ (None, 14, 14,    │     24,576 │ block_9_add[0][0] │\n",
              "│ (Conv2D)            │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_expand_BN  │ (None, 14, 14,    │      1,536 │ block_10_expand[… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_expand_re… │ (None, 14, 14,    │          0 │ block_10_expand_… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_depthwise  │ (None, 14, 14,    │      3,456 │ block_10_expand_… │\n",
              "│ (DepthwiseConv2D)   │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_depthwise… │ (None, 14, 14,    │      1,536 │ block_10_depthwi… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_depthwise… │ (None, 14, 14,    │          0 │ block_10_depthwi… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_project    │ (None, 14, 14,    │     36,864 │ block_10_depthwi… │\n",
              "│ (Conv2D)            │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_project_BN │ (None, 14, 14,    │        384 │ block_10_project… │\n",
              "│ (BatchNormalizatio… │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_expand     │ (None, 14, 14,    │     55,296 │ block_10_project… │\n",
              "│ (Conv2D)            │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_expand_BN  │ (None, 14, 14,    │      2,304 │ block_11_expand[… │\n",
              "│ (BatchNormalizatio… │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_expand_re… │ (None, 14, 14,    │          0 │ block_11_expand_… │\n",
              "│ (ReLU)              │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_depthwise  │ (None, 14, 14,    │      5,184 │ block_11_expand_… │\n",
              "│ (DepthwiseConv2D)   │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_depthwise… │ (None, 14, 14,    │      2,304 │ block_11_depthwi… │\n",
              "│ (BatchNormalizatio… │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_depthwise… │ (None, 14, 14,    │          0 │ block_11_depthwi… │\n",
              "│ (ReLU)              │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_project    │ (None, 14, 14,    │     55,296 │ block_11_depthwi… │\n",
              "│ (Conv2D)            │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_project_BN │ (None, 14, 14,    │        384 │ block_11_project… │\n",
              "│ (BatchNormalizatio… │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_add (Add)  │ (None, 14, 14,    │          0 │ block_10_project… │\n",
              "│                     │ 96)               │            │ block_11_project… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_expand     │ (None, 14, 14,    │     55,296 │ block_11_add[0][… │\n",
              "│ (Conv2D)            │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_expand_BN  │ (None, 14, 14,    │      2,304 │ block_12_expand[… │\n",
              "│ (BatchNormalizatio… │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_expand_re… │ (None, 14, 14,    │          0 │ block_12_expand_… │\n",
              "│ (ReLU)              │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_depthwise  │ (None, 14, 14,    │      5,184 │ block_12_expand_… │\n",
              "│ (DepthwiseConv2D)   │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_depthwise… │ (None, 14, 14,    │      2,304 │ block_12_depthwi… │\n",
              "│ (BatchNormalizatio… │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_depthwise… │ (None, 14, 14,    │          0 │ block_12_depthwi… │\n",
              "│ (ReLU)              │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_project    │ (None, 14, 14,    │     55,296 │ block_12_depthwi… │\n",
              "│ (Conv2D)            │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_project_BN │ (None, 14, 14,    │        384 │ block_12_project… │\n",
              "│ (BatchNormalizatio… │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_add (Add)  │ (None, 14, 14,    │          0 │ block_11_add[0][… │\n",
              "│                     │ 96)               │            │ block_12_project… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_expand     │ (None, 14, 14,    │     55,296 │ block_12_add[0][… │\n",
              "│ (Conv2D)            │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_expand_BN  │ (None, 14, 14,    │      2,304 │ block_13_expand[… │\n",
              "│ (BatchNormalizatio… │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_expand_re… │ (None, 14, 14,    │          0 │ block_13_expand_… │\n",
              "│ (ReLU)              │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_pad        │ (None, 15, 15,    │          0 │ block_13_expand_… │\n",
              "│ (ZeroPadding2D)     │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_depthwise  │ (None, 7, 7, 576) │      5,184 │ block_13_pad[0][… │\n",
              "│ (DepthwiseConv2D)   │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_depthwise… │ (None, 7, 7, 576) │      2,304 │ block_13_depthwi… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_depthwise… │ (None, 7, 7, 576) │          0 │ block_13_depthwi… │\n",
              "│ (ReLU)              │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_project    │ (None, 7, 7, 160) │     92,160 │ block_13_depthwi… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_project_BN │ (None, 7, 7, 160) │        640 │ block_13_project… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_expand     │ (None, 7, 7, 960) │    153,600 │ block_13_project… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_expand_BN  │ (None, 7, 7, 960) │      3,840 │ block_14_expand[… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_expand_re… │ (None, 7, 7, 960) │          0 │ block_14_expand_… │\n",
              "│ (ReLU)              │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_depthwise  │ (None, 7, 7, 960) │      8,640 │ block_14_expand_… │\n",
              "│ (DepthwiseConv2D)   │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_depthwise… │ (None, 7, 7, 960) │      3,840 │ block_14_depthwi… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_depthwise… │ (None, 7, 7, 960) │          0 │ block_14_depthwi… │\n",
              "│ (ReLU)              │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_project    │ (None, 7, 7, 160) │    153,600 │ block_14_depthwi… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_project_BN │ (None, 7, 7, 160) │        640 │ block_14_project… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_add (Add)  │ (None, 7, 7, 160) │          0 │ block_13_project… │\n",
              "│                     │                   │            │ block_14_project… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_expand     │ (None, 7, 7, 960) │    153,600 │ block_14_add[0][… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_expand_BN  │ (None, 7, 7, 960) │      3,840 │ block_15_expand[… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_expand_re… │ (None, 7, 7, 960) │          0 │ block_15_expand_… │\n",
              "│ (ReLU)              │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_depthwise  │ (None, 7, 7, 960) │      8,640 │ block_15_expand_… │\n",
              "│ (DepthwiseConv2D)   │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_depthwise… │ (None, 7, 7, 960) │      3,840 │ block_15_depthwi… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_depthwise… │ (None, 7, 7, 960) │          0 │ block_15_depthwi… │\n",
              "│ (ReLU)              │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_project    │ (None, 7, 7, 160) │    153,600 │ block_15_depthwi… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_project_BN │ (None, 7, 7, 160) │        640 │ block_15_project… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_add (Add)  │ (None, 7, 7, 160) │          0 │ block_14_add[0][… │\n",
              "│                     │                   │            │ block_15_project… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_expand     │ (None, 7, 7, 960) │    153,600 │ block_15_add[0][… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_expand_BN  │ (None, 7, 7, 960) │      3,840 │ block_16_expand[… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_expand_re… │ (None, 7, 7, 960) │          0 │ block_16_expand_… │\n",
              "│ (ReLU)              │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_depthwise  │ (None, 7, 7, 960) │      8,640 │ block_16_expand_… │\n",
              "│ (DepthwiseConv2D)   │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_depthwise… │ (None, 7, 7, 960) │      3,840 │ block_16_depthwi… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_depthwise… │ (None, 7, 7, 960) │          0 │ block_16_depthwi… │\n",
              "│ (ReLU)              │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_project    │ (None, 7, 7, 320) │    307,200 │ block_16_depthwi… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_project_BN │ (None, 7, 7, 320) │      1,280 │ block_16_project… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ Conv_1 (Conv2D)     │ (None, 7, 7,      │    409,600 │ block_16_project… │\n",
              "│                     │ 1280)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ Conv_1_bn           │ (None, 7, 7,      │      5,120 │ Conv_1[0][0]      │\n",
              "│ (BatchNormalizatio… │ 1280)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ out_relu (ReLU)     │ (None, 7, 7,      │          0 │ Conv_1_bn[0][0]   │\n",
              "│                     │ 1280)             │            │                   │\n",
              "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
              "
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Model: \"functional_6\"\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n", "│ input_layer_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ - │\n", "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ Conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m864\u001b[0m │ input_layer_6[\u001b[38;5;34m0\u001b[0m]… │\n", "│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ bn_Conv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ Conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ Conv1_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ bn_Conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ expanded_conv_dept… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ Conv1_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ expanded_conv_dept… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ expanded_conv_de… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ expanded_conv_dept… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ expanded_conv_de… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ expanded_conv_proj… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ expanded_conv_de… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m16\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ expanded_conv_proj… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m64\u001b[0m │ expanded_conv_pr… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m16\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ expanded_conv_pr… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ block_1_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_1_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m113\u001b[0m, \u001b[38;5;34m113\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_1_expand_r… │\n", "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m864\u001b[0m │ block_1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ block_1_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_1_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m2,304\u001b[0m │ block_1_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m24\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_1_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ block_1_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m24\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ block_1_project_… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ block_2_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_2_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,296\u001b[0m │ block_2_expand_r… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ block_2_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_2_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ block_2_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m24\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ block_2_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m24\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_2_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_1_project_… │\n", "│ │ \u001b[38;5;34m24\u001b[0m) │ │ block_2_project_… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ block_2_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ block_3_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_3_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m57\u001b[0m, \u001b[38;5;34m57\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_3_expand_r… │\n", "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,296\u001b[0m │ block_3_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ block_3_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_3_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m144\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m4,608\u001b[0m │ block_3_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_3_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ block_3_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ block_3_project_… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ block_4_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_4_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,728\u001b[0m │ block_4_expand_r… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ block_4_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_4_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ block_4_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ block_4_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_4_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_3_project_… │\n", "│ │ \u001b[38;5;34m32\u001b[0m) │ │ block_4_project_… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ block_4_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ block_5_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_5_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,728\u001b[0m │ block_5_expand_r… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ block_5_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_5_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ block_5_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ block_5_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_5_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_4_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ \u001b[38;5;34m32\u001b[0m) │ │ block_5_project_… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ block_5_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ block_6_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_6_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_6_expand_r… │\n", "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,728\u001b[0m │ block_6_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ block_6_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_6_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ block_6_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_6_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ block_6_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ block_6_project_… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_7_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_7_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ block_7_expand_r… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_7_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_7_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ block_7_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ block_7_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_7_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_6_project_… │\n", "│ │ \u001b[38;5;34m64\u001b[0m) │ │ block_7_project_… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ block_7_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_8_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_8_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ block_8_expand_r… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_8_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_8_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ block_8_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ block_8_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_8_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_7_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ \u001b[38;5;34m64\u001b[0m) │ │ block_8_project_… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ block_8_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_9_expand[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_expand_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_9_expand_B… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ block_9_expand_r… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_9_depthwis… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_9_depthwis… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ block_9_depthwis… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ block_9_project[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_9_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_8_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ \u001b[38;5;34m64\u001b[0m) │ │ block_9_project_… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ block_9_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_10_expand[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_expand_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_10_expand_… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ block_10_expand_… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ block_10_depthwi… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_10_depthwi… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ block_10_depthwi… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_10_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ block_10_project… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ block_10_project… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,304\u001b[0m │ block_11_expand[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_expand_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_11_expand_… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,184\u001b[0m │ block_11_expand_… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,304\u001b[0m │ block_11_depthwi… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_11_depthwi… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ block_11_depthwi… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ block_11_project… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_11_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_10_project… │\n", "│ │ \u001b[38;5;34m96\u001b[0m) │ │ block_11_project… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ block_11_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,304\u001b[0m │ block_12_expand[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_expand_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_12_expand_… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,184\u001b[0m │ block_12_expand_… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,304\u001b[0m │ block_12_depthwi… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_12_depthwi… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ block_12_depthwi… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ block_12_project… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_12_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_11_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ │ \u001b[38;5;34m96\u001b[0m) │ │ block_12_project… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ block_12_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,304\u001b[0m │ block_13_expand[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_expand_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_13_expand_… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block_13_expand_… │\n", "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m576\u001b[0m) │ \u001b[38;5;34m5,184\u001b[0m │ block_13_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m576\u001b[0m) │ \u001b[38;5;34m2,304\u001b[0m │ block_13_depthwi… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m576\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_13_depthwi… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m92,160\u001b[0m │ block_13_depthwi… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_13_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m640\u001b[0m │ block_13_project… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m153,600\u001b[0m │ block_13_project… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m3,840\u001b[0m │ block_14_expand[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_expand_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_14_expand_… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m8,640\u001b[0m │ block_14_expand_… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m3,840\u001b[0m │ block_14_depthwi… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_14_depthwi… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m153,600\u001b[0m │ block_14_depthwi… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m640\u001b[0m │ block_14_project… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_14_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_13_project… │\n", "│ │ │ │ block_14_project… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m153,600\u001b[0m │ block_14_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m3,840\u001b[0m │ block_15_expand[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_expand_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_15_expand_… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m8,640\u001b[0m │ block_15_expand_… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m3,840\u001b[0m │ block_15_depthwi… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_15_depthwi… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m153,600\u001b[0m │ block_15_depthwi… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m640\u001b[0m │ block_15_project… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_15_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_14_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ │ │ │ block_15_project… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m153,600\u001b[0m │ block_15_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_expand_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m3,840\u001b[0m │ block_16_expand[\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_expand_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_16_expand_… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_depthwise │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m8,640\u001b[0m │ block_16_expand_… │\n", "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m3,840\u001b[0m │ block_16_depthwi… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_depthwise… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block_16_depthwi… │\n", "│ (\u001b[38;5;33mReLU\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_project │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m307,200\u001b[0m │ block_16_depthwi… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ block_16_project_BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m1,280\u001b[0m │ block_16_project… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ Conv_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m409,600\u001b[0m │ block_16_project… │\n", "│ │ \u001b[38;5;34m1280\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ Conv_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m5,120\u001b[0m │ Conv_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1280\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ out_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ Conv_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ │ \u001b[38;5;34m1280\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1280\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ out_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1280\u001b[0m) │ \u001b[38;5;34m5,120\u001b[0m │ global_average_p… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ dense_18 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m655,872\u001b[0m │ batch_normalizat… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ dropout_12 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ dense_19 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m131,328\u001b[0m │ dropout_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ dropout_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ dense_20 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m257\u001b[0m │ dropout_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n" ], "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
              "┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_6       │ (None, 224, 224,  │          0 │ -                 │\n",
              "│ (InputLayer)        │ 3)                │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ Conv1 (Conv2D)      │ (None, 112, 112,  │        864 │ input_layer_6[0]… │\n",
              "│                     │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ bn_Conv1            │ (None, 112, 112,  │        128 │ Conv1[0][0]       │\n",
              "│ (BatchNormalizatio… │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ Conv1_relu (ReLU)   │ (None, 112, 112,  │          0 │ bn_Conv1[0][0]    │\n",
              "│                     │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ expanded_conv_dept… │ (None, 112, 112,  │        288 │ Conv1_relu[0][0]  │\n",
              "│ (DepthwiseConv2D)   │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ expanded_conv_dept… │ (None, 112, 112,  │        128 │ expanded_conv_de… │\n",
              "│ (BatchNormalizatio… │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ expanded_conv_dept… │ (None, 112, 112,  │          0 │ expanded_conv_de… │\n",
              "│ (ReLU)              │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ expanded_conv_proj… │ (None, 112, 112,  │        512 │ expanded_conv_de… │\n",
              "│ (Conv2D)            │ 16)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ expanded_conv_proj… │ (None, 112, 112,  │         64 │ expanded_conv_pr… │\n",
              "│ (BatchNormalizatio… │ 16)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_expand      │ (None, 112, 112,  │      1,536 │ expanded_conv_pr… │\n",
              "│ (Conv2D)            │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_expand_BN   │ (None, 112, 112,  │        384 │ block_1_expand[0… │\n",
              "│ (BatchNormalizatio… │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_expand_relu │ (None, 112, 112,  │          0 │ block_1_expand_B… │\n",
              "│ (ReLU)              │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_pad         │ (None, 113, 113,  │          0 │ block_1_expand_r… │\n",
              "│ (ZeroPadding2D)     │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_depthwise   │ (None, 56, 56,    │        864 │ block_1_pad[0][0] │\n",
              "│ (DepthwiseConv2D)   │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_depthwise_… │ (None, 56, 56,    │        384 │ block_1_depthwis… │\n",
              "│ (BatchNormalizatio… │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_depthwise_… │ (None, 56, 56,    │          0 │ block_1_depthwis… │\n",
              "│ (ReLU)              │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_project     │ (None, 56, 56,    │      2,304 │ block_1_depthwis… │\n",
              "│ (Conv2D)            │ 24)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_1_project_BN  │ (None, 56, 56,    │         96 │ block_1_project[… │\n",
              "│ (BatchNormalizatio… │ 24)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_expand      │ (None, 56, 56,    │      3,456 │ block_1_project_… │\n",
              "│ (Conv2D)            │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_expand_BN   │ (None, 56, 56,    │        576 │ block_2_expand[0… │\n",
              "│ (BatchNormalizatio… │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_expand_relu │ (None, 56, 56,    │          0 │ block_2_expand_B… │\n",
              "│ (ReLU)              │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_depthwise   │ (None, 56, 56,    │      1,296 │ block_2_expand_r… │\n",
              "│ (DepthwiseConv2D)   │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_depthwise_… │ (None, 56, 56,    │        576 │ block_2_depthwis… │\n",
              "│ (BatchNormalizatio… │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_depthwise_… │ (None, 56, 56,    │          0 │ block_2_depthwis… │\n",
              "│ (ReLU)              │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_project     │ (None, 56, 56,    │      3,456 │ block_2_depthwis… │\n",
              "│ (Conv2D)            │ 24)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_project_BN  │ (None, 56, 56,    │         96 │ block_2_project[… │\n",
              "│ (BatchNormalizatio… │ 24)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_2_add (Add)   │ (None, 56, 56,    │          0 │ block_1_project_… │\n",
              "│                     │ 24)               │            │ block_2_project_… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_expand      │ (None, 56, 56,    │      3,456 │ block_2_add[0][0] │\n",
              "│ (Conv2D)            │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_expand_BN   │ (None, 56, 56,    │        576 │ block_3_expand[0… │\n",
              "│ (BatchNormalizatio… │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_expand_relu │ (None, 56, 56,    │          0 │ block_3_expand_B… │\n",
              "│ (ReLU)              │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_pad         │ (None, 57, 57,    │          0 │ block_3_expand_r… │\n",
              "│ (ZeroPadding2D)     │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_depthwise   │ (None, 28, 28,    │      1,296 │ block_3_pad[0][0] │\n",
              "│ (DepthwiseConv2D)   │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_depthwise_… │ (None, 28, 28,    │        576 │ block_3_depthwis… │\n",
              "│ (BatchNormalizatio… │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_depthwise_… │ (None, 28, 28,    │          0 │ block_3_depthwis… │\n",
              "│ (ReLU)              │ 144)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_project     │ (None, 28, 28,    │      4,608 │ block_3_depthwis… │\n",
              "│ (Conv2D)            │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_3_project_BN  │ (None, 28, 28,    │        128 │ block_3_project[… │\n",
              "│ (BatchNormalizatio… │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_expand      │ (None, 28, 28,    │      6,144 │ block_3_project_… │\n",
              "│ (Conv2D)            │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_expand_BN   │ (None, 28, 28,    │        768 │ block_4_expand[0… │\n",
              "│ (BatchNormalizatio… │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_expand_relu │ (None, 28, 28,    │          0 │ block_4_expand_B… │\n",
              "│ (ReLU)              │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_depthwise   │ (None, 28, 28,    │      1,728 │ block_4_expand_r… │\n",
              "│ (DepthwiseConv2D)   │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_depthwise_… │ (None, 28, 28,    │        768 │ block_4_depthwis… │\n",
              "│ (BatchNormalizatio… │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_depthwise_… │ (None, 28, 28,    │          0 │ block_4_depthwis… │\n",
              "│ (ReLU)              │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_project     │ (None, 28, 28,    │      6,144 │ block_4_depthwis… │\n",
              "│ (Conv2D)            │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_project_BN  │ (None, 28, 28,    │        128 │ block_4_project[… │\n",
              "│ (BatchNormalizatio… │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_4_add (Add)   │ (None, 28, 28,    │          0 │ block_3_project_… │\n",
              "│                     │ 32)               │            │ block_4_project_… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_expand      │ (None, 28, 28,    │      6,144 │ block_4_add[0][0] │\n",
              "│ (Conv2D)            │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_expand_BN   │ (None, 28, 28,    │        768 │ block_5_expand[0… │\n",
              "│ (BatchNormalizatio… │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_expand_relu │ (None, 28, 28,    │          0 │ block_5_expand_B… │\n",
              "│ (ReLU)              │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_depthwise   │ (None, 28, 28,    │      1,728 │ block_5_expand_r… │\n",
              "│ (DepthwiseConv2D)   │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_depthwise_… │ (None, 28, 28,    │        768 │ block_5_depthwis… │\n",
              "│ (BatchNormalizatio… │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_depthwise_… │ (None, 28, 28,    │          0 │ block_5_depthwis… │\n",
              "│ (ReLU)              │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_project     │ (None, 28, 28,    │      6,144 │ block_5_depthwis… │\n",
              "│ (Conv2D)            │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_project_BN  │ (None, 28, 28,    │        128 │ block_5_project[… │\n",
              "│ (BatchNormalizatio… │ 32)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_5_add (Add)   │ (None, 28, 28,    │          0 │ block_4_add[0][0… │\n",
              "│                     │ 32)               │            │ block_5_project_… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_expand      │ (None, 28, 28,    │      6,144 │ block_5_add[0][0] │\n",
              "│ (Conv2D)            │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_expand_BN   │ (None, 28, 28,    │        768 │ block_6_expand[0… │\n",
              "│ (BatchNormalizatio… │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_expand_relu │ (None, 28, 28,    │          0 │ block_6_expand_B… │\n",
              "│ (ReLU)              │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_pad         │ (None, 29, 29,    │          0 │ block_6_expand_r… │\n",
              "│ (ZeroPadding2D)     │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_depthwise   │ (None, 14, 14,    │      1,728 │ block_6_pad[0][0] │\n",
              "│ (DepthwiseConv2D)   │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_depthwise_… │ (None, 14, 14,    │        768 │ block_6_depthwis… │\n",
              "│ (BatchNormalizatio… │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_depthwise_… │ (None, 14, 14,    │          0 │ block_6_depthwis… │\n",
              "│ (ReLU)              │ 192)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_project     │ (None, 14, 14,    │     12,288 │ block_6_depthwis… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_6_project_BN  │ (None, 14, 14,    │        256 │ block_6_project[… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_expand      │ (None, 14, 14,    │     24,576 │ block_6_project_… │\n",
              "│ (Conv2D)            │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_expand_BN   │ (None, 14, 14,    │      1,536 │ block_7_expand[0… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_expand_relu │ (None, 14, 14,    │          0 │ block_7_expand_B… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_depthwise   │ (None, 14, 14,    │      3,456 │ block_7_expand_r… │\n",
              "│ (DepthwiseConv2D)   │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_depthwise_… │ (None, 14, 14,    │      1,536 │ block_7_depthwis… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_depthwise_… │ (None, 14, 14,    │          0 │ block_7_depthwis… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_project     │ (None, 14, 14,    │     24,576 │ block_7_depthwis… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_project_BN  │ (None, 14, 14,    │        256 │ block_7_project[… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_7_add (Add)   │ (None, 14, 14,    │          0 │ block_6_project_… │\n",
              "│                     │ 64)               │            │ block_7_project_… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_expand      │ (None, 14, 14,    │     24,576 │ block_7_add[0][0] │\n",
              "│ (Conv2D)            │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_expand_BN   │ (None, 14, 14,    │      1,536 │ block_8_expand[0… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_expand_relu │ (None, 14, 14,    │          0 │ block_8_expand_B… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_depthwise   │ (None, 14, 14,    │      3,456 │ block_8_expand_r… │\n",
              "│ (DepthwiseConv2D)   │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_depthwise_… │ (None, 14, 14,    │      1,536 │ block_8_depthwis… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_depthwise_… │ (None, 14, 14,    │          0 │ block_8_depthwis… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_project     │ (None, 14, 14,    │     24,576 │ block_8_depthwis… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_project_BN  │ (None, 14, 14,    │        256 │ block_8_project[… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_8_add (Add)   │ (None, 14, 14,    │          0 │ block_7_add[0][0… │\n",
              "│                     │ 64)               │            │ block_8_project_… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_expand      │ (None, 14, 14,    │     24,576 │ block_8_add[0][0] │\n",
              "│ (Conv2D)            │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_expand_BN   │ (None, 14, 14,    │      1,536 │ block_9_expand[0… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_expand_relu │ (None, 14, 14,    │          0 │ block_9_expand_B… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_depthwise   │ (None, 14, 14,    │      3,456 │ block_9_expand_r… │\n",
              "│ (DepthwiseConv2D)   │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_depthwise_… │ (None, 14, 14,    │      1,536 │ block_9_depthwis… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_depthwise_… │ (None, 14, 14,    │          0 │ block_9_depthwis… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_project     │ (None, 14, 14,    │     24,576 │ block_9_depthwis… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_project_BN  │ (None, 14, 14,    │        256 │ block_9_project[… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_9_add (Add)   │ (None, 14, 14,    │          0 │ block_8_add[0][0… │\n",
              "│                     │ 64)               │            │ block_9_project_… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_expand     │ (None, 14, 14,    │     24,576 │ block_9_add[0][0] │\n",
              "│ (Conv2D)            │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_expand_BN  │ (None, 14, 14,    │      1,536 │ block_10_expand[… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_expand_re… │ (None, 14, 14,    │          0 │ block_10_expand_… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_depthwise  │ (None, 14, 14,    │      3,456 │ block_10_expand_… │\n",
              "│ (DepthwiseConv2D)   │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_depthwise… │ (None, 14, 14,    │      1,536 │ block_10_depthwi… │\n",
              "│ (BatchNormalizatio… │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_depthwise… │ (None, 14, 14,    │          0 │ block_10_depthwi… │\n",
              "│ (ReLU)              │ 384)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_project    │ (None, 14, 14,    │     36,864 │ block_10_depthwi… │\n",
              "│ (Conv2D)            │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_10_project_BN │ (None, 14, 14,    │        384 │ block_10_project… │\n",
              "│ (BatchNormalizatio… │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_expand     │ (None, 14, 14,    │     55,296 │ block_10_project… │\n",
              "│ (Conv2D)            │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_expand_BN  │ (None, 14, 14,    │      2,304 │ block_11_expand[… │\n",
              "│ (BatchNormalizatio… │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_expand_re… │ (None, 14, 14,    │          0 │ block_11_expand_… │\n",
              "│ (ReLU)              │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_depthwise  │ (None, 14, 14,    │      5,184 │ block_11_expand_… │\n",
              "│ (DepthwiseConv2D)   │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_depthwise… │ (None, 14, 14,    │      2,304 │ block_11_depthwi… │\n",
              "│ (BatchNormalizatio… │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_depthwise… │ (None, 14, 14,    │          0 │ block_11_depthwi… │\n",
              "│ (ReLU)              │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_project    │ (None, 14, 14,    │     55,296 │ block_11_depthwi… │\n",
              "│ (Conv2D)            │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_project_BN │ (None, 14, 14,    │        384 │ block_11_project… │\n",
              "│ (BatchNormalizatio… │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_11_add (Add)  │ (None, 14, 14,    │          0 │ block_10_project… │\n",
              "│                     │ 96)               │            │ block_11_project… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_expand     │ (None, 14, 14,    │     55,296 │ block_11_add[0][… │\n",
              "│ (Conv2D)            │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_expand_BN  │ (None, 14, 14,    │      2,304 │ block_12_expand[… │\n",
              "│ (BatchNormalizatio… │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_expand_re… │ (None, 14, 14,    │          0 │ block_12_expand_… │\n",
              "│ (ReLU)              │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_depthwise  │ (None, 14, 14,    │      5,184 │ block_12_expand_… │\n",
              "│ (DepthwiseConv2D)   │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_depthwise… │ (None, 14, 14,    │      2,304 │ block_12_depthwi… │\n",
              "│ (BatchNormalizatio… │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_depthwise… │ (None, 14, 14,    │          0 │ block_12_depthwi… │\n",
              "│ (ReLU)              │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_project    │ (None, 14, 14,    │     55,296 │ block_12_depthwi… │\n",
              "│ (Conv2D)            │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_project_BN │ (None, 14, 14,    │        384 │ block_12_project… │\n",
              "│ (BatchNormalizatio… │ 96)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_12_add (Add)  │ (None, 14, 14,    │          0 │ block_11_add[0][… │\n",
              "│                     │ 96)               │            │ block_12_project… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_expand     │ (None, 14, 14,    │     55,296 │ block_12_add[0][… │\n",
              "│ (Conv2D)            │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_expand_BN  │ (None, 14, 14,    │      2,304 │ block_13_expand[… │\n",
              "│ (BatchNormalizatio… │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_expand_re… │ (None, 14, 14,    │          0 │ block_13_expand_… │\n",
              "│ (ReLU)              │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_pad        │ (None, 15, 15,    │          0 │ block_13_expand_… │\n",
              "│ (ZeroPadding2D)     │ 576)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_depthwise  │ (None, 7, 7, 576) │      5,184 │ block_13_pad[0][… │\n",
              "│ (DepthwiseConv2D)   │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_depthwise… │ (None, 7, 7, 576) │      2,304 │ block_13_depthwi… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_depthwise… │ (None, 7, 7, 576) │          0 │ block_13_depthwi… │\n",
              "│ (ReLU)              │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_project    │ (None, 7, 7, 160) │     92,160 │ block_13_depthwi… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_13_project_BN │ (None, 7, 7, 160) │        640 │ block_13_project… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_expand     │ (None, 7, 7, 960) │    153,600 │ block_13_project… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_expand_BN  │ (None, 7, 7, 960) │      3,840 │ block_14_expand[… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_expand_re… │ (None, 7, 7, 960) │          0 │ block_14_expand_… │\n",
              "│ (ReLU)              │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_depthwise  │ (None, 7, 7, 960) │      8,640 │ block_14_expand_… │\n",
              "│ (DepthwiseConv2D)   │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_depthwise… │ (None, 7, 7, 960) │      3,840 │ block_14_depthwi… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_depthwise… │ (None, 7, 7, 960) │          0 │ block_14_depthwi… │\n",
              "│ (ReLU)              │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_project    │ (None, 7, 7, 160) │    153,600 │ block_14_depthwi… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_project_BN │ (None, 7, 7, 160) │        640 │ block_14_project… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_14_add (Add)  │ (None, 7, 7, 160) │          0 │ block_13_project… │\n",
              "│                     │                   │            │ block_14_project… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_expand     │ (None, 7, 7, 960) │    153,600 │ block_14_add[0][… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_expand_BN  │ (None, 7, 7, 960) │      3,840 │ block_15_expand[… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_expand_re… │ (None, 7, 7, 960) │          0 │ block_15_expand_… │\n",
              "│ (ReLU)              │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_depthwise  │ (None, 7, 7, 960) │      8,640 │ block_15_expand_… │\n",
              "│ (DepthwiseConv2D)   │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_depthwise… │ (None, 7, 7, 960) │      3,840 │ block_15_depthwi… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_depthwise… │ (None, 7, 7, 960) │          0 │ block_15_depthwi… │\n",
              "│ (ReLU)              │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_project    │ (None, 7, 7, 160) │    153,600 │ block_15_depthwi… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_project_BN │ (None, 7, 7, 160) │        640 │ block_15_project… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_15_add (Add)  │ (None, 7, 7, 160) │          0 │ block_14_add[0][… │\n",
              "│                     │                   │            │ block_15_project… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_expand     │ (None, 7, 7, 960) │    153,600 │ block_15_add[0][… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_expand_BN  │ (None, 7, 7, 960) │      3,840 │ block_16_expand[… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_expand_re… │ (None, 7, 7, 960) │          0 │ block_16_expand_… │\n",
              "│ (ReLU)              │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_depthwise  │ (None, 7, 7, 960) │      8,640 │ block_16_expand_… │\n",
              "│ (DepthwiseConv2D)   │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_depthwise… │ (None, 7, 7, 960) │      3,840 │ block_16_depthwi… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_depthwise… │ (None, 7, 7, 960) │          0 │ block_16_depthwi… │\n",
              "│ (ReLU)              │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_project    │ (None, 7, 7, 320) │    307,200 │ block_16_depthwi… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ block_16_project_BN │ (None, 7, 7, 320) │      1,280 │ block_16_project… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ Conv_1 (Conv2D)     │ (None, 7, 7,      │    409,600 │ block_16_project… │\n",
              "│                     │ 1280)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ Conv_1_bn           │ (None, 7, 7,      │      5,120 │ Conv_1[0][0]      │\n",
              "│ (BatchNormalizatio… │ 1280)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ out_relu (ReLU)     │ (None, 7, 7,      │          0 │ Conv_1_bn[0][0]   │\n",
              "│                     │ 1280)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ global_average_poo… │ (None, 1280)      │          0 │ out_relu[0][0]    │\n",
              "│ (GlobalAveragePool… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ batch_normalizatio… │ (None, 1280)      │      5,120 │ global_average_p… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_18 (Dense)    │ (None, 512)       │    655,872 │ batch_normalizat… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dropout_12          │ (None, 512)       │          0 │ dense_18[0][0]    │\n",
              "│ (Dropout)           │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_19 (Dense)    │ (None, 256)       │    131,328 │ dropout_12[0][0]  │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dropout_13          │ (None, 256)       │          0 │ dense_19[0][0]    │\n",
              "│ (Dropout)           │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_20 (Dense)    │ (None, 1)         │        257 │ dropout_13[0][0]  │\n",
              "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
              "
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 Non-trainable params: 2,260,544 (8.62 MB)\n",
              "
\n" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "mobilenet_model.compile(\n", "\n", " optimizer=tf.keras.optimizers.Adam(\n", " learning_rate=0.0001\n", " ),\n", "\n", " loss=\"binary_crossentropy\",\n", "\n", " metrics=[\"accuracy\"]\n", "\n", ")" ], "metadata": { "id": "s_64k6OuVLxj" }, "execution_count": 146, "outputs": [] }, { "cell_type": "code", "source": [ "mobilenet_callbacks = [\n", "\n", " tf.keras.callbacks.EarlyStopping(\n", "\n", " monitor=\"val_loss\",\n", "\n", " patience=5,\n", "\n", " restore_best_weights=True\n", "\n", " ),\n", "\n", " tf.keras.callbacks.ReduceLROnPlateau(\n", "\n", " monitor=\"val_loss\",\n", "\n", " factor=0.2,\n", "\n", " patience=3,\n", "\n", " verbose=1\n", "\n", " ),\n", "\n", " tf.keras.callbacks.ModelCheckpoint(\n", "\n", " \"best_mobilenet.keras\",\n", "\n", " monitor=\"val_accuracy\",\n", "\n", " save_best_only=True\n", "\n", " )\n", "\n", "]" ], "metadata": { "id": "xdEvvejiVLu8" }, "execution_count": 147, "outputs": [] }, { "cell_type": "code", "source": [ "mobilenet_history = mobilenet_model.fit(\n", "\n", " train_dataset,\n", "\n", " validation_data=test_dataset,\n", "\n", " epochs=5,\n", "\n", " callbacks=mobilenet_callbacks\n", "\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BmM66FRSVLsi", "outputId": "5d939e4f-d913-4cf9-fda4-192a2e61e1b7" }, "execution_count": 148, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 147ms/step - accuracy: 0.8080 - loss: 0.4295 - val_accuracy: 0.4910 - val_loss: 1.0210 - learning_rate: 1.0000e-04\n", "Epoch 2/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 111ms/step - accuracy: 0.8583 - loss: 0.3307 - val_accuracy: 0.5432 - val_loss: 0.7792 - learning_rate: 1.0000e-04\n", "Epoch 3/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 111ms/step - accuracy: 0.8701 - loss: 0.3095 - val_accuracy: 0.5722 - val_loss: 0.7173 - learning_rate: 1.0000e-04\n", "Epoch 4/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 111ms/step - accuracy: 0.8801 - loss: 0.2925 - val_accuracy: 0.5460 - val_loss: 0.7531 - learning_rate: 1.0000e-04\n", "Epoch 5/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 110ms/step - accuracy: 0.8816 - loss: 0.2854 - val_accuracy: 0.5631 - val_loss: 0.7158 - learning_rate: 1.0000e-04\n" ] } ] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(8,5))\n", "plt.plot(mobilenet_history.history[\"accuracy\"],label=\"Training Accuracy\")\n", "plt.plot(mobilenet_history.history[\"val_accuracy\"],label=\"Validation Accuracy\")\n", "plt.title(\"MobileNetV2 Accuracy\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Accuracy\")\n", "plt.legend()\n", "plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "nL4YmLBmVLp7", "outputId": "9a3c1164-7fc3-4440-d203-31167b61e199" }, "execution_count": 149, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(8,5))\n", "plt.plot(mobilenet_history.history[\"loss\"],label=\"Training Loss\")\n", "plt.plot(mobilenet_history.history[\"val_loss\"],label=\"Validation Loss\")\n", "plt.title(\"MobileNetV2 Loss\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "plt.legend()\n", "plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "ICC6oKmrVLca", "outputId": "fdc127a6-6165-42ad-c617-20a0e994bcb6" }, "execution_count": 150, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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YREREcPDgQa1HX0B51MVkMtGoUSNCQ0Mv6jwKsyIiIlKIw+Fg7969WCwWGjRoQGBgYJUIdw6Hg7NnzxIaGnrBxfqrkrKui2EYHDt2jEOHDtGiRYuLGqFVmBUREZFCsrOzcTgcREVFERIS4uvuVBiHw0F2djZBQUEKswWUR13q1q3Lvn37sNvtFxVm9ackIiIiXinQSXkpq5F+fUJFRERExG8pzIqIiIiI31KYFRERESlCdHQ006dPL/b+K1euxGQycfr06XLrk5yjMCsiIiKXBJPJVOQ2ZcqUUp33559/ZuzYscXe/+qrryYxMZHq1auX6nrFpdDspNUMKkBATpqvuyAiInLJS0xMdD2eN28ezzzzDDt37nS1FVzP1DAMcnNzCQi4cBSqW7duifoRGBhIREREiY6R0tPIbDkz7VxE722PY9r9ra+7IiIiUmqGYZCeneOTzTCMYvUxIiLCtVWvXh2TyeR6vmPHDsLCwvjmm2+IiYnBZrPx448/8scff3DTTTdRv359QkND6datGytXrnQ77/nTDEwmE++88w4333wzISEhtGjRgi+//NL1+vkjprNnz6ZGjRosWbKENm3aEBoaSt++fd3Cd05ODg899BA1atSgdu3aPPHEE4waNYpBgwaV9o+MU6dOMXLkSGrWrElISAj9+vVj165drtf379/PwIEDqVmzJtWqVaNdu3YsWrTIdezw4cOpW7cuwcHBtGrVio8++qjUfSlPGpktT4aB+dcPCMhNw5g3FHpPhasfgiqw6LSIiFxaMuy5tH1miU+uvW1aHCGBZRNZnnzySV566SWaNm1KzZo1OXjwIP379+cf//gHNpuN999/n6FDh7J9+3aio6O9nmfq1Kn861//4sUXX+TVV19l+PDh7N+/n1q1anncPz09nZdeeok5c+ZgNpu58847efzxx10B8YUXXuCjjz7ivffeo02bNvznP/9h4cKF9OzZs9Tv9a677mLXrl18+eWXhIeH88QTT9C/f3+2bduG1Wpl3LhxZGdn8/3331OtWjW2bdvmGr1++umn2bZtG9988w116tTh999/58SJE6XuS3lSmC1PJhO5t33AgZlDiT6xCpY9A0lb4Mb/gjXY170TERGpcqZNm0bv3r1dz2vVqkXHjh3dXv/ss8/4v//7Px588EGv57nrrrsYOnQoAM899xz//e9/Wb9+PX379vW4v91uZ8aMGTRr1gyA8ePHM23aNNfrr776KhMnTuTmm28G4LXXXnONkpZGfohdvXo1V199NQAfffQRUVFRLFy4kNtuu40DBw4wePBg2rdvD0DTpk1dxx84cIDOnTvTpUsXABo3bkxqamqp+1OeFGbLmyWQzVF3E9WlP5alf4OET+DELrhjLoQ38HXvREREiiXYamHbtDifXbus5IezfGfPnmXKlCl8/fXXJCYmkpOTQ0ZGBgcOHCjyPB06dHA9rlatGuHh4Rw9etTr/iEhIa4gCxAZGenaPyUlheTkZLp27ep63WKxEBMTg8PhKNH7y7d9+3YCAgLo1q2bq6127dq0atWK7du3A/DQQw9x//33s3TpUnr16sXgwYNd7+v+++9n8ODBbNy4kT59+nDjjTdy+eWXl6ov5U1zZiuCyYSjyz0wYgEE14Qjv8LbPeDgz77umYiISLGYTCZCAgN8spXVnaLAGTwLevzxx1mwYAHPPfccP/zwAxs3bqRt27ZkZ2cXeR6r1VqoPkUFT0/7F3cucHm599572bNnDyNGjCAhIYEuXbrw6quvAtCvXz/279/Po48+ypEjR+jduzdPP/20T/vrjcJsRWraHcasgHpt4WwyzO4Pv1bOydQiIiJVwerVq7nrrru4+eabad++PRERERcclS1r1atXp379+vz887lBrtzcXDZu3Fjqc7Zp04acnBzWrVvnajtx4gQ7d+6kbdu2rraoqCjuu+8+Pv/8cx577DFmzpzpeq1u3bqMGjWKDz/8kJdffpn333+/1P0pT5pmUNFqXQb3LIMFf4EdX8EXD0DyFuj9d7Doj0NERKQitWjRgs8//5yBAwdiMpl46qmnfDJi+uCDD/L888/TvHlzWrduzauvvsqpU6eKNSqdkJBAWFiY67nJZKJjx47cdNNNjBkzhrfeeouwsDCefPJJGjZsyE033QTAI488Qr9+/WjZsiWnTp1ixYoVtGnTBoBnnnmGmJgY2rVrR1ZWFl9//TUtW7Ysnzd/kXw6Mvv9998zcOBAGjRogMlkYuHChRc8ZuXKlVxxxRXYbDaaN2/O7Nmzy72fZc4WCrfPge5POp//9AZ8dCukn/Rtv0RERKqYl19+mZo1a3L11VczcOBA4uLi3ObDVpQnnniCoUOHMnLkSGJjYwkNDSUuLo6goKALHnvdddfRuXNn1xYTEwPAe++9R0xMDDfccAOxsbEYhsGiRYtcUx5yc3MZN24cbdq0oW/fvrRs2ZI33ngDcK6VO3HiRDp06MB1112HxWLh3XffLb8CXAzDhxYtWmRMmjTJ+Pzzzw3AWLBgQZH779mzxwgJCTHi4+ONbdu2Ga+++qphsViMxYsXF/uaKSkpBmCkpKRcZO+LJzs721i4cKGRnZ3teYetCw3j2QjDmBxuGNM7Gkby9grpV2VwwdpUUaqLd6qNZ6qLd6qNZ8WpS0ZGhrFt2zYjIyOjAnvme7m5ucapU6eM3Nxcn/ejZcuWxlNPPeXTfuQrj7oU9RkrSV7z6e+1+/XrR79+/Yq9/4wZM7jsssv497//DTjng/z444+88sorxMX55huWF63tTVCrKfxvGJzaC+/0gsEzoVXx6yIiIiL+bf/+/SxdupTu3buTlZXFa6+9xt69exk2bJivu1bp+dUkzbVr19KrVy+3tri4OB555BGvx2RlZZGVleV6nr9Gmt1ux263l0s/C8q/RpHXqt0aRi/F8vndmA+swfjfUBw9/obj6kcu6RssFKs2VZDq4p1q45nq4p1q41lx6mK32zEMA4fDUerlofyRkTdfNv+9V6TZs2fz+OOPYxgGl19+OUuXLqVVq1aVov7lUReHw4FhGNjtdiwW9+XXSvJ31mQYPl4XIo/JZGLBggVF3ratZcuWjB49mokTJ7raFi1axIABA0hPTyc4uPCNCKZMmcLUqVMLtc+dO5eQkJAy6XtZMRk5tD/0EZcdXw7AoRrd2NTkXnLNNh/3TEREqpqAgAAiIiKIiooiMDDQ192RS1B2djYHDx4kKSmJnJwct9fS09MZNmwYKSkphIeHF3kevxqZLY2JEycSHx/vep6amkpUVBR9+vS5YHHKgt1uZ9myZfTu3bvQGnOe3UjuxtmYlzxJo9PraGhLJ+e2OVC9Ubn3taKVvDZVg+rinWrjmerinWrjWXHqkpmZycGDBwkNDS3Wl5AuFYZhcObMGcLCwsp0fVt/Vx51yczMJDg4mOuuu67QZ6wkdxvzqzAbERFBcnKyW1tycjLh4eEeR2UBbDYbNlvhkU2r1VqhP9hKdL1uY6B+W/hkBKbkBKyzesGQD6FJbPl20kcq+s/CX6gu3qk2nqku3qk2nhVVl9zcXEwmE2azGbO56ixLn/8r9Pz3Lk7lURez2YzJZPL4OSzJ31e/+lOKjY1l+fLlbm3Lli0jNvYSDHnR18DYlVC/PaQfh/cHwobZvu6ViIiISKXi0zB79uxZNm3axKZNmwDYu3cvmzZtct15Y+LEiYwcOdK1/3333ceePXuYMGECO3bs4I033uCTTz7h0Ucf9UX3y1+NxnDPEmg7CBx2+L+H4evHIVdfZBAREREBH4fZX375xbXAL0B8fDydO3fmmWeeASAxMdHtlnKXXXYZX3/9NcuWLaNjx478+9//5p133vHfZbmKI7Aa3DYb/vyU8/nPM2HOzZB2wqfdEhEREakMfDpntkePHkXeMs7T3b169OjBr7/+Wo69qoRMJrjur1CvLXw+Fvb9ADN7wNCPoX47X/dORERExGf8as5sldd6ANz7LdSMhtMH4J3esP3/fN0rERGRS8oNN9zgNoUxOjqa6dOnF3mMyWRi4cKFF33tsjpPVaIw62/qtYExK+Cy7mBPg3l3wsp/QiVYUFlERMSXBg4cSN++fT2+9sMPP2Aymfjtt99KfN6ff/6ZsWPHXmz33EyZMoVOnToVak9MTCzR3VFLY/bs2dSoUaNcr1GRFGb9UUgtuPNz6Haf8/nK52H+SMg669t+iYiI+NA999zDsmXLOHToUKHX3nvvPbp06UKHDh1KfN66detW2I2WIiIiPC4pKt4pzPorSwD0ewFufA3MVud0g1lxcGqfr3smIiKXIsOA7DTfbMW8WekNN9xA3bp1C33n5uzZs8yfP5977rmHEydOMHToUBo2bEhISAjt27fnf//7X5HnPX+awa5du1wL/bdt25Zly5YVOuaJJ56gZcuWhISE0LRpU55++mnXLVpnz57N1KlT2bx5MyaTCZPJ5Orz+dMMEhIS+POf/0xwcDC1a9dm7NixnD17bvDqrrvuYtCgQbz00ktERkZSu3Ztxo0bd1G3cD5w4AA33XQToaGhhIeHc/vtt7ut879582Z69uxJWFgY4eHhxMTE8MsvvwCwf/9+Bg4cSM2aNalWrRrt2rVj0aJFpe5LcfjVTRPEgytGQJ2WzukGyVvg7Z5w+wdw2Z983TMREbmU2NPhuQa+ufbfjjhX97mAgIAARo4cyezZs5k0aZLrTlXz588nNzeXoUOHcvbsWWJiYnjiiScIDw/n66+/ZsSIETRr1oyuXbte8BoOh4NbbrmF+vXrs27dOlJSUnjkkUcK7RcWFsbs2bNp0KABCQkJjBkzhrCwMCZMmMCQIUPYsmULixcv5ttvvwWgevXqhc6RlpZGXFwcsbGx/Pzzzxw9epR7772X8ePHuwX2FStWEBkZyYoVK9i9ezdDhgyhU6dOjBkz5oLvx9P7yw+yq1atIicnh3HjxjF06FBXyB4+fDidO3fmzTffxGKxsGnTJtdNDsaNG0d2djbff/891apVY9u2bYSGhpa4HyWhMHspaNwNxq6Aj4dD4iaYMwj6/hOuvNe5EoKIiEgVcffdd/Piiy+yatUqevToATinGAwePJjq1atTvXp1Hn/8cdf+Dz74IEuWLOGTTz4pVpj99ttv2bFjB0uWLKFBA2e4f+655wrNc33qqadcj6Ojo3n88cf5+OOPmTBhAsHBwYSGhhIQEEBERITXa82dO5fMzEw++OADqlVzhvnXXnuNgQMH8sILL1C/fn0AatasyWuvvYbFYqF169YMGDCA5cuXlyrMLl++nISEBPbu3UtUVBQAH3zwAe3atWPjxo306NGDAwcO8Ne//pXWrVsD0KJFC9fxBw4cYPDgwbRv3x6Apk2blrgPJaUwe6mo3gjuXgxfPggJ82HR486R2n4vQkCgr3snIiL+zhriHCH11bWLqXXr1lx99dXMmjWLHj16sHv3bn744QemTZsGOG/T+9xzz/HJJ59w+PBhsrOzycrKKvac2O3btxMVFeUKsoDHO5HOmzeP//73v/zxxx+cPXuWnJwcwsPDi/0+8q/VsWNHV5AFuOaaa3A4HOzcudMVZtu1a4fFYnHtExkZSUJCQomuVfCaUVFRriAL0LZtW2rUqMHvv/9Ojx49iI+P595772XOnDn06tWL2267jWbNmgHw0EMPcf/997N06VJ69erF4MGDSzVPuSQ0Z/ZSYg2GW2ZCr6mAyXn72w9ugrPHfN0zERHxdyaT81f9vthK+FvGe+65h88++4wzZ87w3nvv0axZM7p37w7Aiy++yH/+8x+eeOIJVqxYwaZNm4iLiyM7O7vMSrV27VqGDx9O//79+eqrr/j111+ZNGlSmV6joPxf8eczmUw4ynGVoylTprB161YGDBjAd999R9u2bVmwYAEA9957L3v27GHEiBEkJCTQpUsXXn311XLrCyjMXnpMJrj2ERg2D2zhcGANzOwJiSVfikRERMQf3X777ZjNZubOncsHH3zA3Xff7Zo/u3r1am666SbuvPNOOnbsSNOmTfn999+Lfe42bdpw8OBBEhMTXW0//fST2z5r1qyhSZMmTJo0iS5dutCiRQv279/vtk9gYCC5ubkXvNbmzZtJS0tzta1evRqz2UyrVq2K3eeSyH9/Bw8edLVt27aN06dPu12zZcuWPProoyxdupRbbrmF9957z/VaVFQU9913H59//jmPPfYYM2fOLJe+5lOYvVS1jIN7l0OtZpByEN7tA1s+93WvREREyl1oaChDhgxh4sSJJCYmctddd7lea9GiBcuWLWPNmjVs376dv/zlL27f1L+QXr160bJlS0aNGsXmzZv54YcfmDRpkts+LVq04MCBA3z88cf88ccf/Pe//3WNXOaLjo5m7969bNq0iePHj5OVlVXoWsOHDycoKIhRo0axZcsWVqxYwYMPPsiIESNcUwxKKzc3l02bNrlt27dvp1evXrRv357hw4ezceNG1q9fz8iRI+nevTudO3cmIyOD8ePHs3LlSvbv38/q1av5+eefadOmDQCPPPIIS5YsYe/evWzcuJEVK1a4XisvCrOXsrotYcxyaHY95GTAp6Nh+d91gwUREbnk3XPPPZw6dYq4uDi3+a1PPfUUV1xxBXFxcfTo0YOIiAgGDRpU7POazWYWLFhARkYGXbt25d577+Uf//iH2z433ngjjz76KOPHj6dTp06sWbOGp59+2m2fwYMH07dvX3r27EndunU9Lg8WEhLCkiVLOHnyJFdeeSW33nor119/Pa+99lrJiuHB2bNn6dy5s9s2cOBATCYTX3zxBTVr1uS6666jV69eNG3a1NU/i8XCiRMnGDlyJC1btuT222+nX79+TJ06FXCG5HHjxtGmTRv69u1Ly5YteeONNy66v0UxGUYxF2+7RKSmplK9enVSUlJKPBG7NOx2O4sWLaJ///6F5rRUGEcuLHsG1uZ9+Fv1h5vfgqDyf/9FqRS1qYRUF+9UG89UF+9UG8+KU5fMzEz27t3LZZddRlBQUAX30HccDgepqamEh4djNmvML1951KWoz1hJ8pr+lKoCswXi/gGDZoDFBjsXwbu94eQeX/dMRERE5KIozFYlnYbC6G8gNAKO7XDeYOGPFb7ulYiIiEipKcxWNY1iYOxKaBgDmafhw8Hw05vFvlWgiIiISGWiMFsVhUfCXYug41AwcmHxk/DFeMgp/E1KERERkcpMYbaqsgbBoDch7jkwmWHThzD7BjhT/OVJRETk0lfFvicuFaisPlsKs1WZyQSx42D4fAiqDofWw9s94PBGX/dMRER8LH+Vg/T0dB/3RC5V+XdEK3gr3tIIKIvOiJ9r3gvGrID/3QHHf4f3+sGNr0GH23zdMxER8RGLxUKNGjU4evQo4Fzz1FTC28r6I4fDQXZ2NpmZmVqaq4CyrovD4eDYsWOEhIQQEHBxcVRhVpxqN4N7v4XPxsCuJfD5vZCcANdPdi7tJSIiVU5ERASAK9BWBYZhkJGRQXBwcJUI78VVHnUxm800btz4os+nMCvnBFWHof+D7/4OP74Cq/8DR7fD4Hecr4mISJViMpmIjIykXr162O12X3enQtjtdr7//nuuu+463WijgPKoS2BgYJmM8irMijuzBXpNgfqXwxfjYNdSmHk9DP0Y6jT3de9ERMQHLBbLRc9r9BcWi4WcnByCgoIUZguozHXRZBDxrP2tcPdiCG8IJ3bBzD/Drm993SsRERERNwqz4l2Dzs4bLER1g6wUmHsbrP6vbrAgIiIilYbCrBQttB6M+j/oPAIMByx7Ghb8BeyZvu6ZiIiIiMKsFEOADW58Ffr9C0wW+G2ec/mu1CO+7pmIiIhUcQqzUjwmE3T7C4z4HIJrwpGN8HZPOPSLr3smIiIiVZjCrJRM0x7OGyzUbQNnk5wjtJvm+rpXIiIiUkUpzErJ1boM7l0GrQZAbjYsvB+WTILcHF/3TERERKoYhVkpHVsYDPkQuj/hfL72NedqBxmnfNsvERERqVIUZqX0zGbo+Te47X2whsAf3znXoz2209c9ExERkSpCYVYuXrtBcPcSqN4YTu5x3jFs52Jf90pERESqAIVZKRuRHWDsCmhyDWSfgf/dAT/8WzdYEBERkXKlMCtlp1odGLEQutwNGLB8Gnx2D2Sn+7pnIiIicolSmJWyFRAIN7wCA14GcwBs+Qze6wsph3zdMxEREbkEKcxK+bjyHhj5BYTUhsTN8HYPOPCTr3slIiIilxiFWSk/0dc6b7BQ/3JIOwazb4AN7/u6VyIiInIJ8XmYff3114mOjiYoKIhu3bqxfv16r/va7XamTZtGs2bNCAoKomPHjixerG/NV2o1m8A9S6HtTeCww/89BIsmQK7d1z0TERGRS4BPw+y8efOIj49n8uTJbNy4kY4dOxIXF8fRo0c97v/UU0/x1ltv8eqrr7Jt2zbuu+8+br75Zn799dcK7rmUSGA151q0PSc5n69/Cz68BdJP+rZfIiIi4vd8GmZffvllxowZw+jRo2nbti0zZswgJCSEWbNmedx/zpw5/O1vf6N///40bdqU+++/n/79+/Pvf/+7gnsuJWYyQfcJMOQjCAyFvd8T8F5vwjIO+rpnIiIi4scCfHXh7OxsNmzYwMSJE11tZrOZXr16sXbtWo/HZGVlERQU5NYWHBzMjz/+6PU6WVlZZGVluZ6npqYCzikLdnv5/6o7/xoVcS2/0DwORn1DwPw7MZ3ez3Wp08jdFom97Y2+7lmloc+Md6qNZ6qLd6qNZ6qLd6qNZxVdl5Jcx2QYvlnV/siRIzRs2JA1a9YQGxvrap8wYQKrVq1i3bp1hY4ZNmwYmzdvZuHChTRr1ozly5dz0003kZub6xZYC5oyZQpTp04t1D537lxCQkLK7g1JiVhzznDl3tepe3YbANsjbuH3iBvB5PNp3CIiIuJj6enpDBs2jJSUFMLDw4vc12cjs6Xxn//8hzFjxtC6dWtMJhPNmjVj9OjRXqclAEycOJH4+HjX89TUVKKioujTp88Fi1MW7HY7y5Yto3fv3lit1nK/nj+xZw5kzwf30PTYMtokfU6rGnZyB77mnGNbhekz451q45nq4p1q45nq4p1q41lF1yX/N+nF4bMwW6dOHSwWC8nJyW7tycnJREREeDymbt26LFy4kMzMTE6cOEGDBg148sknadq0qdfr2Gw2bDZboXar1VqhH9KKvp5/CCGh0Qgadx1AwDd/xbzj/zCf2gt3zHWuglDF6TPjnWrjmerinWrjmerinWrjWUXVpSTX8NnvdAMDA4mJiWH58uWuNofDwfLly92mHXgSFBREw4YNycnJ4bPPPuOmm24q7+5KOTI63Ql3fQXV6kLyFpjZE/Z5nwctIiIiks+nExTj4+OZOXMm77//Ptu3b+f+++8nLS2N0aNHAzBy5Ei3L4itW7eOzz//nD179vDDDz/Qt29fHA4HEyZM8NVbkLLS+CoYuxIiO0L6CfjgJvj5XV/3SkRERCo5n86ZHTJkCMeOHeOZZ54hKSmJTp06sXjxYurXrw/AgQMHMJvP5e3MzEyeeuop9uzZQ2hoKP3792fOnDnUqFHDR+9AylT1RjB6MXw5HrZ8Bl/HO0dq+74AAYG+7p2IiIhUQj7/Atj48eMZP368x9dWrlzp9rx79+5s27atAnolPhMYAoPfdd4Cd/k0+GUWHNsJt38A1er4unciIiJSyWgdJKl8TCb4UzwM/RgCw2D/ani7ByT+5uueiYiISCWjMCuVV6u+MGY51GoKKQdhVhxsXeDrXomIiEglojArlVvdVjDmO2j2Z7Cnw/y74Lt/gMPh656JiIhIJaAwK5VfcE0YNh9i8+ZWf/8vmHcnZJ3xbb9ERETE5xRmxT9YAiDuHzBoBlgCYefX8E5vOLnH1z0TERERH1KYFf/SaSiM/gZCI+DYdpj5Z9iz0te9EhERER9RmBX/06iL8wYLDWMg4xTMuQXWvQWG4eueiYiISAVTmBX/FB4Jdy2CDneAkQvfTIAvH4ScLF/3TERERCqQwqz4L2sQ3DwD+jwLJjP8OgfeHwhnj/q6ZyIiIlJBFGbFv5lMcPWDMHw+2KrDwXXOGywc+dXXPRMREZEKoDArl4bmvZzr0dZuAamHYVZfSPjU170SERGRcqYwK5eOOs2ddwxr0QdyMuGze+DbKeDI9XXPREREpJwozMqlJag6DP0YrnnE+fzHV+B/QyEzxafdEhERkfKhMCuXHrMFek+FW96BgCDYtQTe6QUn/vB1z0RERKSMKczKpavDbc4bLIQ1gOO/w8yesHu5r3slIiIiZUhhVi5tDa9w3mChUVfnVIOPboU1r+kGCyIiIpcIhVm59IXVh7u+gs53guGApZNg4f1gz/R1z0REROQiKcxK1RBggxtfg74vgMkCm/8HswdAaqKveyYiIiIXQWFWqg6TCa66D0Z8DkE14PAvzhssHPrF1z0TERGRUlKYlaqnaQ8YuwLqtoGzSfBef9j0P1/3SkREREpBYVaqplpN4d5l0Ko/5GbBwvtgySTIzfF1z0RERKQEFGal6rKFwZCP4Lq/Op+vfQ3m3g4Zp3zbLxERESk2hVmp2sxm+PNTcNtssIbAH8th5vVw7Hdf90xERESKQWFWBKDdzXD3EqgeBSf/gHeuh9+X+rpXIiIicgEKsyL5IjvAmBXQ+GrISnVOOfjxFd1gQUREpBJTmBUpKLQujPwCYkYDBnw7BT4fA/YMX/dMREREPFCYFTlfQCAMnA4D/g3mAEiYD7P6QsphX/dMREREzqMwK+LNlfc6R2mDa0HiJucNFg6s83WvREREpACFWZGiRF8LY1dC/csh7ajzFrgb5/i6VyIiIpJHYVbkQmo2ca500OZGcNjhy/HwzRO6wYKIiEgloDArUhy2ULjtfejxN+fzdTPgw1sg/aRv+yUiIlLFKcyKFJfZDD2egCEfgrUa7F0FM3vC0e2+7pmIiEiVpTArUlJtBsK9y6BGEzi1D97pBTu+9nWvREREqiSFWZHSqN/OeYOF6D9B9ln4eBiselE3WBAREalgCrMipVWtNoxYAF3HOp+veBbm3wXZaT7tloiISFWiMCtyMSxW6P8iDPwPmK2wbSHMioPTB3zdMxERkSrB52H29ddfJzo6mqCgILp168b69euL3H/69Om0atWK4OBgoqKiePTRR8nMzKyg3op4EXMXjPo/qFYXkhLg7Z6wb7WveyUiInLJ82mYnTdvHvHx8UyePJmNGzfSsWNH4uLiOHr0qMf9586dy5NPPsnkyZPZvn077777LvPmzeNvf/tbBfdcxIMmsc55tBEdIP04fHAj/DLL170SERG5pPk0zL788suMGTOG0aNH07ZtW2bMmEFISAizZnkOAGvWrOGaa65h2LBhREdH06dPH4YOHXrB0VyRClMjynmDhXa3gCMHvnoUvoqHXLuveyYiInJJCvDVhbOzs9mwYQMTJ050tZnNZnr16sXatWs9HnP11Vfz4Ycfsn79erp27cqePXtYtGgRI0aM8HqdrKwssrKyXM9TU1MBsNvt2O3lHzDyr1ER1/I3l2xtTFa46S3MddtiXvkPTL+8i+PodnJvmQXV6lzw8Eu2LmVAtfFMdfFOtfFMdfFOtfGsoutSkuuYDMM3awkdOXKEhg0bsmbNGmJjY13tEyZMYNWqVaxbt87jcf/97395/PHHMQyDnJwc7rvvPt58802v15kyZQpTp04t1D537lxCQkIu/o2IFKF+yq/E7HsTqyOT9MA6rLvsEVJDGvu6WyIiIpVaeno6w4YNIyUlhfDw8CL39dnIbGmsXLmS5557jjfeeINu3bqxe/duHn74Yf7+97/z9NNPezxm4sSJxMfHu56npqYSFRVFnz59LlicsmC321m2bBm9e/fGarWW+/X8SdWoTX84divG/DsJObWXHnueI/fG1zFaD/R6RNWoS+moNp6pLt6pNp6pLt6pNp5VdF3yf5NeHD4Ls3Xq1MFisZCcnOzWnpycTEREhMdjnn76aUaMGMG9994LQPv27UlLS2Ps2LFMmjQJs7nwFGCbzYbNZivUbrVaK/RDWtHX8yeXfG0aXA5jvoNP78a0ZwUBn42G7k9A9yedt8j14pKvy0VQbTxTXbxTbTxTXbxTbTyrqLqU5Bo++wJYYGAgMTExLF++3NXmcDhYvny527SDgtLT0wsFVovFAoCPZkuIFE9ILRj+KVw1zvl81QvwyQjIOuvbfomIiPg5n65mEB8fz8yZM3n//ffZvn07999/P2lpaYwePRqAkSNHun1BbODAgbz55pt8/PHH7N27l2XLlvH0008zcOBAV6gVqbQsAdD3ObjpDbAEwo6v4N3ecHKvr3smIiLit3w6Z3bIkCEcO3aMZ555hqSkJDp16sTixYupX78+AAcOHHAbiX3qqacwmUw89dRTHD58mLp16zJw4ED+8Y9/+OotiJRc5+FQpyXMGw5Ht8HMnnDb+9C0u697JiIi4nd8/gWw8ePHM378eI+vrVy50u15QEAAkydPZvLkyRXQM5FyFHUljF0JHw+HIxthzs3Q95/QdYyveyYiIuJXfH47W5EqK7wBjF4EHYaAkQvf/BX+7yHIzfZ1z0RERPyGwqyIL1mD4ea3oPffwWSGjR9g+fBmbPYUX/dMRETEL/h8moFIlWcywTUPQb228OndmA+to3vy75iXbnFOR2h4BdRq6txPRERE3CjMilQWLXrBmOUY/7uD4BO74ee3nBtAUA1o0BkaxjjDbcMYCPO8HrOIiEhVojArUpnUaUHO3d/y2yfP06muA0vSJkj8DTJPw54Vzi1fWIO8YJsXbht0hqDqvuq5iIiITyjMilQ2gaEcqnUNHeL6Y7FaISfbuYTX4Q3OlQ8Ob4RjO+DMEdhxxLlebb7aLQqE2ysgoj1Yg3z3XkRERMqZwqxIZRcQCA06OTfucbZlnYXEzXnhdoMz4J7eDyd2Obff5jn3MwdA/Xbnwm3DGKjbCsy6yYiIiFwaFGZF/JEtFKKvcW750o47Q23BgJt+3Bl6EzcDs5z7Was5g3HDK84F3BqN9QUzERHxSwqzIpeKanWgZR/nBmAYcPpAgXD7Kxz5FexpsH+1c8sXUtt99LbhFc7ziYiIVHIKsyKXKpMJajZxbu1udrY5cuH47+dGbg9vgOStkH4Cdi11bvlqNHYPt5GdnCPCIiIilYjCrEhVYrZAvTbOrfOdzjZ7JiRvORduj2x0Bt7TB5zbtoXO/UxmqNMqL9zmLRNWr51zTq+IiIiPKMyKVHXWIGjUxbnly0yBI5vcV1BIPQzHtju3TR8697PYnCsmFFxBoXZzMOvmgiIiUjEUZkWksKDq0LS7c8t3JqnwF8wyT8PhX5xbPlt43hfMYs4F3PAG+oKZiIiUC4VZESmesAho3d+5gfMLZif3OL9UdniDc0vcDFmpsPd755YvNOLcDR4aXOG8wUNILd+8DxERuaQozIpI6ZhMULuZc2t/q7Mt1w5Ht7uvoHB0G5xNgp2LnFu+Wk3dV1CI7ADWYN+8F5GqIuM0pBzEdGIvDU79hOlwPajdFELr6bcn4rcUZkWk7FiszlAa2QFi7nK2ZadD0m/uKyic2usc1T25BxLmO/czWaB+W/cVFOq2AYt+TIkUi2HA2aOQctD55c2Ug3D6oPt/s1IB5z/+VwLMfsN5bEAQVI9yrn5So3He1iRva+xcqk9hVyop/SshIuUrMAQaX+Xc8qWfzBu9LTBFIe0oJCU4t43vO/cLCIbIjufCbf0Ozn+wRaqi3BznFzHdQmrB0HoIcrMufJ6Q2jjCG3HqTAa1LOmYzhyBnMxzdxD0xBpSIOQ2PhdyazSGmtEQXFNhV3xGYVZEKl5ILWjey7mBM6CmHnZfHuzwr5B9Bg7+5NwAK9DPUg1LajdoVOALZmH1ffdeRMqKPSMvlB4oPKJ6+iCcOQKGo+hzmMwQFukcZa0RVeC/jfP+2wgCq5Frt/PjokX0798fqynv71/+cnyn9597fGo/nEkEezoc2+HcPAkM9RJ08x4H1VDYlXKjMCsivmcyOf+Rrd4I2t7obHM44MTuAuF2A0ZSAoG5abDnO+eWL7zRuS+YNYxx3uAhKNwnb0XEI8Nwrv5RKKQWGFlNP37h81gC8/6unB9S856HN3RO9ymJgECodZlz8yQnyznq6ynonj7gnBOffdY5P/7oNs/nsIV7D7o1GjtXUBEpJYVZEamczGao29K5dRoKQE5mGmsWvM21l4VgSdrsHMk9tgNSDzm37V/mHWyCOi3d17+NuBwCbL57P3JpczicU2WKGlnNPnPh8wSGnTeiel5orVav4tdxDrCd+7KnJ/bMvLC7r3DQPX3AWZesVOfNWZK3eD5HUPUCQbfJeXN3G4MtrNzenvg/hVkR8R+WQE6HNMUR0x+LNW/0KeuMc0kw1xfMNjrDxPGdzm3z/5z7ma3OQFtwBYU6LZx3RRO5kFx73q/iC4bUAqE15XAx56vWKRBSGxcOrf7463hrENRp7tw8yU4/Nwp9er970D19wDkinZni/KJo0m+ezxFc67wR3fPCbmC18nt/UukpzIqIf7OFQfS1zi3f2WPuN3c4vAEyTjrXxD3y67n9AkOda9426HzuS2bVo/wvTMjFcwUuLyOrZxKLOV+1gfeR1eqNnF+IrGoCQ6BuK+fmSdbZc2H31H73qQyn90PGKeff34yTkLjJ8zlC6hSeuuBajSFKy/5d4hRmReTSE1oXWsY5N3DOVzy93330NnGTc57fvh+cW75qdQssD5YXcHWDB/9mGM5AdDzR+8hq+okLn8dicwZSbyOr4Q1KPl9VwBYK9do4N08yU51/RqfOC7n5oTczxTm6m37c+T+xnlSrV3jqQn7Yrd7IObosfkthVkQufSaTc/mgmtFw+WBnW26OcxpCwRUUkrdC2jHYtcS55asZ7b7+bWRH/VqzMnE44Gyyx/VVA04fYMCJfQRsyrzweWzhHkZUC4TWanUrfr6qOL/MGdQO6rfz/HrGafdpCwW/qHZqv3OuctpR53boZ8/nCIt0hVxzWCMaH0/BtLca1L7M+WcfEFhub08unsKsiFRNlgDnP47128EVI5xt9gxI2uK2ggIndsOpfc5t6+fO/Uxm5w0dCq6gUK+tRuXKS062h/VVC4ysph6G3GyPh5oo8A9dtbqeQ2r+8+AaFfSGpEwF13BukR0Kv5Y/Ku9p2bH8sGtPc04jOZMIB9dhAToDzJ2VdxKTc9Td29JjpVlBQsqUwqyISD5rMERd6dzyZZzOm2u78dwUhTNH4OhW5/brHOd+AUEQ0cF9BYVaTTWSVxzZaV5uApC/vmoicIGbZZgszsBx3shqTmgDVm3aw3UDh2EN0XJtVY7J5JwmFFILGnQq/LphOG/iUmDaQu7JfRzbtYH6gRmYTh+EnAzn/zClHoYDaz1cw+wMtN6WHgtroDsZljNVV0SkKME1oFlP55YvNbHAF8w2OMNuZgocWu/c8gVVP/flsvxpCuGRFf4WfKrgyJi39VUzTl74PAFBRa+v6iUwGHY7Z3dk6AtA4pnJBNVqO7eGVwDgsNtZ58i7oURAAKQdd5+je/7SY7lZeStaHIT9qz1cwwLVG7rfHrjg/N2wSK2qcpEUZkVESio8EsIHQOsBzucOB5zc476CQuJmZ8Dds9K55QuLzAu3nc/9159/ve1wOBfNL2pk1Z524fPYql9gfdW6WmVCKp7J5PxCaWhdaNSl8Ouu9YULTGMoGHRTDjqnwOQ/54fC5zBb875Y6GnpsSYQWl+/4bkAhVkRkYtlNp9bZ7PD7c62XLvzbkgFV1A4tt35K/MdXzm3fLWbu3/BLKJ95RlJzMl23pDC252rUg6Dw37h81Sr5+X2qvnrq+oOUOKHzGYIi3BuUV0Lv+76n72CI7r7zwXflEPOvz+n9jo3TyyB5+Z412xy3nSGJhBar8r/j57CrIhIebBYnaseRHaELnc727LT8m7wUGAFhVP7nF8yO7EbEj5x7mfO+3JawYBbt3X5/CrStcant/VVkyjefNWGRa+vqqWPpCoym51zucMbQOOrCr/uyHX+D+75UxfypzWk5H258eQfzs2TgCDn37dCS49FO/9brc4lH3YVZkVEKkpgNWhytXPLl3bCOee24AoKacecoTdxM2x4z7mftZozGBdcQaFGk6L/kcr/covH26vmjaxmnLpwv/P/sfQ2shoWqS+4iJSG2ZI3F7yR+8+FfLk5zi+cFgq6eY9TD0NOJpzY5dw8sYZ4WF+3wOOQWn4fdvXTR0TEl6rVhha9nBs4A2jKoQLhdqMz7GafhQNrnFu+4FrQMAZzREeiTqRgXv07nDlvCSt7+oX7EFS98K/9C4bWKjCyI1IpWQLOBU9P8pet8xR0T+13jvra0+HYDufmSWCol6CbN63BD26xrDArIlKZmEzOEFkjCtoNcrY5cuH4LvcvmCUlOFcB2L0My+5lXAFwwMs5Q+sXvb5qkJasEvFLAYFQ6zLn5klOlvN/jj0F3dMHnPN5s8865/cf3eb5HLZwqNEYS/UoLj+VC6kdoXZ0ub2l0lCYFRGp7MwWqNfauXUa5mzLyYLkLXB4I45Dv3Biz2/Uvqw95ppN3ENreEPNVxWpqgJsULuZc/PEnpkXdvd5XnYs7ShkpULyFszJW2gG2HP/UZHvoFgUZkVE/FGALe/LYTHkdr6LNYuc62KarboTkYgUkzXo3EosnmSnu+bY557Yw56NK4kOb1ixfSwGhVkRERERKSwwBOq2grqtcETb2ZYcQbQl0Ne9KkSr8IqIiIiI36oUYfb1118nOjqaoKAgunXrxvr1673u26NHD0wmU6FtwIABFdhjEREREakMfB5m582bR3x8PJMnT2bjxo107NiRuLg4jh496nH/zz//nMTERNe2ZcsWLBYLt912WwX3XERERER8zedh9uWXX2bMmDGMHj2atm3bMmPGDEJCQpg1a5bH/WvVqkVERIRrW7ZsGSEhIQqzIiIiIlWQT78Alp2dzYYNG5g4caKrzWw206tXL9auXVusc7z77rvccccdVKtWzePrWVlZZGVluZ6npqYCYLfbsduLcT/xi5R/jYq4lr9RbTxTXbxTbTxTXbxTbTxTXbxTbTyr6LqU5DomwzAucNPt8nPkyBEaNmzImjVriI2NdbVPmDCBVatWsW7duiKPX79+Pd26dWPdunV07drV4z5Tpkxh6tSphdrnzp1LSEjIxb0BERERESlz6enpDBs2jJSUFMLDi76xi18vzfXuu+/Svn17r0EWYOLEicTHx7uep6amEhUVRZ8+fS5YnLJgt9tZtmwZvXv3xqr1H92oNp6pLt6pNp6pLt6pNp6pLt6pNp5VdF3yf5NeHD4Ns3Xq1MFisZCcnOzWnpycTERERJHHpqWl8fHHHzNt2rQi97PZbNhstkLtVqu1Qj+kFX09f6LaeKa6eKfaeKa6eKfaeKa6eKfaeFZRdSnJNXz6BbDAwEBiYmJYvny5q83hcLB8+XK3aQeezJ8/n6ysLO68887y7qaIiIiIVFI+n2YQHx/PqFGj6NKlC127dmX69OmkpaUxevRoAEaOHEnDhg15/vnn3Y579913GTRoELVr1/ZFt0VERESkEvB5mB0yZAjHjh3jmWeeISkpiU6dOrF48WLq168PwIEDBzCb3QeQd+7cyY8//sjSpUt90WURERERqSR8HmYBxo8fz/jx4z2+tnLlykJtrVq1woeLMIiIiIhIJeHzmyaIiIiIiJSWwqyIiIiI+C2FWRERERHxWwqzIiIiIuK3ShVmDx48yKFDh1zP169fzyOPPMLbb79dZh0TEREREbmQUoXZYcOGsWLFCgCSkpLo3bs369evZ9KkSRe8I5eIiIiISFkpVZjdsmULXbt2BeCTTz7h8ssvZ82aNXz00UfMnj27LPsnIiIiIuJVqcKs3W7HZrMB8O2333LjjTcC0Lp1axITE8uudyIiIiIiRShVmG3Xrh0zZszghx9+YNmyZfTt2xeAI0eO6PayIiIiIlJhShVmX3jhBd566y169OjB0KFD6dixIwBffvmla/qBiIiIiEh5K9XtbHv06MHx48dJTU2lZs2arvaxY8cSEhJSZp0TERERESlKqUZmMzIyyMrKcgXZ/fv3M336dHbu3Em9evXKtIMiIiIiIt6UKszedNNNfPDBBwCcPn2abt268e9//5tBgwbx5ptvlmkHRURERES8KVWY3bhxI3/6058A+PTTT6lfvz779+/ngw8+4L///W+ZdlBERERExJtShdn09HTCwsIAWLp0Kbfccgtms5mrrrqK/fv3l2kHRURERES8KVWYbd68OQsXLuTgwYMsWbKEPn36AHD06FHCw8PLtIMiIiIiIt6UKsw+88wzPP7440RHR9O1a1diY2MB5yht586dy7SDIiIiIiLelGpprltvvZVrr72WxMRE1xqzANdffz0333xzmXVORERERKQopQqzABEREURERHDo0CEAGjVqpBsmiIiIiEiFKtU0A4fDwbRp06hevTpNmjShSZMm1KhRg7///e84HI6y7qOIiIiIiEelGpmdNGkS7777Lv/85z+55pprAPjxxx+ZMmUKmZmZ/OMf/yjTToqIiIiIeFKqMPv+++/zzjvvcOONN7raOnToQMOGDXnggQcUZkVERESkQpRqmsHJkydp3bp1ofbWrVtz8uTJi+6UiIiIiEhxlCrMduzYkddee61Q+2uvvUaHDh0uulMiIiIiIsVRqmkG//rXvxgwYADffvuta43ZtWvXcvDgQRYtWlSmHRQRERER8aZUI7Pdu3fn999/5+abb+b06dOcPn2aW265ha1btzJnzpyy7qOIiIiIiEelXme2QYMGhb7otXnzZt59913efvvti+6YiIiIiMiFlGpkVkRERESkMlCYFRERERG/pTArIiIiIn6rRHNmb7nlliJfP3369MX0RURERESkREoUZqtXr37B10eOHHlRHRIRERERKa4Shdn33nuvvPohIiIiIlJimjMrIiIiIn5LYVZERERE/JbCrIiIiIj4LYVZEREREfFbPg+zr7/+OtHR0QQFBdGtWzfWr19f5P6nT59m3LhxREZGYrPZaNmyJYsWLaqg3oqIiIhIZVKi1QzK2rx584iPj2fGjBl069aN6dOnExcXx86dO6lXr16h/bOzs+nduzf16tXj008/pWHDhuzfv58aNWpUfOdFRERExOd8GmZffvllxowZw+jRowGYMWMGX3/9NbNmzeLJJ58stP+sWbM4efIka9aswWq1AhAdHV2RXRYRERGRSsRnYTY7O5sNGzYwceJEV5vZbKZXr16sXbvW4zFffvklsbGxjBs3ji+++IK6desybNgwnnjiCSwWi8djsrKyyMrKcj1PTU0FwG63Y7fby/AdeZZ/jYq4lr9RbTxTXbxTbTxTXbxTbTxTXbxTbTyr6LqU5DomwzCMcuyLV0eOHKFhw4asWbOG2NhYV/uECRNYtWoV69atK3RM69at2bdvH8OHD+eBBx5g9+7dPPDAAzz00ENMnjzZ43WmTJnC1KlTC7XPnTuXkJCQsntDIiIiIlIm0tPTGTZsGCkpKYSHhxe5r0+nGZSUw+GgXr16vP3221gsFmJiYjh8+DAvvvii1zA7ceJE4uPjXc9TU1OJioqiT58+FyxOWbDb7SxbtozevXu7pkaIk2rjmerinWrjmerinWrjmerinWrjWUXXJf836cXhszBbp04dLBYLycnJbu3JyclERER4PCYyMhKr1eo2paBNmzYkJSWRnZ1NYGBgoWNsNhs2m61Qu9VqrdAPaUVfz5+oNp6pLt6pNp6pLt6pNp6pLt6pNp5VVF1Kcg2fLc0VGBhITEwMy5cvd7U5HA6WL1/uNu2goGuuuYbdu3fjcDhcbb///juRkZEeg6yIiIiIXNp8us5sfHw8M2fO5P3332f79u3cf//9pKWluVY3GDlypNsXxO6//35OnjzJww8/zO+//87XX3/Nc889x7hx43z1FkRERETEh3w6Z3bIkCEcO3aMZ555hqSkJDp16sTixYupX78+AAcOHMBsPpe3o6KiWLJkCY8++igdOnSgYcOGPPzwwzzxxBO+egsiIiIi4kM+/wLY+PHjGT9+vMfXVq5cWagtNjaWn376qZx7JSIiIiL+wOe3sxURERERKS2FWRERERHxWwqzIiIiIuK3FGZFRERExG8pzIqIiIiI31KYFRERERG/pTArIiIiIn5LYVZERERE/JbCrIiIiIj4LYVZEREREfFbCrMiIiIi4rcUZkVERETEbynMioiIiIjfUpgVEREREb+lMCsiIiIifkthVkRERET8lsKsiIiIiPgthVkRERER8VsKsyIiIiLitxRmRURERMRvKcyKiIiIiN9SmBURERERv6UwKyIiIiJ+S2FWRERERPyWwqyIiIiI+C2FWRERERHxWwqzIiIiIuK3FGZFRERExG8pzIqIiIiI31KYFRERERG/pTArIiIiIn5LYVZERERE/JbCrIiIiIj4LYVZEREREfFbCrMiIiIi4rcUZkVERETEb1WKMPv6668THR1NUFAQ3bp1Y/369V73nT17NiaTyW0LCgqqwN6KiIiISGXh8zA7b9484uPjmTx5Mhs3bqRjx47ExcVx9OhRr8eEh4eTmJjo2vbv31+BPRYRERGRysLnYfbll19mzJgxjB49mrZt2zJjxgxCQkKYNWuW12NMJhMRERGurX79+hXYYxERERGpLAJ8efHs7Gw2bNjAxIkTXW1ms5levXqxdu1ar8edPXuWJk2a4HA4uOKKK3juuedo166dx32zsrLIyspyPU9NTQXAbrdjt9vL6J14l3+NiriWv1FtPFNdvFNtPFNdvFNtPFNdvFNtPKvoupTkOibDMIxy7EuRjhw5QsOGDVmzZg2xsbGu9gkTJrBq1SrWrVtX6Ji1a9eya9cuOnToQEpKCi+99BLff/89W7dupVGjRoX2nzJlClOnTi3UPnfuXEJCQsr2DYmIiIjIRUtPT2fYsGGkpKQQHh5e5L4+HZktjdjYWLfge/XVV9OmTRveeust/v73vxfaf+LEicTHx7uep6amEhUVRZ8+fS5YnLJgt9tZtmwZvXv3xmq1lvv1/Ilq45nq4p1q45nq4p1q45nq4p1q41lF1yX/N+nF4dMwW6dOHSwWC8nJyW7tycnJREREFOscVquVzp07s3v3bo+v22w2bDabx+Mq8kNa0dfzJ6qNZ6qLd6qNZ6qLd6qNZ6qLd6qNZxVVl5Jcw6dfAAsMDCQmJobly5e72hwOB8uXL3cbfS1Kbm4uCQkJREZGllc3RURERKSS8vk0g/j4eEaNGkWXLl3o2rUr06dPJy0tjdGjRwMwcuRIGjZsyPPPPw/AtGnTuOqqq2jevDmnT5/mxRdfZP/+/dx7772+fBsiIiIi4gM+D7NDhgzh2LFjPPPMMyQlJdGpUycWL17sWm7rwIEDmM3nBpBPnTrFmDFjSEpKombNmsTExLBmzRratm3rq7cgIiIiIj7i8zALMH78eMaPH+/xtZUrV7o9f+WVV3jllVcqoFciIiIiUtn5/KYJIiIiIiKlpTArIiIiIn5LYVZERERE/JbCrIiIiIj4LYVZEREREfFbCrMiIiIi4rcUZkVERETEbynMioiIiIjfUpgVEREREb+lMCsiIiIifkthVkRERET8lsKsiIiIiPgthVkRERER8VsKsyIiIiLitxRmRURERMRvKcyKiIiIiN9SmBURERERv6UwKyIiIiJ+S2FWRERERPyWwqyIiIiI+C2FWRERERHxWwqzIiIiIuK3FGZFRERExG8pzIqIiIiI31KYFRERERG/pTBbzhIOp7Ay0URSaqavuyIiIiJyyQnwdQcudfM3HGbBPgsLXvyeLk1q0r99JP3bRxJRPcjXXRMRERHxewqz5axDo+r8tOMge8+Y+GX/KX7Zf4ppX22jS5OaDOgQSb/LFWxFRERESkthtpzdekVDQpI20/maP/PtjuMsSkh0hdpf9p9i6v8p2IqIiIiUlsJsBYmsHsTd117G3ddeRmJKBt8kJPF1QiIbzgu2V0Y7pyIo2IqIiIhcmMKsD0RWD/YabH/e59zypyIo2IqIiIh4pzDrY+cH20UJSSwqItj2bx9J/XAFWxERERFQmK1UIqsHc8+1l3HPtZdx5HQG32zxHmwHtI+kn4KtiIiIVHEKs5VUgxqFg+3Xvx1h44HTrmA79attXNmkFv3bRyjYioiISJWkMOsHigq26/edZP2+kwq2IiIiUiUpzPqZ84PtooREFiUkKtiKiIhIlaQw68ca1Ajm3j815d4/Nb1gsHWuYxtBPQVbERERuYSYfd0BgNdff53o6GiCgoLo1q0b69evL9ZxH3/8MSaTiUGDBpVvB/1AfrD9/IFrWPPkn3lqQBuuaFwDw4D1+04y+cutdHt+Obe/tZb31+zjaGqmr7ssIiIictF8PjI7b9484uPjmTFjBt26dWP69OnExcWxc+dO6tWr5/W4ffv28fjjj/OnP/2pAnvrHzyN2H6dkMivB06zfu9J1u89yZT/28qV0bWcqyJoxFZERET8lM9HZl9++WXGjBnD6NGjadu2LTNmzCAkJIRZs2Z5PSY3N5fhw4czdepUmjZtWoG99T/5wXbBA9ewOm/EtnP+iO1ejdiKiIiIf/PpyGx2djYbNmxg4sSJrjaz2UyvXr1Yu3at1+OmTZtGvXr1uOeee/jhhx+KvEZWVhZZWVmu56mpqQDY7XbsdvtFvoMLy79GRVzrQupVC2DUVVGMuiqKI6czWLw1mW+2JrPpYIrbiG2XJjXpf3l94trWp26Yrdz6U5lqU5moLt6pNp6pLt6pNp6pLt6pNp5VdF1Kch2TYRhGOfalSEeOHKFhw4asWbOG2NhYV/uECRNYtWoV69atK3TMjz/+yB133MGmTZuoU6cOd911F6dPn2bhwoUerzFlyhSmTp1aqH3u3LmEhISU2XvxZyezYPMJE5tOmNl31uRqN2HQNAw613HQsZZBeKAPOykiIiJVRnp6OsOGDSMlJYXw8PAi9/X5nNmSOHPmDCNGjGDmzJnUqVOnWMdMnDiR+Ph41/PU1FSioqLo06fPBYtTFux2O8uWLaN3795YrdZyv15p3Zn33/wR20Vbktl8KIU/zsAfZyx8tg+ubFKTfmU4Yusvtaloqot3qo1nqot3qo1nqot3qo1nFV2X/N+kF4dPw2ydOnWwWCwkJye7tScnJxMREVFo/z/++IN9+/YxcOBAV5vD4QAgICCAnTt30qxZM7djbDYbNlvh4GW1Wiv0Q1rR1yutJnWt/KVHOH/p0YJDp9L5JiGJrxMS2XTwNOv3nWL9vlNM+3oHXaOdy331vTyCemEX9+Uxf6lNRVNdvFNtPFNdvFNtPFNdvFNtPKuoupTkGj4Ns4GBgcTExLB8+XLX8loOh4Ply5czfvz4Qvu3bt2ahIQEt7annnqKM2fO8J///IeoqKiK6HaV0ahmCGOua8qY65oWCrbr9p5kXd4XyLpG1+KGDpHElUGwFRERESkJn08ziI+PZ9SoUXTp0oWuXbsyffp00tLSGD16NAAjR46kYcOGPP/88wQFBXH55Ze7HV+jRg2AQu1StooTbJ9RsBUREZEK5vMwO2TIEI4dO8YzzzxDUlISnTp1YvHixdSvXx+AAwcOYDb7fAUxKcBTsP0qIZHN5wXbbpc517FVsBUREZHy4vMwCzB+/HiP0woAVq5cWeSxs2fPLvsOSbEVDLYHT6bzzZZEvk5IYvPB0/y05yQ/7cmbiqBgKyIiIuWgUoRZuTRE1Qph7HXNGHtds2IF216ti7cihYiIiIg3CrNSLooXbKFpmJlTdQ4yoEPDcr1Bg4iIiFyaFGal3HkMtr8lsvlQCrtTzUz5v+1M+2o73S6rTf8OkfRtF6FgKyIiIsWiMCsVqmCw3XM0hemfrmJfbk1+O5zK2j0nWLvnBJO/2KJgKyIiIsWiMCs+E1UzhOsbGvTvfxVJZ+wsSkhkUYJzxFbBVkRERIpDYVYqhahaIfylezP+0t05FaGoYJt/57E6oQq2IiIiVZ3CrFQ6noLt1wmJ/FYg2D6jYCsiIiIozEolV9xge1XT2vRvr2ArIiJS1SjMit84P9h+nTcV4bdDKaz54wRr/lCwFRERqWoUZsUvRdUK4b7uzbivezMOnEhn0RbvwXZAh0ji2inYioiIXIoUZsXvNa5dONh+/VsiCYfPBdunF54Ltn3bRVBbwVZEROSSoDArl5TiBtvYZnlTERRsRURE/JrCrFyyzg+2+XNsEw6nsHr3CVbvVrAVERHxdwqzUiU0rh3C/T2acX8PBVsREZFLicKsVDnFCbbPfLGVq5rWYkD7BsS1q69gKyIiUkkpzEqVVjDY7j+RxqKEJL5OOMKWw6nnRmy/2EJs3nJfCrYiIiKVi8KsSJ4mtau5Bdv8Edsth1P5cfdxftx9XMFWRESkklGYFfGgSe1qPNCjOQ/0aK5gKyIiUokpzIpcQEmCbf4NGmpVC/R1t0VERKoEhVmREigYbPcdT3OtY7v1yLlg+9TCLVzdLH/EVsFWRESkPCnMipRSdB33YJs/Yrv1SCo/7DrOD7sUbEVERMqbwqxIGYiuU41xPZszrqeCrYiISEVSmBUpYyUJtgPaR9JHwVZERKTUFGZFypGnYPv1b4lsSzwXbCcVCLZx7SIIDTT5utsiIiJ+Q2FWpIIUDLZ7j6exyEuwjW1ai4hcE7YdR6kbHkz14EBqhlipHmwlwGL29dsQERGpVBRmRXzgsiKC7Y+7TwAWPt27qdBxYbYAalSzUiM4kBohVmqEBFIj2OoMu/mPq1ldAbhGSCDhQQEKwSIicslSmBXxsfOD7f9tOsSin3/HGlqDlIwcTqVncyYzB4AzWTmcycrhIBklukZ4UAA1QgLdQ2/eY2foLRyQw4OtWMya8iAiIpWbwqxIJXJZnWrc370pTdJ20L//VVitVgBych2kZNg5nWHndLqd0+nZnE63cyo929nu6XG6nTNZzhCcmplDamYOB04Wvy8mE4QHWQsF4BoheaE3uMDj/FAcHEhYUABmhWAREakgCrMifiDAYqZ2qK3Et8y154fgIgKwMyBn5+3jfJyWnYthQEqGnZQMO5xIL/Y1zSaoHuwl9AYH5k2DcA/ANapZCbMFYDIpBIuISMkozIpcwqwWM3VCbdQpYQjOznFwOsM5uns6w86ptPNCr4cAfDrDTnp2Lg4DTqXbOZVuL9E1LWZTXsj1EIDzpkKEBpr5/bSJxodTqRMe7GxTCBYRqdIUZkWkkMAAM/XCgqgXFlSi4zLtuaRmXDgAn8r7b0qG83Gm3UGuw+BkWjYn07IvcBULb27/yfUswGyiRt5qDzXzAnD1AgG4esER4Pz5wSGBVAu0KASLiFwCFGZFpMwEWS0EWS3UCy95CHYG3uzzpkTYXSPEp9KzOZWWzcHkkzgCgjiVYSc7x0GOw+D42WyOn80G0op9TavF5B56Czw+f1S4esi5oBxsVQgWEalMFGZFxOeCrBYiqluIqF50CLbb7SxatIj+/btjtVrJyM51BeD8L72dH4BdUyEyzj3OznVgzzU4fjaL42ezStTXQIvZbYS3Rt7UiJohgVT3EoBrBAcSHGi5mBKJiIgXCrMi4reCAy0EBwYTWT242McYhkGGPdcZes8LwOePCqdkZOft52zPcRhk5zo4eiaLo2dKFoJtAeZzofe8AFyzQCjOHxXO3y/IqhAsIlIUhVkRqVJMJhMhgQGEBAbQsEbJQnBadq77F9/ywm6KxwCcnTcn2E6uwyArx0FyahbJqSULwUFWc6EAfP6ocFighd0psC0xlVqhwYTaAgjTzTJEpIpQmBURKQaTyUSoLYBQWwCNahb/OMMwOJuV4zEAnyo0BcL9y3IOAzLtDhJTMklMybzAlQJ4ddtPbi3BVgthQQGEBgUQFmQlPCjAFXTDgqyux+FB1rx9zrWH5z0Ospo1R1hEKrVKEWZff/11XnzxRZKSkujYsSOvvvoqXbt29bjv559/znPPPcfu3bux2+20aNGCxx57jBEjRlRwr0VELsxkMhEWZCUsyEpUreIf53AYnMnKyVse7dxob+E5wM4VIBKPn8YRYONsVg6ZdgcAGfZcMuy5JZ4SUVCA2eQKuqE2a174dQ/DYXlhOPy8/cKCAgizOV/T3eREpLz4PMzOmzeP+Ph4ZsyYQbdu3Zg+fTpxcXHs3LmTevXqFdq/Vq1aTJo0idatWxMYGMhXX33F6NGjqVevHnFxcT54ByIiZc+ct+5u9WArjQkpct9zX4zrgdVqJTvHwdmsHM5m5pCaaedMZg5ns3I4k/f4TKbz7nDOxzmcdbU790vNtHM2KwfDgByH4QrQlPA2ygVVC7S4RojD8kaIwws8drV7CcthQQHYAjRKLCKF+TzMvvzyy4wZM4bRo0cDMGPGDL7++mtmzZrFk08+WWj/Hj16uD1/+OGHef/99/nxxx8VZkVEcK4TXCsgkFrVAkt9DofDIN2eWyAAnwvD7sH43Gtns9wfp2bmkJ3jHCVOy84lLTu3xHOGC7JaTG5h2BV0zxshLtgeHABH0uHI6QxqhkFooG63LHKp8WmYzc7OZsOGDUycONHVZjab6dWrF2vXrr3g8YZh8N1337Fz505eeOEFj/tkZWWRlXXuh2dqairgHMmw20t2h6LSyL9GRVzL36g2nqku3qk2npVXXWxmsIUEUCek9P9UZBUYJT4XhHM4k2XnbFZugfCb69zH1Z4XlrNySMvKBcCeW9wba5wvgBc2/+B6Vs1mcQVgVyi2WQkNsrhGiUNtFrf2MNu5keNQm3OU2J/p75J3qo1nFV2XklzHZBiGUY59KdKRI0do2LAha9asITY21tU+YcIEVq1axbp16zwel5KSQsOGDcnKysJisfDGG29w9913e9x3ypQpTJ06tVD73LlzCQkp+ld3IiLiew4DsnIhIxcycyEzBzJyTefaciAz11Tgcf6+prx9nW25RtmNyAaYDIICIMgCwRYIshjOx3ltzsfGuccWCMp77nwMgWbQILGIZ+np6QwbNoyUlBTCw8OL3Nfn0wxKIywsjE2bNnH27FmWL19OfHw8TZs2LTQFAWDixInEx8e7nqemphIVFUWfPn0uWJyyYLfbWbZsGb1798ZqtZb79fyJauOZ6uKdauOZ6uJdfm169eqFYbK45gqfLThnOCvH1Z7mpT1/dDkt2zlKnGOYOGuHs67Bo5KnUpMJ50iw7dwIcWhQ3vO8/7qPHp8bGS7Ybi3FEmz6zHin2nhW0XXJ/016cfg0zNapUweLxUJycrJbe3JyMhEREV6PM5vNNG/eHIBOnTqxfft2nn/+eY9h1mazYbPZCrVbrdYK/ZBW9PX8iWrjmerinWrjmeriXWBgIFarldAQiLyI8+Q6DLc5w8WZP+xqzzr3ONdhYBi4nl+MIKuZUJv13GoSedMjPC/L5mwPCoDEdDicmk2wzXlnO6vFTGCA879Wi6nKf9lOf588q6i6lOQaPg2zgYGBxMTEsHz5cgYNGgSAw+Fg+fLljB8/vtjncTgcbvNiRUREyoOlwCoTpWUYBpl2x3mrSthd84pTPXyZztnuvvJEht05Spxpd5BpL/mtmSGAf25e7fVVq8WUF2zzgq7FhDUg/7EZa0BeW8F9Agofcy4g54dlU4HXzVjzjjl3znOB+vyArdAtnvh8mkF8fDyjRo2iS5cudO3alenTp5OWluZa3WDkyJE0bNiQ559/HoDnn3+eLl260KxZM7Kysli0aBFz5szhzTff9OXbEBERKRaTyZR3K2YLhRegLL6cXIfbdInzR4tTzxs5dgvLmXZOnc3AZLFiz3WQnevg/G/Q2HMN7Lm5QO7FvN1yV1Tozg++JQndFpPBnkMmjvy4j6DAgEKh+9w5FborC5+H2SFDhnDs2DGeeeYZkpKS6NSpE4sXL6Z+/foAHDhwALP53HygtLQ0HnjgAQ4dOkRwcDCtW7fmww8/ZMiQIb56CyIiIhUuwGJ23tY4pORLsJ1bmzjO9evcXIfhCrb2HAf2XIPsnLznBbbsHMP9ea6BvcB+2XnHnnvdgb3AMe7nNAocU+D5+efKO6biQreFrw/+XobnK7vQXTAkFzXSXZLQfW5E3D9Dt8/DLMD48eO9TitYuXKl2/Nnn32WZ599tgJ6JSIiUnVYzCYsZgtBVouvu1IkT6HbnusgK6d4oTv/WG+hO8uew559+4lo0BC7g2KFbnuO4epPxYbusuctdAeYTWSmWegQm8Fl9SrXXOJKEWZFREREiqO8Q7dz1Hov/fu3v6gvOnkL3dluobjo0H0upBcx0p2bP4qeW6zQXXB0vOSh24TDdyu6eqUwKyIiIlLG/H2k232qh4OMLDur166jXljhFaJ8TWFWREREpIoqbui22+2c2G5UynDu3/fjExEREZEqTWFWRERERPyWwqyIiIiI+C2FWRERERHxWwqzIiIiIuK3FGZFRERExG8pzIqIiIiI31KYFRERERG/pTArIiIiIn5LYVZERERE/JbCrIiIiIj4LYVZEREREfFbCrMiIiIi4rcUZkVERETEbwX4ugMVzTAMAFJTUyvkena7nfT0dFJTU7FarRVyTX+h2nimunin2nimunin2nimunin2nhW0XXJz2n5ua0oVS7MnjlzBoCoqCgf90REREREinLmzBmqV69e5D4moziR9xLicDg4cuQIYWFhmEymcr9eamoqUVFRHDx4kPDw8HK/nj9RbTxTXbxTbTxTXbxTbTxTXbxTbTyr6LoYhsGZM2do0KABZnPRs2Kr3Mis2WymUaNGFX7d8PBw/aXwQrXxTHXxTrXxTHXxTrXxTHXxTrXxrCLrcqER2Xz6ApiIiIiI+C2FWRERERHxWwqz5cxmszF58mRsNpuvu1LpqDaeqS7eqTaeqS7eqTaeqS7eqTaeVea6VLkvgImIiIjIpUMjsyIiIiLitxRmRURERMRvKcyKiIiIiN9SmBURERERv6UwWwZef/11oqOjCQoKolu3bqxfv77I/efPn0/r1q0JCgqiffv2LFq0qIJ6WvFKUpvZs2djMpnctqCgoArsbcX4/vvvGThwIA0aNMBkMrFw4cILHrNy5UquuOIKbDYbzZs3Z/bs2eXez4pW0rqsXLmy0OfFZDKRlJRUMR2uIM8//zxXXnklYWFh1KtXj0GDBrFz584LHlcVfs6UpjZV4efMm2++SYcOHVyL28fGxvLNN98UeUxV+LxAyWtTFT4vnvzzn//EZDLxyCOPFLlfZfncKMxepHnz5hEfH8/kyZPZuHEjHTt2JC4ujqNHj3rcf82aNQwdOpR77rmHX3/9lUGDBjFo0CC2bNlSwT0vfyWtDTjvLJKYmOja9u/fX4E9rhhpaWl07NiR119/vVj77927lwEDBtCzZ082bdrEI488wr333suSJUvKuacVq6R1ybdz5063z0y9evXKqYe+sWrVKsaNG8dPP/3EsmXLsNvt9OnTh7S0NK/HVJWfM6WpDVz6P2caNWrEP//5TzZs2MAvv/zCn//8Z2666Sa2bt3qcf+q8nmBktcGLv3Py/l+/vln3nrrLTp06FDkfpXqc2PIRenatasxbtw41/Pc3FyjQYMGxvPPP+9x/9tvv90YMGCAW1u3bt2Mv/zlL+XaT18oaW3ee+89o3r16hXUu8oBMBYsWFDkPhMmTDDatWvn1jZkyBAjLi6uHHvmW8Wpy4oVKwzAOHXqVIX0qbI4evSoARirVq3yuk9V+jlTUHFqUxV/zhiGYdSsWdN45513PL5WVT8v+YqqTVX7vJw5c8Zo0aKFsWzZMqN79+7Gww8/7HXfyvS50cjsRcjOzmbDhg306tXL1WY2m+nVqxdr1671eMzatWvd9geIi4vzur+/Kk1tAM6ePUuTJk2Iioq64P8tVxVV5TNTWp06dSIyMpLevXuzevVqX3en3KWkpABQq1Ytr/tU1c9McWoDVevnTG5uLh9//DFpaWnExsZ63Keqfl6KUxuoWp+XcePGMWDAgEKfB08q0+dGYfYiHD9+nNzcXOrXr+/WXr9+fa/z9pKSkkq0v78qTW1atWrFrFmz+OKLL/jwww9xOBxcffXVHDp0qCK6XGl5+8ykpqaSkZHho175XmRkJDNmzOCzzz7js88+Iyoqih49erBx40Zfd63cOBwOHnnkEa655houv/xyr/tVlZ8zBRW3NlXl50xCQgKhoaHYbDbuu+8+FixYQNu2bT3uW9U+LyWpTVX5vAB8/PHHbNy4keeff75Y+1emz01AhV9RxIvY2Fi3/zu++uqradOmDW+99RZ///vffdgzqYxatWpFq1atXM+vvvpq/vjjD1555RXmzJnjw56Vn3HjxrFlyxZ+/PFHX3el0ilubarKz5lWrVqxadMmUlJS+PTTTxk1ahSrVq3yGtqqkpLUpqp8Xg4ePMjDDz/MsmXL/PILbgqzF6FOnTpYLBaSk5Pd2pOTk4mIiPB4TERERIn291elqc35rFYrnTt3Zvfu3eXRRb/h7TMTHh5OcHCwj3pVOXXt2vWSDXrjx4/nq6++4vvvv6dRo0ZF7ltVfs7kK0ltznep/pwJDAykefPmAMTExPDzzz/zn//8h7feeqvQvlXt81KS2pzvUv28bNiwgaNHj3LFFVe42nJzc/n+++957bXXyMrKwmKxuB1TmT43mmZwEQIDA4mJiWH58uWuNofDwfLly73Ov4mNjXXbH2DZsmVFztfxR6Wpzflyc3NJSEggMjKyvLrpF6rKZ6YsbNq06ZL7vBiGwfjx41mwYAHfffcdl1122QWPqSqfmdLU5nxV5eeMw+EgKyvL42tV5fPiTVG1Od+l+nm5/vrrSUhIYNOmTa6tS5cuDB8+nE2bNhUKslDJPjcV/pWzS8zHH39s2Gw2Y/bs2ca2bduMsWPHGjVq1DCSkpIMwzCMESNGGE8++aRr/9WrVxsBAQHGSy+9ZGzfvt2YPHmyYbVajYSEBF+9hXJT0tpMnTrVWLJkifHHH38YGzZsMO644w4jKCjI2Lp1q6/eQrk4c+aM8euvvxq//vqrARgvv/yy8euvvxr79+83DMMwnnzySWPEiBGu/ffs2WOEhIQYf/3rX43t27cbr7/+umGxWIzFixf76i2Ui5LW5ZVXXjEWLlxo7Nq1y0hISDAefvhhw2w2G99++62v3kK5uP/++43q1asbK1euNBITE11benq6a5+q+nOmNLWpCj9nnnzySWPVqlXG3r17jd9++8148sknDZPJZCxdutQwjKr7eTGMktemKnxevDl/NYPK/LlRmC0Dr776qtG4cWMjMDDQ6Nq1q/HTTz+5XuvevbsxatQot/0/+eQTo2XLlkZgYKDRrl074+uvv67gHlecktTmkUcece1bv359o3///sbGjRt90Ovylb+k1Plbfi1GjRpldO/evdAxnTp1MgIDA42mTZsa7733XoX3u7yVtC4vvPCC0axZMyMoKMioVauW0aNHD+O7777zTefLkaeaAG6fgar6c6Y0takKP2fuvvtuo0mTJkZgYKBRt25d4/rrr3eFNcOoup8Xwyh5barC58Wb88NsZf7cmAzDMCpuHFhEREREpOxozqyIiIiI+C2FWRERERHxWwqzIiIiIuK3FGZFRERExG8pzIqIiIiI31KYFRERERG/pTArIiIiIn5LYVZERERE/JbCrIhIFWUymVi4cKGvuyEiclEUZkVEfOCuu+7CZDIV2vr27evrromI+JUAX3dARKSq6tu3L++9955bm81m81FvRET8k0ZmRUR8xGazERER4bbVrFkTcE4BePPNN+nXrx/BwcE0bdqUTz/91O34hIQE/vznPxMcHEzt2rUZO3YsZ8+eddtn1qxZtGvXDpvNRmRkJOPHj3d7/fjx49x8882EhITQokULvvzyy/J90yIiZUxhVkSkknr66acZPHgwmzdvZvjw4dxxxx1s374dgLS0NOLi4qhZsyY///wz8+fP59tvv3ULq2+++Sbjxo1j7NixJCQk8OWXX9K8eXO3a0ydOpXbb7+d3377jf79+zN8+HBOnjxZoe9TRORimAzDMHzdCRGRquauu+7iww8/JCgoyK39b3/7G3/7298wmUzcd999vPnmm67XrrrqKq644greeOMNZs6cyRNPPMHBgwepVq0aAIsWLWLgwIEcOXKE+vXr07BhQ0aPHs2zzz7rsQ8mk4mnnnqKv//974AzIIeGhvLNN99o7q6I+A3NmRUR8ZGePXu6hVWAWrVquR7Hxsa6vRYbG8umTZsA2L59Ox07dnQFWYBrrrkGh8PBzp07MZlMHDlyhOuvv77IPnTo0MH1uFq1aoSHh3P06NHSviURkQqnMCsi4iPVqlUr9Gv/shIcHFys/axWq9tzk8mEw+Eojy6JiJQLzZkVEamkfvrpp0LP27RpA0CbNm3YvHkzaWlprtdXr16N2WymVatWhIWFER0dzfLlyyu0zyIiFU0jsyIiPpKVlUVSUpJbW0BAAHXq1AFg/vz5dOnShWuvvZaPPvqI9evX8+677wIwfPhwJk+ezKhRo5gyZQrHjh3jwQcfZMSIEdSvXx+AKVOmcN9991GvXj369evHmTNnWL16NQ8++GDFvlERkXKkMCsi4iOLFy8mMjLSra1Vq1bs2LEDcK408PHHH/PAAw8QGRnJ//73P9q2bQtASEgIS5Ys4eGHH+bKK68kJCSEwYMH8/LLL7vONWrUKDIzM3nllVd4/PHHqVOnDrfeemvFvUERkQqg1QxERCohk8nEggULGDRokK+7IiJSqWnOrIiIiIj4LYVZEREREfFbmjMrIlIJaQaYiEjxaGRWRERERPyWwqyIiIiI+C2FWRERERHxWwqzIiIiIuK3FGZFRERExG8pzIqIiIiI31KYFRERERG/pTArIiIiIn7r/wHMJEx+EGewTwAAAABJRU5ErkJggg==\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "mobilenet_loss, mobilenet_accuracy = mobilenet_model.evaluate(test_dataset)\n", "print(\"Loss :\", mobilenet_loss)\n", "print(\"Accuracy :\", mobilenet_accuracy)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "t4lTvsrGVLZW", "outputId": "cec126da-e891-47fc-dae9-2f0205c3e51b" }, "execution_count": 151, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 51ms/step - accuracy: 0.5631 - loss: 0.7158\n", "Loss : 0.7157962918281555\n", "Accuracy : 0.5630720257759094\n" ] } ] }, { "cell_type": "code", "source": [ "y_true = []\n", "\n", "y_pred = []\n", "\n", "for images, labels in test_dataset:\n", "\n", " pred = mobilenet_model.predict(images, verbose=0)\n", "\n", " pred = (pred > 0.5).astype(int)\n", "\n", " y_true.extend(labels.numpy())\n", "\n", " y_pred.extend(pred.flatten())" ], "metadata": { "id": "YJwm_CR6VLWa" }, "execution_count": 152, "outputs": [] }, { "cell_type": "code", "source": [ "cm = confusion_matrix(y_true, y_pred)\n", "\n", "plt.figure(figsize=(6,5))\n", "\n", "sns.heatmap(\n", "\n", " cm,\n", "\n", " annot=True,\n", "\n", " fmt=\"d\",\n", "\n", " cmap=\"Blues\",\n", "\n", " xticklabels=[\"Organic\",\"Recyclable\"],\n", "\n", " yticklabels=[\"Organic\",\"Recyclable\"]\n", "\n", ")\n", "\n", "plt.xlabel(\"Predicted\")\n", "\n", "plt.ylabel(\"Actual\")\n", "\n", "plt.title(\"MobileNetV2 Confusion Matrix\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "leMe5P5rVLTi", "outputId": "a5044580-d911-478f-aa8a-a17cd8d432fc" }, "execution_count": 153, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "print(\n", "\n", " classification_report(\n", "\n", " y_true,\n", "\n", " y_pred,\n", "\n", " target_names=[\n", " \"Organic\",\n", " \"Recyclable\"\n", " ]\n", "\n", " )\n", "\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qLqCPkLdVLQ0", "outputId": "373df29e-f132-427c-f7a5-f0506b6fa4de" }, "execution_count": 154, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " Organic 0.76 0.32 0.45 1401\n", " Recyclable 0.50 0.87 0.64 1112\n", "\n", " accuracy 0.56 2513\n", " macro avg 0.63 0.59 0.54 2513\n", "weighted avg 0.64 0.56 0.53 2513\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "mobilenet_acc = accuracy_score(\n", " y_true,\n", " y_pred\n", ")\n", "\n", "mobilenet_precision = precision_score(\n", " y_true,\n", " y_pred\n", ")\n", "\n", "mobilenet_recall = recall_score(\n", " y_true,\n", " y_pred\n", ")\n", "\n", "mobilenet_f1 = f1_score(\n", " y_true,\n", " y_pred\n", ")\n", "\n", "print(\"Accuracy :\", round(mobilenet_acc,4))\n", "print(\"Precision :\", round(mobilenet_precision,4))\n", "print(\"Recall :\", round(mobilenet_recall,4))\n", "print(\"F1 Score :\", round(mobilenet_f1,4))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "D6kZdUWKVLNz", "outputId": "1c505003-e927-40c8-fefb-316565a6d8c1" }, "execution_count": 155, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy : 0.5631\n", "Precision : 0.5036\n", "Recall : 0.8705\n", "F1 Score : 0.6381\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Model 3 – ResNet50" ], "metadata": { "id": "cZpdbyFsXA6a" } }, { "cell_type": "code", "source": [ "from tensorflow.keras.applications import ResNet50\n", "from tensorflow.keras.models import Model\n", "from tensorflow.keras.layers import (\n", " GlobalAveragePooling2D,\n", " Dense,\n", " Dropout,\n", " BatchNormalization\n", ")" ], "metadata": { "id": "pWtOwhFLVLKq" }, "execution_count": 156, "outputs": [] }, { "cell_type": "code", "source": [ "resnet_base = ResNet50(\n", "\n", " weights='imagenet',\n", "\n", " include_top=False,\n", "\n", " input_shape=(224,224,3)\n", "\n", ")\n", "\n", "resnet_base.summary()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "VkLzFFftVLH_", "outputId": "276c4ed9-91b8-4ec3-fdb5-8d7dbace684f" }, "execution_count": 157, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1mModel: \"resnet50\"\u001b[0m\n" ], "text/html": [ "
Model: \"resnet50\"\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n", "│ input_layer_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ - │\n", "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv1_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m230\u001b[0m, \u001b[38;5;34m230\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ input_layer_7[\u001b[38;5;34m0\u001b[0m]… │\n", "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv1_conv (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m9,472\u001b[0m │ conv1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv1_conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ pool1_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m114\u001b[0m, \u001b[38;5;34m114\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv1_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ pool1_pool │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ pool1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m4,160\u001b[0m │ pool1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block1_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block1_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block1_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ pool1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block1_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block1_0_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block1_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_0_b… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block1_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,448\u001b[0m │ conv2_block1_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block2_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block2_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block2_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block2_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block2_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block2_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,448\u001b[0m │ conv2_block2_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block3_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block3_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block3_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block3_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block3_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block3_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m32,896\u001b[0m │ conv2_block3_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block1_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block1_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block1_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m131,584\u001b[0m │ conv2_block3_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block1_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block1_0_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block1_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_0_b… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block1_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block1_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block2_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block2_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block2_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block2_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block2_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block2_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block2_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block3_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block3_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block3_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block3_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block3_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block3_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block3_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block4_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block4_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block4_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block4_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block4_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block4_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m131,328\u001b[0m │ conv3_block4_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block1_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block1_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block1_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m525,312\u001b[0m │ conv3_block4_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block1_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block1_0_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block1_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_0_b… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block1_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block1_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block2_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block2_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block2_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block2_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block2_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block2_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block2_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block3_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block3_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block3_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block3_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block3_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block3_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block3_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block4_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block4_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block4_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block4_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block4_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block4_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block4_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block5_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block5_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block5_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block5_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block5_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block5_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block5_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block6_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block6_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block6_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block6_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block6_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block6_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m524,800\u001b[0m │ conv4_block6_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block1_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block1_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block1_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m2,099,200\u001b[0m │ conv4_block6_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block1_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block1_0_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block1_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_0_b… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ conv5_block1_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,049,088\u001b[0m │ conv5_block1_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block2_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block2_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block2_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block2_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block2_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ conv5_block2_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,049,088\u001b[0m │ conv5_block2_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block3_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block3_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block3_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block3_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block3_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ conv5_block3_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n" ], "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
              "┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_7       │ (None, 224, 224,  │          0 │ -                 │\n",
              "│ (InputLayer)        │ 3)                │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1_pad           │ (None, 230, 230,  │          0 │ input_layer_7[0]… │\n",
              "│ (ZeroPadding2D)     │ 3)                │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1_conv (Conv2D) │ (None, 112, 112,  │      9,472 │ conv1_pad[0][0]   │\n",
              "│                     │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1_bn            │ (None, 112, 112,  │        256 │ conv1_conv[0][0]  │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1_relu          │ (None, 112, 112,  │          0 │ conv1_bn[0][0]    │\n",
              "│ (Activation)        │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ pool1_pad           │ (None, 114, 114,  │          0 │ conv1_relu[0][0]  │\n",
              "│ (ZeroPadding2D)     │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ pool1_pool          │ (None, 56, 56,    │          0 │ pool1_pad[0][0]   │\n",
              "│ (MaxPooling2D)      │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_1_conv │ (None, 56, 56,    │      4,160 │ pool1_pool[0][0]  │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_1_bn   │ (None, 56, 56,    │        256 │ conv2_block1_1_c… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_1_relu │ (None, 56, 56,    │          0 │ conv2_block1_1_b… │\n",
              "│ (Activation)        │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_2_conv │ (None, 56, 56,    │     36,928 │ conv2_block1_1_r… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_2_bn   │ (None, 56, 56,    │        256 │ conv2_block1_2_c… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_2_relu │ (None, 56, 56,    │          0 │ conv2_block1_2_b… │\n",
              "│ (Activation)        │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_0_conv │ (None, 56, 56,    │     16,640 │ pool1_pool[0][0]  │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_3_conv │ (None, 56, 56,    │     16,640 │ conv2_block1_2_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_0_bn   │ (None, 56, 56,    │      1,024 │ conv2_block1_0_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_3_bn   │ (None, 56, 56,    │      1,024 │ conv2_block1_3_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_add    │ (None, 56, 56,    │          0 │ conv2_block1_0_b… │\n",
              "│ (Add)               │ 256)              │            │ conv2_block1_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_out    │ (None, 56, 56,    │          0 │ conv2_block1_add… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_1_conv │ (None, 56, 56,    │     16,448 │ conv2_block1_out… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_1_bn   │ (None, 56, 56,    │        256 │ conv2_block2_1_c… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_1_relu │ (None, 56, 56,    │          0 │ conv2_block2_1_b… │\n",
              "│ (Activation)        │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_2_conv │ (None, 56, 56,    │     36,928 │ conv2_block2_1_r… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_2_bn   │ (None, 56, 56,    │        256 │ conv2_block2_2_c… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_2_relu │ (None, 56, 56,    │          0 │ conv2_block2_2_b… │\n",
              "│ (Activation)        │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_3_conv │ (None, 56, 56,    │     16,640 │ conv2_block2_2_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_3_bn   │ (None, 56, 56,    │      1,024 │ conv2_block2_3_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_add    │ (None, 56, 56,    │          0 │ conv2_block1_out… │\n",
              "│ (Add)               │ 256)              │            │ conv2_block2_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_out    │ (None, 56, 56,    │          0 │ conv2_block2_add… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_1_conv │ (None, 56, 56,    │     16,448 │ conv2_block2_out… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_1_bn   │ (None, 56, 56,    │        256 │ conv2_block3_1_c… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_1_relu │ (None, 56, 56,    │          0 │ conv2_block3_1_b… │\n",
              "│ (Activation)        │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_2_conv │ (None, 56, 56,    │     36,928 │ conv2_block3_1_r… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_2_bn   │ (None, 56, 56,    │        256 │ conv2_block3_2_c… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_2_relu │ (None, 56, 56,    │          0 │ conv2_block3_2_b… │\n",
              "│ (Activation)        │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_3_conv │ (None, 56, 56,    │     16,640 │ conv2_block3_2_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_3_bn   │ (None, 56, 56,    │      1,024 │ conv2_block3_3_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_add    │ (None, 56, 56,    │          0 │ conv2_block2_out… │\n",
              "│ (Add)               │ 256)              │            │ conv2_block3_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_out    │ (None, 56, 56,    │          0 │ conv2_block3_add… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_1_conv │ (None, 28, 28,    │     32,896 │ conv2_block3_out… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_1_bn   │ (None, 28, 28,    │        512 │ conv3_block1_1_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_1_relu │ (None, 28, 28,    │          0 │ conv3_block1_1_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block1_1_r… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_2_bn   │ (None, 28, 28,    │        512 │ conv3_block1_2_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_2_relu │ (None, 28, 28,    │          0 │ conv3_block1_2_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_0_conv │ (None, 28, 28,    │    131,584 │ conv2_block3_out… │\n",
              "│ (Conv2D)            │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block1_2_r… │\n",
              "│ (Conv2D)            │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_0_bn   │ (None, 28, 28,    │      2,048 │ conv3_block1_0_c… │\n",
              "│ (BatchNormalizatio… │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block1_3_c… │\n",
              "│ (BatchNormalizatio… │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_add    │ (None, 28, 28,    │          0 │ conv3_block1_0_b… │\n",
              "│ (Add)               │ 512)              │            │ conv3_block1_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_out    │ (None, 28, 28,    │          0 │ conv3_block1_add… │\n",
              "│ (Activation)        │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_1_conv │ (None, 28, 28,    │     65,664 │ conv3_block1_out… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_1_bn   │ (None, 28, 28,    │        512 │ conv3_block2_1_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_1_relu │ (None, 28, 28,    │          0 │ conv3_block2_1_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block2_1_r… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_2_bn   │ (None, 28, 28,    │        512 │ conv3_block2_2_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_2_relu │ (None, 28, 28,    │          0 │ conv3_block2_2_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block2_2_r… │\n",
              "│ (Conv2D)            │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block2_3_c… │\n",
              "│ (BatchNormalizatio… │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_add    │ (None, 28, 28,    │          0 │ conv3_block1_out… │\n",
              "│ (Add)               │ 512)              │            │ conv3_block2_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_out    │ (None, 28, 28,    │          0 │ conv3_block2_add… │\n",
              "│ (Activation)        │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_1_conv │ (None, 28, 28,    │     65,664 │ conv3_block2_out… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_1_bn   │ (None, 28, 28,    │        512 │ conv3_block3_1_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_1_relu │ (None, 28, 28,    │          0 │ conv3_block3_1_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block3_1_r… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_2_bn   │ (None, 28, 28,    │        512 │ conv3_block3_2_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_2_relu │ (None, 28, 28,    │          0 │ conv3_block3_2_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block3_2_r… │\n",
              "│ (Conv2D)            │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block3_3_c… │\n",
              "│ (BatchNormalizatio… │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_add    │ (None, 28, 28,    │          0 │ conv3_block2_out… │\n",
              "│ (Add)               │ 512)              │            │ conv3_block3_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_out    │ (None, 28, 28,    │          0 │ conv3_block3_add… │\n",
              "│ (Activation)        │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_1_conv │ (None, 28, 28,    │     65,664 │ conv3_block3_out… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_1_bn   │ (None, 28, 28,    │        512 │ conv3_block4_1_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_1_relu │ (None, 28, 28,    │          0 │ conv3_block4_1_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block4_1_r… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_2_bn   │ (None, 28, 28,    │        512 │ conv3_block4_2_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_2_relu │ (None, 28, 28,    │          0 │ conv3_block4_2_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block4_2_r… │\n",
              "│ (Conv2D)            │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block4_3_c… │\n",
              "│ (BatchNormalizatio… │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_add    │ (None, 28, 28,    │          0 │ conv3_block3_out… │\n",
              "│ (Add)               │ 512)              │            │ conv3_block4_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_out    │ (None, 28, 28,    │          0 │ conv3_block4_add… │\n",
              "│ (Activation)        │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_1_conv │ (None, 14, 14,    │    131,328 │ conv3_block4_out… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block1_1_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_1_relu │ (None, 14, 14,    │          0 │ conv4_block1_1_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block1_1_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block1_2_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_2_relu │ (None, 14, 14,    │          0 │ conv4_block1_2_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_0_conv │ (None, 14, 14,    │    525,312 │ conv3_block4_out… │\n",
              "│ (Conv2D)            │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block1_2_r… │\n",
              "│ (Conv2D)            │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_0_bn   │ (None, 14, 14,    │      4,096 │ conv4_block1_0_c… │\n",
              "│ (BatchNormalizatio… │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block1_3_c… │\n",
              "│ (BatchNormalizatio… │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_add    │ (None, 14, 14,    │          0 │ conv4_block1_0_b… │\n",
              "│ (Add)               │ 1024)             │            │ conv4_block1_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_out    │ (None, 14, 14,    │          0 │ conv4_block1_add… │\n",
              "│ (Activation)        │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block1_out… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block2_1_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_1_relu │ (None, 14, 14,    │          0 │ conv4_block2_1_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block2_1_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block2_2_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_2_relu │ (None, 14, 14,    │          0 │ conv4_block2_2_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block2_2_r… │\n",
              "│ (Conv2D)            │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block2_3_c… │\n",
              "│ (BatchNormalizatio… │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_add    │ (None, 14, 14,    │          0 │ conv4_block1_out… │\n",
              "│ (Add)               │ 1024)             │            │ conv4_block2_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_out    │ (None, 14, 14,    │          0 │ conv4_block2_add… │\n",
              "│ (Activation)        │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block2_out… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block3_1_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_1_relu │ (None, 14, 14,    │          0 │ conv4_block3_1_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block3_1_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block3_2_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_2_relu │ (None, 14, 14,    │          0 │ conv4_block3_2_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block3_2_r… │\n",
              "│ (Conv2D)            │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block3_3_c… │\n",
              "│ (BatchNormalizatio… │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_add    │ (None, 14, 14,    │          0 │ conv4_block2_out… │\n",
              "│ (Add)               │ 1024)             │            │ conv4_block3_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_out    │ (None, 14, 14,    │          0 │ conv4_block3_add… │\n",
              "│ (Activation)        │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block3_out… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block4_1_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_1_relu │ (None, 14, 14,    │          0 │ conv4_block4_1_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block4_1_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block4_2_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_2_relu │ (None, 14, 14,    │          0 │ conv4_block4_2_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block4_2_r… │\n",
              "│ (Conv2D)            │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block4_3_c… │\n",
              "│ (BatchNormalizatio… │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_add    │ (None, 14, 14,    │          0 │ conv4_block3_out… │\n",
              "│ (Add)               │ 1024)             │            │ conv4_block4_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_out    │ (None, 14, 14,    │          0 │ conv4_block4_add… │\n",
              "│ (Activation)        │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block4_out… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block5_1_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_1_relu │ (None, 14, 14,    │          0 │ conv4_block5_1_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block5_1_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block5_2_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_2_relu │ (None, 14, 14,    │          0 │ conv4_block5_2_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block5_2_r… │\n",
              "│ (Conv2D)            │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block5_3_c… │\n",
              "│ (BatchNormalizatio… │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_add    │ (None, 14, 14,    │          0 │ conv4_block4_out… │\n",
              "│ (Add)               │ 1024)             │            │ conv4_block5_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_out    │ (None, 14, 14,    │          0 │ conv4_block5_add… │\n",
              "│ (Activation)        │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block5_out… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block6_1_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_1_relu │ (None, 14, 14,    │          0 │ conv4_block6_1_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block6_1_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block6_2_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_2_relu │ (None, 14, 14,    │          0 │ conv4_block6_2_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block6_2_r… │\n",
              "│ (Conv2D)            │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block6_3_c… │\n",
              "│ (BatchNormalizatio… │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_add    │ (None, 14, 14,    │          0 │ conv4_block5_out… │\n",
              "│ (Add)               │ 1024)             │            │ conv4_block6_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_out    │ (None, 14, 14,    │          0 │ conv4_block6_add… │\n",
              "│ (Activation)        │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_1_conv │ (None, 7, 7, 512) │    524,800 │ conv4_block6_out… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_1_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block1_1_c… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_1_relu │ (None, 7, 7, 512) │          0 │ conv5_block1_1_b… │\n",
              "│ (Activation)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_2_conv │ (None, 7, 7, 512) │  2,359,808 │ conv5_block1_1_r… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_2_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block1_2_c… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_2_relu │ (None, 7, 7, 512) │          0 │ conv5_block1_2_b… │\n",
              "│ (Activation)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_0_conv │ (None, 7, 7,      │  2,099,200 │ conv4_block6_out… │\n",
              "│ (Conv2D)            │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_3_conv │ (None, 7, 7,      │  1,050,624 │ conv5_block1_2_r… │\n",
              "│ (Conv2D)            │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_0_bn   │ (None, 7, 7,      │      8,192 │ conv5_block1_0_c… │\n",
              "│ (BatchNormalizatio… │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_3_bn   │ (None, 7, 7,      │      8,192 │ conv5_block1_3_c… │\n",
              "│ (BatchNormalizatio… │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_add    │ (None, 7, 7,      │          0 │ conv5_block1_0_b… │\n",
              "│ (Add)               │ 2048)             │            │ conv5_block1_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_out    │ (None, 7, 7,      │          0 │ conv5_block1_add… │\n",
              "│ (Activation)        │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_1_conv │ (None, 7, 7, 512) │  1,049,088 │ conv5_block1_out… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_1_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block2_1_c… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_1_relu │ (None, 7, 7, 512) │          0 │ conv5_block2_1_b… │\n",
              "│ (Activation)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_2_conv │ (None, 7, 7, 512) │  2,359,808 │ conv5_block2_1_r… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_2_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block2_2_c… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_2_relu │ (None, 7, 7, 512) │          0 │ conv5_block2_2_b… │\n",
              "│ (Activation)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_3_conv │ (None, 7, 7,      │  1,050,624 │ conv5_block2_2_r… │\n",
              "│ (Conv2D)            │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_3_bn   │ (None, 7, 7,      │      8,192 │ conv5_block2_3_c… │\n",
              "│ (BatchNormalizatio… │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_add    │ (None, 7, 7,      │          0 │ conv5_block1_out… │\n",
              "│ (Add)               │ 2048)             │            │ conv5_block2_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_out    │ (None, 7, 7,      │          0 │ conv5_block2_add… │\n",
              "│ (Activation)        │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_1_conv │ (None, 7, 7, 512) │  1,049,088 │ conv5_block2_out… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_1_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block3_1_c… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_1_relu │ (None, 7, 7, 512) │          0 │ conv5_block3_1_b… │\n",
              "│ (Activation)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_2_conv │ (None, 7, 7, 512) │  2,359,808 │ conv5_block3_1_r… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_2_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block3_2_c… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_2_relu │ (None, 7, 7, 512) │          0 │ conv5_block3_2_b… │\n",
              "│ (Activation)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_3_conv │ (None, 7, 7,      │  1,050,624 │ conv5_block3_2_r… │\n",
              "│ (Conv2D)            │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_3_bn   │ (None, 7, 7,      │      8,192 │ conv5_block3_3_c… │\n",
              "│ (BatchNormalizatio… │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_add    │ (None, 7, 7,      │          0 │ conv5_block2_out… │\n",
              "│ (Add)               │ 2048)             │            │ conv5_block3_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_out    │ (None, 7, 7,      │          0 │ conv5_block3_add… │\n",
              "│ (Activation)        │ 2048)             │            │                   │\n",
              "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m23,587,712\u001b[0m (89.98 MB)\n" ], "text/html": [ "
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              "
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\n" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "for layer in resnet_base.layers:\n", " layer.trainable = False" ], "metadata": { "id": "kJ0V-g-PVLFM" }, "execution_count": 158, "outputs": [] }, { "cell_type": "code", "source": [ "x = resnet_base.output\n", "\n", "x = GlobalAveragePooling2D()(x)\n", "\n", "x = BatchNormalization()(x)\n", "\n", "x = Dense(\n", " 512,\n", " activation='relu'\n", ")(x)\n", "\n", "x = Dropout(0.5)(x)\n", "\n", "x = Dense(\n", " 256,\n", " activation='relu'\n", ")(x)\n", "\n", "x = Dropout(0.3)(x)\n", "\n", "output = Dense(\n", " 1,\n", " activation='sigmoid'\n", ")(x)\n", "\n", "resnet_model = Model(\n", "\n", " inputs=resnet_base.input,\n", "\n", " outputs=output\n", "\n", ")\n", "\n", "resnet_model.summary()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "bVDrb4n8VLCr", "outputId": "b4ee9ab4-8faa-4d32-b9a1-a287b495da0e" }, "execution_count": 159, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1mModel: \"functional_7\"\u001b[0m\n" ], "text/html": [ "
Model: \"functional_7\"\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n", "│ input_layer_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ - │\n", "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv1_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m230\u001b[0m, \u001b[38;5;34m230\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ input_layer_7[\u001b[38;5;34m0\u001b[0m]… │\n", "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv1_conv (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m9,472\u001b[0m │ conv1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv1_conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ pool1_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m114\u001b[0m, \u001b[38;5;34m114\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv1_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ pool1_pool │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ pool1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m4,160\u001b[0m │ pool1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block1_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block1_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block1_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ pool1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block1_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block1_0_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block1_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_0_b… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block1_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,448\u001b[0m │ conv2_block1_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block2_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block2_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block2_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block2_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block2_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block2_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,448\u001b[0m │ conv2_block2_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block3_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block3_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block3_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block3_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block3_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block3_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv2_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m32,896\u001b[0m │ conv2_block3_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block1_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block1_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block1_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m131,584\u001b[0m │ conv2_block3_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block1_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block1_0_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block1_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_0_b… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block1_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block1_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block2_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block2_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block2_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block2_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block2_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block2_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block2_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block3_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block3_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block3_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block3_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block3_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block3_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block3_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block4_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block4_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block4_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block4_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block4_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block4_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv3_block4_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m131,328\u001b[0m │ conv3_block4_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block1_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block1_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block1_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m525,312\u001b[0m │ conv3_block4_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block1_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block1_0_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block1_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_0_b… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block1_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block1_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block2_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block2_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block2_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block2_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block2_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block2_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block2_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block3_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block3_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block3_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block3_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block3_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block3_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block3_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block4_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block4_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block4_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block4_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block4_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block4_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block4_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block4_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block5_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block5_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block5_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block5_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block5_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block5_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block5_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block5_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block6_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block6_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block6_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block6_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block6_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block6_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv4_block6_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m524,800\u001b[0m │ conv4_block6_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block1_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block1_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block1_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m2,099,200\u001b[0m │ conv4_block6_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block1_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block1_0_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block1_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_0_b… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ conv5_block1_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,049,088\u001b[0m │ conv5_block1_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block2_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block2_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block2_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block2_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block2_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ conv5_block2_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,049,088\u001b[0m │ conv5_block2_out… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block3_1_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_1_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block3_1_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block3_2_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_2_b… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block3_2_r… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block3_3_c… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_out… │\n", "│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ conv5_block3_3_b… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ conv5_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_add… │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_out… │\n", "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m8,192\u001b[0m │ global_average_p… │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ dense_21 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,049,088\u001b[0m │ batch_normalizat… │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ dropout_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ dense_22 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m131,328\u001b[0m │ dropout_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ dropout_15 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ dense_23 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m257\u001b[0m │ dropout_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n" ], "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
              "┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_7       │ (None, 224, 224,  │          0 │ -                 │\n",
              "│ (InputLayer)        │ 3)                │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1_pad           │ (None, 230, 230,  │          0 │ input_layer_7[0]… │\n",
              "│ (ZeroPadding2D)     │ 3)                │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1_conv (Conv2D) │ (None, 112, 112,  │      9,472 │ conv1_pad[0][0]   │\n",
              "│                     │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1_bn            │ (None, 112, 112,  │        256 │ conv1_conv[0][0]  │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1_relu          │ (None, 112, 112,  │          0 │ conv1_bn[0][0]    │\n",
              "│ (Activation)        │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ pool1_pad           │ (None, 114, 114,  │          0 │ conv1_relu[0][0]  │\n",
              "│ (ZeroPadding2D)     │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ pool1_pool          │ (None, 56, 56,    │          0 │ pool1_pad[0][0]   │\n",
              "│ (MaxPooling2D)      │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_1_conv │ (None, 56, 56,    │      4,160 │ pool1_pool[0][0]  │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_1_bn   │ (None, 56, 56,    │        256 │ conv2_block1_1_c… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_1_relu │ (None, 56, 56,    │          0 │ conv2_block1_1_b… │\n",
              "│ (Activation)        │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_2_conv │ (None, 56, 56,    │     36,928 │ conv2_block1_1_r… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_2_bn   │ (None, 56, 56,    │        256 │ conv2_block1_2_c… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_2_relu │ (None, 56, 56,    │          0 │ conv2_block1_2_b… │\n",
              "│ (Activation)        │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_0_conv │ (None, 56, 56,    │     16,640 │ pool1_pool[0][0]  │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_3_conv │ (None, 56, 56,    │     16,640 │ conv2_block1_2_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_0_bn   │ (None, 56, 56,    │      1,024 │ conv2_block1_0_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_3_bn   │ (None, 56, 56,    │      1,024 │ conv2_block1_3_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_add    │ (None, 56, 56,    │          0 │ conv2_block1_0_b… │\n",
              "│ (Add)               │ 256)              │            │ conv2_block1_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block1_out    │ (None, 56, 56,    │          0 │ conv2_block1_add… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_1_conv │ (None, 56, 56,    │     16,448 │ conv2_block1_out… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_1_bn   │ (None, 56, 56,    │        256 │ conv2_block2_1_c… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_1_relu │ (None, 56, 56,    │          0 │ conv2_block2_1_b… │\n",
              "│ (Activation)        │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_2_conv │ (None, 56, 56,    │     36,928 │ conv2_block2_1_r… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_2_bn   │ (None, 56, 56,    │        256 │ conv2_block2_2_c… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_2_relu │ (None, 56, 56,    │          0 │ conv2_block2_2_b… │\n",
              "│ (Activation)        │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_3_conv │ (None, 56, 56,    │     16,640 │ conv2_block2_2_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_3_bn   │ (None, 56, 56,    │      1,024 │ conv2_block2_3_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_add    │ (None, 56, 56,    │          0 │ conv2_block1_out… │\n",
              "│ (Add)               │ 256)              │            │ conv2_block2_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block2_out    │ (None, 56, 56,    │          0 │ conv2_block2_add… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_1_conv │ (None, 56, 56,    │     16,448 │ conv2_block2_out… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_1_bn   │ (None, 56, 56,    │        256 │ conv2_block3_1_c… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_1_relu │ (None, 56, 56,    │          0 │ conv2_block3_1_b… │\n",
              "│ (Activation)        │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_2_conv │ (None, 56, 56,    │     36,928 │ conv2_block3_1_r… │\n",
              "│ (Conv2D)            │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_2_bn   │ (None, 56, 56,    │        256 │ conv2_block3_2_c… │\n",
              "│ (BatchNormalizatio… │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_2_relu │ (None, 56, 56,    │          0 │ conv2_block3_2_b… │\n",
              "│ (Activation)        │ 64)               │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_3_conv │ (None, 56, 56,    │     16,640 │ conv2_block3_2_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_3_bn   │ (None, 56, 56,    │      1,024 │ conv2_block3_3_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_add    │ (None, 56, 56,    │          0 │ conv2_block2_out… │\n",
              "│ (Add)               │ 256)              │            │ conv2_block3_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv2_block3_out    │ (None, 56, 56,    │          0 │ conv2_block3_add… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_1_conv │ (None, 28, 28,    │     32,896 │ conv2_block3_out… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_1_bn   │ (None, 28, 28,    │        512 │ conv3_block1_1_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_1_relu │ (None, 28, 28,    │          0 │ conv3_block1_1_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block1_1_r… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_2_bn   │ (None, 28, 28,    │        512 │ conv3_block1_2_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_2_relu │ (None, 28, 28,    │          0 │ conv3_block1_2_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_0_conv │ (None, 28, 28,    │    131,584 │ conv2_block3_out… │\n",
              "│ (Conv2D)            │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block1_2_r… │\n",
              "│ (Conv2D)            │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_0_bn   │ (None, 28, 28,    │      2,048 │ conv3_block1_0_c… │\n",
              "│ (BatchNormalizatio… │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block1_3_c… │\n",
              "│ (BatchNormalizatio… │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_add    │ (None, 28, 28,    │          0 │ conv3_block1_0_b… │\n",
              "│ (Add)               │ 512)              │            │ conv3_block1_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block1_out    │ (None, 28, 28,    │          0 │ conv3_block1_add… │\n",
              "│ (Activation)        │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_1_conv │ (None, 28, 28,    │     65,664 │ conv3_block1_out… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_1_bn   │ (None, 28, 28,    │        512 │ conv3_block2_1_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_1_relu │ (None, 28, 28,    │          0 │ conv3_block2_1_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block2_1_r… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_2_bn   │ (None, 28, 28,    │        512 │ conv3_block2_2_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_2_relu │ (None, 28, 28,    │          0 │ conv3_block2_2_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block2_2_r… │\n",
              "│ (Conv2D)            │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block2_3_c… │\n",
              "│ (BatchNormalizatio… │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_add    │ (None, 28, 28,    │          0 │ conv3_block1_out… │\n",
              "│ (Add)               │ 512)              │            │ conv3_block2_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block2_out    │ (None, 28, 28,    │          0 │ conv3_block2_add… │\n",
              "│ (Activation)        │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_1_conv │ (None, 28, 28,    │     65,664 │ conv3_block2_out… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_1_bn   │ (None, 28, 28,    │        512 │ conv3_block3_1_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_1_relu │ (None, 28, 28,    │          0 │ conv3_block3_1_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block3_1_r… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_2_bn   │ (None, 28, 28,    │        512 │ conv3_block3_2_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_2_relu │ (None, 28, 28,    │          0 │ conv3_block3_2_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block3_2_r… │\n",
              "│ (Conv2D)            │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block3_3_c… │\n",
              "│ (BatchNormalizatio… │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_add    │ (None, 28, 28,    │          0 │ conv3_block2_out… │\n",
              "│ (Add)               │ 512)              │            │ conv3_block3_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block3_out    │ (None, 28, 28,    │          0 │ conv3_block3_add… │\n",
              "│ (Activation)        │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_1_conv │ (None, 28, 28,    │     65,664 │ conv3_block3_out… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_1_bn   │ (None, 28, 28,    │        512 │ conv3_block4_1_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_1_relu │ (None, 28, 28,    │          0 │ conv3_block4_1_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block4_1_r… │\n",
              "│ (Conv2D)            │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_2_bn   │ (None, 28, 28,    │        512 │ conv3_block4_2_c… │\n",
              "│ (BatchNormalizatio… │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_2_relu │ (None, 28, 28,    │          0 │ conv3_block4_2_b… │\n",
              "│ (Activation)        │ 128)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block4_2_r… │\n",
              "│ (Conv2D)            │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block4_3_c… │\n",
              "│ (BatchNormalizatio… │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_add    │ (None, 28, 28,    │          0 │ conv3_block3_out… │\n",
              "│ (Add)               │ 512)              │            │ conv3_block4_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv3_block4_out    │ (None, 28, 28,    │          0 │ conv3_block4_add… │\n",
              "│ (Activation)        │ 512)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_1_conv │ (None, 14, 14,    │    131,328 │ conv3_block4_out… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block1_1_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_1_relu │ (None, 14, 14,    │          0 │ conv4_block1_1_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block1_1_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block1_2_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_2_relu │ (None, 14, 14,    │          0 │ conv4_block1_2_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_0_conv │ (None, 14, 14,    │    525,312 │ conv3_block4_out… │\n",
              "│ (Conv2D)            │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block1_2_r… │\n",
              "│ (Conv2D)            │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_0_bn   │ (None, 14, 14,    │      4,096 │ conv4_block1_0_c… │\n",
              "│ (BatchNormalizatio… │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block1_3_c… │\n",
              "│ (BatchNormalizatio… │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_add    │ (None, 14, 14,    │          0 │ conv4_block1_0_b… │\n",
              "│ (Add)               │ 1024)             │            │ conv4_block1_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block1_out    │ (None, 14, 14,    │          0 │ conv4_block1_add… │\n",
              "│ (Activation)        │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block1_out… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block2_1_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_1_relu │ (None, 14, 14,    │          0 │ conv4_block2_1_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block2_1_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block2_2_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_2_relu │ (None, 14, 14,    │          0 │ conv4_block2_2_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block2_2_r… │\n",
              "│ (Conv2D)            │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block2_3_c… │\n",
              "│ (BatchNormalizatio… │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_add    │ (None, 14, 14,    │          0 │ conv4_block1_out… │\n",
              "│ (Add)               │ 1024)             │            │ conv4_block2_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block2_out    │ (None, 14, 14,    │          0 │ conv4_block2_add… │\n",
              "│ (Activation)        │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block2_out… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block3_1_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_1_relu │ (None, 14, 14,    │          0 │ conv4_block3_1_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block3_1_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block3_2_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_2_relu │ (None, 14, 14,    │          0 │ conv4_block3_2_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block3_2_r… │\n",
              "│ (Conv2D)            │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block3_3_c… │\n",
              "│ (BatchNormalizatio… │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_add    │ (None, 14, 14,    │          0 │ conv4_block2_out… │\n",
              "│ (Add)               │ 1024)             │            │ conv4_block3_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block3_out    │ (None, 14, 14,    │          0 │ conv4_block3_add… │\n",
              "│ (Activation)        │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block3_out… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block4_1_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_1_relu │ (None, 14, 14,    │          0 │ conv4_block4_1_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block4_1_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block4_2_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_2_relu │ (None, 14, 14,    │          0 │ conv4_block4_2_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block4_2_r… │\n",
              "│ (Conv2D)            │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block4_3_c… │\n",
              "│ (BatchNormalizatio… │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_add    │ (None, 14, 14,    │          0 │ conv4_block3_out… │\n",
              "│ (Add)               │ 1024)             │            │ conv4_block4_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block4_out    │ (None, 14, 14,    │          0 │ conv4_block4_add… │\n",
              "│ (Activation)        │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block4_out… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block5_1_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_1_relu │ (None, 14, 14,    │          0 │ conv4_block5_1_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block5_1_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block5_2_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_2_relu │ (None, 14, 14,    │          0 │ conv4_block5_2_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block5_2_r… │\n",
              "│ (Conv2D)            │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block5_3_c… │\n",
              "│ (BatchNormalizatio… │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_add    │ (None, 14, 14,    │          0 │ conv4_block4_out… │\n",
              "│ (Add)               │ 1024)             │            │ conv4_block5_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block5_out    │ (None, 14, 14,    │          0 │ conv4_block5_add… │\n",
              "│ (Activation)        │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block5_out… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block6_1_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_1_relu │ (None, 14, 14,    │          0 │ conv4_block6_1_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block6_1_r… │\n",
              "│ (Conv2D)            │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block6_2_c… │\n",
              "│ (BatchNormalizatio… │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_2_relu │ (None, 14, 14,    │          0 │ conv4_block6_2_b… │\n",
              "│ (Activation)        │ 256)              │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block6_2_r… │\n",
              "│ (Conv2D)            │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block6_3_c… │\n",
              "│ (BatchNormalizatio… │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_add    │ (None, 14, 14,    │          0 │ conv4_block5_out… │\n",
              "│ (Add)               │ 1024)             │            │ conv4_block6_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv4_block6_out    │ (None, 14, 14,    │          0 │ conv4_block6_add… │\n",
              "│ (Activation)        │ 1024)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_1_conv │ (None, 7, 7, 512) │    524,800 │ conv4_block6_out… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_1_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block1_1_c… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_1_relu │ (None, 7, 7, 512) │          0 │ conv5_block1_1_b… │\n",
              "│ (Activation)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_2_conv │ (None, 7, 7, 512) │  2,359,808 │ conv5_block1_1_r… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_2_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block1_2_c… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_2_relu │ (None, 7, 7, 512) │          0 │ conv5_block1_2_b… │\n",
              "│ (Activation)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_0_conv │ (None, 7, 7,      │  2,099,200 │ conv4_block6_out… │\n",
              "│ (Conv2D)            │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_3_conv │ (None, 7, 7,      │  1,050,624 │ conv5_block1_2_r… │\n",
              "│ (Conv2D)            │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_0_bn   │ (None, 7, 7,      │      8,192 │ conv5_block1_0_c… │\n",
              "│ (BatchNormalizatio… │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_3_bn   │ (None, 7, 7,      │      8,192 │ conv5_block1_3_c… │\n",
              "│ (BatchNormalizatio… │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_add    │ (None, 7, 7,      │          0 │ conv5_block1_0_b… │\n",
              "│ (Add)               │ 2048)             │            │ conv5_block1_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block1_out    │ (None, 7, 7,      │          0 │ conv5_block1_add… │\n",
              "│ (Activation)        │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_1_conv │ (None, 7, 7, 512) │  1,049,088 │ conv5_block1_out… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_1_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block2_1_c… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_1_relu │ (None, 7, 7, 512) │          0 │ conv5_block2_1_b… │\n",
              "│ (Activation)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_2_conv │ (None, 7, 7, 512) │  2,359,808 │ conv5_block2_1_r… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_2_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block2_2_c… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_2_relu │ (None, 7, 7, 512) │          0 │ conv5_block2_2_b… │\n",
              "│ (Activation)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_3_conv │ (None, 7, 7,      │  1,050,624 │ conv5_block2_2_r… │\n",
              "│ (Conv2D)            │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_3_bn   │ (None, 7, 7,      │      8,192 │ conv5_block2_3_c… │\n",
              "│ (BatchNormalizatio… │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_add    │ (None, 7, 7,      │          0 │ conv5_block1_out… │\n",
              "│ (Add)               │ 2048)             │            │ conv5_block2_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block2_out    │ (None, 7, 7,      │          0 │ conv5_block2_add… │\n",
              "│ (Activation)        │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_1_conv │ (None, 7, 7, 512) │  1,049,088 │ conv5_block2_out… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_1_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block3_1_c… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_1_relu │ (None, 7, 7, 512) │          0 │ conv5_block3_1_b… │\n",
              "│ (Activation)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_2_conv │ (None, 7, 7, 512) │  2,359,808 │ conv5_block3_1_r… │\n",
              "│ (Conv2D)            │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_2_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block3_2_c… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_2_relu │ (None, 7, 7, 512) │          0 │ conv5_block3_2_b… │\n",
              "│ (Activation)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_3_conv │ (None, 7, 7,      │  1,050,624 │ conv5_block3_2_r… │\n",
              "│ (Conv2D)            │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_3_bn   │ (None, 7, 7,      │      8,192 │ conv5_block3_3_c… │\n",
              "│ (BatchNormalizatio… │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_add    │ (None, 7, 7,      │          0 │ conv5_block2_out… │\n",
              "│ (Add)               │ 2048)             │            │ conv5_block3_3_b… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv5_block3_out    │ (None, 7, 7,      │          0 │ conv5_block3_add… │\n",
              "│ (Activation)        │ 2048)             │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ global_average_poo… │ (None, 2048)      │          0 │ conv5_block3_out… │\n",
              "│ (GlobalAveragePool… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ batch_normalizatio… │ (None, 2048)      │      8,192 │ global_average_p… │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_21 (Dense)    │ (None, 512)       │  1,049,088 │ batch_normalizat… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dropout_14          │ (None, 512)       │          0 │ dense_21[0][0]    │\n",
              "│ (Dropout)           │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_22 (Dense)    │ (None, 256)       │    131,328 │ dropout_14[0][0]  │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dropout_15          │ (None, 256)       │          0 │ dense_22[0][0]    │\n",
              "│ (Dropout)           │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_23 (Dense)    │ (None, 1)         │        257 │ dropout_15[0][0]  │\n",
              "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m24,776,577\u001b[0m (94.52 MB)\n" ], "text/html": [ "
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\n" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "resnet_model.compile(\n", "\n", " optimizer=tf.keras.optimizers.Adam(\n", " learning_rate=0.0001\n", " ),\n", "\n", " loss='binary_crossentropy',\n", "\n", " metrics=['accuracy']\n", "\n", ")" ], "metadata": { "id": "92mAduFhVK_r" }, "execution_count": 160, "outputs": [] }, { "cell_type": "code", "source": [ "resnet_callbacks = [\n", "\n", " tf.keras.callbacks.EarlyStopping(\n", "\n", " monitor='val_loss',\n", "\n", " patience=5,\n", "\n", " restore_best_weights=True\n", "\n", " ),\n", "\n", " tf.keras.callbacks.ReduceLROnPlateau(\n", "\n", " monitor='val_loss',\n", "\n", " factor=0.2,\n", "\n", " patience=3,\n", "\n", " verbose=1\n", "\n", " ),\n", "\n", " tf.keras.callbacks.ModelCheckpoint(\n", "\n", " \"best_resnet50.keras\",\n", "\n", " monitor='val_accuracy',\n", "\n", " save_best_only=True\n", "\n", " )\n", "\n", "]" ], "metadata": { "id": "fcOf50E3VK9L" }, "execution_count": 161, "outputs": [] }, { "cell_type": "code", "source": [ "resnet_history = resnet_model.fit(\n", "\n", " train_dataset,\n", "\n", " validation_data=test_dataset,\n", "\n", " epochs=5,\n", "\n", " callbacks=resnet_callbacks\n", "\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2jjldqFeXLCo", "outputId": "bec903de-ab75-4134-95b1-893b8ad36321" }, "execution_count": 162, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 136ms/step - accuracy: 0.7112 - loss: 0.5875 - val_accuracy: 0.7939 - val_loss: 1.1552 - learning_rate: 1.0000e-04\n", "Epoch 2/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 119ms/step - accuracy: 0.7552 - loss: 0.5113 - val_accuracy: 0.7963 - val_loss: 1.1169 - learning_rate: 1.0000e-04\n", "Epoch 3/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 120ms/step - accuracy: 0.7684 - loss: 0.4952 - val_accuracy: 0.7453 - val_loss: 1.3404 - learning_rate: 1.0000e-04\n", "Epoch 4/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 116ms/step - accuracy: 0.7797 - loss: 0.4712 - val_accuracy: 0.7569 - val_loss: 1.1926 - learning_rate: 1.0000e-04\n", "Epoch 5/5\n", "\u001b[1m704/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - accuracy: 0.7796 - loss: 0.4633\n", "Epoch 5: ReduceLROnPlateau reducing learning rate to 1.9999999494757503e-05.\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 117ms/step - accuracy: 0.7807 - loss: 0.4671 - val_accuracy: 0.7055 - val_loss: 1.5787 - learning_rate: 1.0000e-04\n" ] } ] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(8,5))\n", "\n", "plt.plot(\n", " resnet_history.history['accuracy'],\n", " label='Training Accuracy'\n", ")\n", "\n", "plt.plot(\n", " resnet_history.history['val_accuracy'],\n", " label='Validation Accuracy'\n", ")\n", "\n", "plt.title(\"ResNet50 Accuracy\")\n", "\n", "plt.xlabel(\"Epoch\")\n", "\n", "plt.ylabel(\"Accuracy\")\n", "\n", "plt.legend()\n", "\n", "plt.grid(True)\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "QBMgvDyKXO9O", "outputId": "e22a8660-af45-4551-b14b-1aae0238a63e" }, "execution_count": 163, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(8,5))\n", "\n", "plt.plot(\n", " resnet_history.history['loss'],\n", " label='Training Loss'\n", ")\n", "\n", "plt.plot(\n", " resnet_history.history['val_loss'],\n", " label='Validation Loss'\n", ")\n", "\n", "plt.title(\"ResNet50 Loss\")\n", "\n", "plt.xlabel(\"Epoch\")\n", "\n", "plt.ylabel(\"Loss\")\n", "\n", "plt.legend()\n", "\n", "plt.grid(True)\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "KHZcER8kXO6w", "outputId": "8e63bbaa-1a0c-412f-bfe9-76ce8fdb34bc" }, "execution_count": 164, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "resnet_loss, resnet_accuracy = resnet_model.evaluate(test_dataset)\n", "\n", "print(\"Loss :\", resnet_loss)\n", "\n", "print(\"Accuracy :\", resnet_accuracy)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "og7a6S86XO4b", "outputId": "f51d7009-0d11-44e7-d6e4-e3dcce38a1c5" }, "execution_count": 165, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 49ms/step - accuracy: 0.7963 - loss: 1.1169\n", "Loss : 1.1169136762619019\n", "Accuracy : 0.7962594628334045\n" ] } ] }, { "cell_type": "code", "source": [ "y_true = []\n", "y_pred = []\n", "\n", "for images, labels in test_dataset:\n", "\n", " pred = resnet_model.predict(\n", " images,\n", " verbose=0\n", " )\n", "\n", " pred = (pred > 0.5).astype(int)\n", "\n", " y_true.extend(labels.numpy())\n", "\n", " y_pred.extend(pred.flatten())" ], "metadata": { "id": "Acq09CI6XO1r" }, "execution_count": 166, "outputs": [] }, { "cell_type": "code", "source": [ "cm = confusion_matrix(\n", " y_true,\n", " y_pred\n", ")\n", "\n", "plt.figure(figsize=(6,5))\n", "\n", "sns.heatmap(\n", "\n", " cm,\n", "\n", " annot=True,\n", "\n", " fmt='d',\n", "\n", " cmap='Blues',\n", "\n", " xticklabels=[\n", " 'Organic',\n", " 'Recyclable'\n", " ],\n", "\n", " yticklabels=[\n", " 'Organic',\n", " 'Recyclable'\n", " ]\n", "\n", ")\n", "\n", "plt.xlabel(\"Predicted\")\n", "\n", "plt.ylabel(\"Actual\")\n", "\n", "plt.title(\"ResNet50 Confusion Matrix\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "M3qjR8I1XOzN", "outputId": "6f2dcf4f-1263-4456-98cd-8ef7e892f56a" }, "execution_count": 167, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "print(\n", "\n", " classification_report(\n", "\n", " y_true,\n", "\n", " y_pred,\n", "\n", " target_names=[\n", " \"Organic\",\n", " \"Recyclable\"\n", " ]\n", "\n", " )\n", "\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_xP-odibXOwt", "outputId": "4fd19eb0-d544-4419-a5a1-453b2383f1de" }, "execution_count": 168, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " Organic 0.82 0.82 0.82 1401\n", " Recyclable 0.77 0.77 0.77 1112\n", "\n", " accuracy 0.80 2513\n", " macro avg 0.79 0.79 0.79 2513\n", "weighted avg 0.80 0.80 0.80 2513\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "resnet_acc = accuracy_score(\n", " y_true,\n", " y_pred\n", ")\n", "\n", "resnet_precision = precision_score(\n", " y_true,\n", " y_pred\n", ")\n", "\n", "resnet_recall = recall_score(\n", " y_true,\n", " y_pred\n", ")\n", "\n", "resnet_f1 = f1_score(\n", " y_true,\n", " y_pred\n", ")\n", "\n", "print(\"Accuracy :\", round(resnet_acc,4))\n", "print(\"Precision :\", round(resnet_precision,4))\n", "print(\"Recall :\", round(resnet_recall,4))\n", "print(\"F1 Score :\", round(resnet_f1,4))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VwCxfoHvXOuN", "outputId": "77b3427c-8ec8-4095-8c92-b7346a8db192" }, "execution_count": 169, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy : 0.7963\n", "Precision : 0.7698\n", "Recall : 0.7698\n", "F1 Score : 0.7698\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Model 4 – VGG16 (Transfer Learning)" ], "metadata": { "id": "dpBq66C1XvK9" } }, { "cell_type": "code", "source": [ "from tensorflow.keras.applications import VGG16\n", "from tensorflow.keras.models import Model\n", "from tensorflow.keras.layers import (\n", " GlobalAveragePooling2D,\n", " Dense,\n", " Dropout,\n", " BatchNormalization\n", ")" ], "metadata": { "id": "uwMeaelKXvAy" }, "execution_count": 170, "outputs": [] }, { "cell_type": "code", "source": [ "vgg_base = VGG16(\n", "\n", " weights=\"imagenet\",\n", "\n", " include_top=False,\n", "\n", " input_shape=(224,224,3)\n", "\n", ")\n", "\n", "vgg_base.summary()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 797 }, "id": "5y42YAD0Xu9_", "outputId": "124680be-ad19-4bde-8529-b0389b1479fd" }, "execution_count": 171, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1mModel: \"vgg16\"\u001b[0m\n" ], "text/html": [ "
Model: \"vgg16\"\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", "│ input_layer_8 (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m1,792\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m295,168\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,180,160\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block5_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ], "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_8 (InputLayer)      │ (None, 224, 224, 3)    │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_conv1 (Conv2D)           │ (None, 224, 224, 64)   │         1,792 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_conv2 (Conv2D)           │ (None, 224, 224, 64)   │        36,928 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_pool (MaxPooling2D)      │ (None, 112, 112, 64)   │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_conv1 (Conv2D)           │ (None, 112, 112, 128)  │        73,856 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_conv2 (Conv2D)           │ (None, 112, 112, 128)  │       147,584 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_pool (MaxPooling2D)      │ (None, 56, 56, 128)    │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv1 (Conv2D)           │ (None, 56, 56, 256)    │       295,168 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv2 (Conv2D)           │ (None, 56, 56, 256)    │       590,080 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv3 (Conv2D)           │ (None, 56, 56, 256)    │       590,080 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_pool (MaxPooling2D)      │ (None, 28, 28, 256)    │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv1 (Conv2D)           │ (None, 28, 28, 512)    │     1,180,160 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv2 (Conv2D)           │ (None, 28, 28, 512)    │     2,359,808 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv3 (Conv2D)           │ (None, 28, 28, 512)    │     2,359,808 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_pool (MaxPooling2D)      │ (None, 14, 14, 512)    │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv1 (Conv2D)           │ (None, 14, 14, 512)    │     2,359,808 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv2 (Conv2D)           │ (None, 14, 14, 512)    │     2,359,808 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv3 (Conv2D)           │ (None, 14, 14, 512)    │     2,359,808 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_pool (MaxPooling2D)      │ (None, 7, 7, 512)      │             0 │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m14,714,688\u001b[0m (56.13 MB)\n" ], "text/html": [ "
 Total params: 14,714,688 (56.13 MB)\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m14,714,688\u001b[0m (56.13 MB)\n" ], "text/html": [ "
 Trainable params: 14,714,688 (56.13 MB)\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ], "text/html": [ "
 Non-trainable params: 0 (0.00 B)\n",
              "
\n" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "for layer in vgg_base.layers:\n", " layer.trainable = False" ], "metadata": { "id": "qfmOKWLOXu7P" }, "execution_count": 172, "outputs": [] }, { "cell_type": "code", "source": [ "x = vgg_base.output\n", "\n", "x = GlobalAveragePooling2D()(x)\n", "\n", "x = BatchNormalization()(x)\n", "\n", "x = Dense(\n", " 512,\n", " activation=\"relu\"\n", ")(x)\n", "\n", "x = Dropout(0.5)(x)\n", "\n", "x = Dense(\n", " 256,\n", " activation=\"relu\"\n", ")(x)\n", "\n", "x = Dropout(0.3)(x)\n", "\n", "output = Dense(\n", " 1,\n", " activation=\"sigmoid\"\n", ")(x)\n", "\n", "vgg_model = Model(\n", "\n", " inputs=vgg_base.input,\n", "\n", " outputs=output\n", "\n", ")\n", "\n", "vgg_model.summary()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "CLxtHOf4Xu45", "outputId": "96e28508-f561-4ef9-bb67-fec0133d1983" }, "execution_count": 173, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1mModel: \"functional_8\"\u001b[0m\n" ], "text/html": [ "
Model: \"functional_8\"\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", "│ input_layer_8 (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m1,792\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m295,168\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,180,160\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ block5_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ global_average_pooling2d_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ batch_normalization_17 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_24 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m262,656\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout_16 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_25 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m131,328\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout_17 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_26 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m257\u001b[0m │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ], "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_8 (InputLayer)      │ (None, 224, 224, 3)    │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_conv1 (Conv2D)           │ (None, 224, 224, 64)   │         1,792 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_conv2 (Conv2D)           │ (None, 224, 224, 64)   │        36,928 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_pool (MaxPooling2D)      │ (None, 112, 112, 64)   │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_conv1 (Conv2D)           │ (None, 112, 112, 128)  │        73,856 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_conv2 (Conv2D)           │ (None, 112, 112, 128)  │       147,584 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_pool (MaxPooling2D)      │ (None, 56, 56, 128)    │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv1 (Conv2D)           │ (None, 56, 56, 256)    │       295,168 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv2 (Conv2D)           │ (None, 56, 56, 256)    │       590,080 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv3 (Conv2D)           │ (None, 56, 56, 256)    │       590,080 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_pool (MaxPooling2D)      │ (None, 28, 28, 256)    │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv1 (Conv2D)           │ (None, 28, 28, 512)    │     1,180,160 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv2 (Conv2D)           │ (None, 28, 28, 512)    │     2,359,808 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv3 (Conv2D)           │ (None, 28, 28, 512)    │     2,359,808 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_pool (MaxPooling2D)      │ (None, 14, 14, 512)    │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv1 (Conv2D)           │ (None, 14, 14, 512)    │     2,359,808 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv2 (Conv2D)           │ (None, 14, 14, 512)    │     2,359,808 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv3 (Conv2D)           │ (None, 14, 14, 512)    │     2,359,808 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_pool (MaxPooling2D)      │ (None, 7, 7, 512)      │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ global_average_pooling2d_5      │ (None, 512)            │             0 │\n",
              "│ (GlobalAveragePooling2D)        │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_17          │ (None, 512)            │         2,048 │\n",
              "│ (BatchNormalization)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_24 (Dense)                │ (None, 512)            │       262,656 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_16 (Dropout)            │ (None, 512)            │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_25 (Dense)                │ (None, 256)            │       131,328 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_17 (Dropout)            │ (None, 256)            │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_26 (Dense)                │ (None, 1)              │           257 │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m15,110,977\u001b[0m (57.64 MB)\n" ], "text/html": [ "
 Total params: 15,110,977 (57.64 MB)\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m395,265\u001b[0m (1.51 MB)\n" ], "text/html": [ "
 Trainable params: 395,265 (1.51 MB)\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m14,715,712\u001b[0m (56.14 MB)\n" ], "text/html": [ "
 Non-trainable params: 14,715,712 (56.14 MB)\n",
              "
\n" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "vgg_model.compile(\n", "\n", " optimizer=tf.keras.optimizers.Adam(\n", " learning_rate=0.0001\n", " ),\n", "\n", " loss=\"binary_crossentropy\",\n", "\n", " metrics=[\"accuracy\"]\n", "\n", ")" ], "metadata": { "id": "sPSMLYdSXu10" }, "execution_count": 174, "outputs": [] }, { "cell_type": "code", "source": [ "vgg_callbacks = [\n", "\n", " tf.keras.callbacks.EarlyStopping(\n", "\n", " monitor=\"val_loss\",\n", "\n", " patience=5,\n", "\n", " restore_best_weights=True\n", "\n", " ),\n", "\n", " tf.keras.callbacks.ReduceLROnPlateau(\n", "\n", " monitor=\"val_loss\",\n", "\n", " factor=0.2,\n", "\n", " patience=3,\n", "\n", " verbose=1\n", "\n", " ),\n", "\n", " tf.keras.callbacks.ModelCheckpoint(\n", "\n", " \"best_vgg16.keras\",\n", "\n", " monitor=\"val_accuracy\",\n", "\n", " save_best_only=True\n", "\n", " )\n", "\n", "]" ], "metadata": { "id": "F8A1-1oSXuzK" }, "execution_count": 175, "outputs": [] }, { "cell_type": "code", "source": [ "vgg_history = vgg_model.fit(\n", "\n", " train_dataset,\n", "\n", " validation_data=test_dataset,\n", "\n", " epochs=5,\n", "\n", " callbacks=vgg_callbacks\n", "\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rrFd4SloXuwh", "outputId": "f779da3b-a089-415b-c266-70050518446e" }, "execution_count": 176, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m94s\u001b[0m 125ms/step - accuracy: 0.8114 - loss: 0.4132 - val_accuracy: 0.8488 - val_loss: 5.8573 - learning_rate: 1.0000e-04\n", "Epoch 2/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 119ms/step - accuracy: 0.8668 - loss: 0.3188 - val_accuracy: 0.8528 - val_loss: 6.7152 - learning_rate: 1.0000e-04\n", "Epoch 3/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 124ms/step - accuracy: 0.8820 - loss: 0.2917 - val_accuracy: 0.8472 - val_loss: 6.4414 - learning_rate: 1.0000e-04\n", "Epoch 4/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 121ms/step - accuracy: 0.8854 - loss: 0.2791 - val_accuracy: 0.8739 - val_loss: 5.0342 - learning_rate: 1.0000e-04\n", "Epoch 5/5\n", "\u001b[1m706/706\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 118ms/step - accuracy: 0.8899 - loss: 0.2747 - val_accuracy: 0.8715 - val_loss: 4.9463 - learning_rate: 1.0000e-04\n" ] } ] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(8,5))\n", "plt.plot(vgg_history.history[\"accuracy\"], label=\"Training Accuracy\")\n", "plt.plot(vgg_history.history[\"val_accuracy\"],label=\"Validation Accuracy\")\n", "plt.title(\"VGG16 Accuracy\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Accuracy\")\n", "plt.legend()\n", "plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "zCZc6kShXuuH", "outputId": "32b32996-6dc8-4421-867a-a9f51eb4bbe6" }, "execution_count": 177, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(8,5))\n", "plt.plot(vgg_history.history[\"loss\"],label=\"Training Loss\")\n", "plt.plot(vgg_history.history[\"val_loss\"],label=\"Validation Loss\")\n", "plt.title(\"VGG16 Loss\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "plt.legend()\n", "plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "IklwQcn6XurR", "outputId": "5e6be1cf-88ff-4de7-82b8-f9e3cf4f9b0d" }, "execution_count": 178, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "vgg_loss, vgg_accuracy = vgg_model.evaluate(test_dataset)\n", "print(\"Loss :\", vgg_loss)\n", "print(\"Accuracy :\", vgg_accuracy)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zYW5j9S7Xuo3", "outputId": "78862d1f-583f-470a-fa1c-946b5226d29b" }, "execution_count": 179, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 47ms/step - accuracy: 0.8715 - loss: 4.9463\n", "Loss : 4.946267604827881\n", "Accuracy : 0.8714683651924133\n" ] } ] }, { "cell_type": "code", "source": [ "y_true = []\n", "\n", "y_pred = []\n", "\n", "for images, labels in test_dataset:\n", "\n", " pred = vgg_model.predict(\n", " images,\n", " verbose=0\n", " )\n", "\n", " pred = (pred > 0.5).astype(int)\n", "\n", " y_true.extend(labels.numpy())\n", "\n", " y_pred.extend(pred.flatten())" ], "metadata": { "id": "_4ZeiOnXXul-" }, "execution_count": 180, "outputs": [] }, { "cell_type": "code", "source": [ "cm = confusion_matrix(\n", " y_true,\n", " y_pred\n", ")\n", "\n", "plt.figure(figsize=(6,5))\n", "\n", "sns.heatmap(\n", "\n", " cm,\n", "\n", " annot=True,\n", "\n", " fmt=\"d\",\n", "\n", " cmap=\"Blues\",\n", "\n", " xticklabels=[\n", " \"Organic\",\n", " \"Recyclable\"\n", " ],\n", "\n", " yticklabels=[\n", " \"Organic\",\n", " \"Recyclable\"\n", " ]\n", "\n", ")\n", "\n", "plt.xlabel(\"Predicted\")\n", "\n", "plt.ylabel(\"Actual\")\n", "\n", "plt.title(\"VGG16 Confusion Matrix\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "Z9qum8FyXujY", "outputId": "2324e1d4-a0e9-4292-b700-3947a90475e4" }, "execution_count": 181, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "print(\n", "\n", " classification_report(\n", "\n", " y_true,\n", "\n", " y_pred,\n", "\n", " target_names=[\n", " \"Organic\",\n", " \"Recyclable\"\n", " ]\n", "\n", " )\n", "\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XH7a6OPZXugp", "outputId": "75ad5edd-1457-4a98-d0e6-c5777de2c814" }, "execution_count": 182, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " Organic 0.84 0.95 0.89 1401\n", " Recyclable 0.92 0.77 0.84 1112\n", "\n", " accuracy 0.87 2513\n", " macro avg 0.88 0.86 0.87 2513\n", "weighted avg 0.88 0.87 0.87 2513\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "vgg_acc = accuracy_score(\n", " y_true,\n", " y_pred\n", ")\n", "\n", "vgg_precision = precision_score(\n", " y_true,\n", " y_pred\n", ")\n", "\n", "vgg_recall = recall_score(\n", " y_true,\n", " y_pred\n", ")\n", "\n", "vgg_f1 = f1_score(\n", " y_true,\n", " y_pred\n", ")\n", "\n", "print(\"Accuracy :\", round(vgg_acc,4))\n", "\n", "print(\"Precision :\", round(vgg_precision,4))\n", "\n", "print(\"Recall :\", round(vgg_recall,4))\n", "\n", "print(\"F1 Score :\", round(vgg_f1,4))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7axQtLi5XueW", "outputId": "013686df-612f-4ca2-a367-17271f181bd3" }, "execution_count": 183, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy : 0.8715\n", "Precision : 0.9228\n", "Recall : 0.7743\n", "F1 Score : 0.8421\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Model Comparison" ], "metadata": { "id": "cX_vBF7bYJrU" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "\n", "comparison = pd.DataFrame({\n", "\n", " \"Model\":[\n", "\n", " \"Custom CNN\",\n", "\n", " \"MobileNetV2\",\n", "\n", " \"ResNet50\",\n", "\n", " \"VGG16\"\n", "\n", " ],\n", "\n", " \"Accuracy\":[\n", "\n", " cnn_acc,\n", "\n", " mobilenet_acc,\n", "\n", " resnet_acc,\n", "\n", " vgg_acc\n", "\n", " ],\n", "\n", " \"Precision\":[\n", "\n", " cnn_precision,\n", "\n", " mobilenet_precision,\n", "\n", " resnet_precision,\n", "\n", " vgg_precision\n", "\n", " ],\n", "\n", " \"Recall\":[\n", "\n", " cnn_recall,\n", "\n", " mobilenet_recall,\n", "\n", " resnet_recall,\n", "\n", " vgg_recall\n", "\n", " ],\n", "\n", " \"F1 Score\":[\n", "\n", " cnn_f1,\n", "\n", " mobilenet_f1,\n", "\n", " resnet_f1,\n", "\n", " vgg_f1\n", "\n", " ]\n", "\n", "})\n", "\n", "comparison" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "Qs8k-PkHXubX", "outputId": "dcf76f2e-6c13-4b95-84e5-a5a50db04be3" }, "execution_count": 184, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Model Accuracy Precision Recall F1 Score\n", "0 Custom CNN 0.858735 0.886619 0.780576 0.830225\n", "1 MobileNetV2 0.563072 0.503642 0.870504 0.638102\n", "2 ResNet50 0.796259 0.769784 0.769784 0.769784\n", "3 VGG16 0.871468 0.922830 0.774281 0.842054" ], "text/html": [ "\n", "
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ModelAccuracyPrecisionRecallF1 Score
0Custom CNN0.8587350.8866190.7805760.830225
1MobileNetV20.5630720.5036420.8705040.638102
2ResNet500.7962590.7697840.7697840.769784
3VGG160.8714680.9228300.7742810.842054
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ModelAccuracyPrecisionRecallF1 Score
0Custom CNN0.85870.88660.78060.8302
1MobileNetV20.56310.50360.87050.6381
2ResNet500.79630.76980.76980.7698
3VGG160.87150.92280.77430.8421
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(8,5))\n", "\n", "plt.bar(\n", "\n", " comparison[\"Model\"],\n", "\n", " comparison[\"Precision\"]\n", "\n", ")\n", "\n", "plt.title(\"Model Precision Comparison\")\n", "\n", "plt.xlabel(\"Models\")\n", "\n", "plt.ylabel(\"Precision\")\n", "\n", "plt.grid(axis=\"y\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "lZknhuA1XOpg", "outputId": "fac321c7-7030-4fa4-af9c-37d13a347c3c" }, "execution_count": 187, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(8,5))\n", "\n", "plt.bar(\n", "\n", " comparison[\"Model\"],\n", "\n", " comparison[\"Recall\"]\n", "\n", ")\n", "\n", "plt.title(\"Model Recall Comparison\")\n", "\n", "plt.xlabel(\"Models\")\n", "\n", "plt.ylabel(\"Recall\")\n", "\n", "plt.grid(axis=\"y\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "4-W0KvaFXOm9", "outputId": "370f002f-23fd-45b4-e37b-173af0013e6c" }, "execution_count": 188, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(8,5))\n", "\n", "plt.bar(\n", "\n", " comparison[\"Model\"],\n", "\n", " comparison[\"F1 Score\"]\n", "\n", ")\n", "\n", "plt.title(\"Model F1 Score Comparison\")\n", "\n", "plt.xlabel(\"Models\")\n", "\n", "plt.ylabel(\"F1 Score\")\n", "\n", "plt.grid(axis=\"y\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "sTMYD6i0YR_h", "outputId": "e91482f5-cf34-45a7-a5ec-250b76ff096b" }, "execution_count": 189, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "comparison.set_index(\"Model\").plot(\n", "\n", " kind=\"bar\",\n", "\n", " figsize=(10,6)\n", "\n", ")\n", "\n", "plt.title(\"Performance Comparison of All Models\")\n", "\n", "plt.ylabel(\"Score\")\n", "\n", "plt.xticks(rotation=0)\n", "\n", "plt.grid(axis=\"y\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 564 }, "id": "j0eYZHq2YR8y", "outputId": "bde9413a-f3e2-476e-9b46-38516ae2b93c" }, "execution_count": 190, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} } ] }, { "cell_type": "code", "source": [ "best_model = comparison.loc[\n", "\n", " comparison[\"Accuracy\"].idxmax()\n", "\n", "]\n", "\n", "print(\"Best Performing Model\")\n", "\n", "print(best_model)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "07WD5Nr4YR5-", "outputId": "5c9d3281-9e6b-499b-f696-72d4d49f9e82" }, "execution_count": 191, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Best Performing Model\n", "Model VGG16\n", "Accuracy 0.8715\n", "Precision 0.9228\n", "Recall 0.7743\n", "F1 Score 0.8421\n", "Name: 3, dtype: object\n" ] } ] }, { "cell_type": "code", "source": [ "comparison = comparison.sort_values(\n", "\n", " by=\"Accuracy\",\n", "\n", " ascending=False\n", "\n", ")\n", "\n", "comparison" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "zGgY-_qQXOkq", "outputId": "6175f900-e1a0-4750-f51d-ba4944a77c06" }, "execution_count": 192, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Model Accuracy Precision Recall F1 Score\n", "3 VGG16 0.8715 0.9228 0.7743 0.8421\n", "0 Custom CNN 0.8587 0.8866 0.7806 0.8302\n", "2 ResNet50 0.7963 0.7698 0.7698 0.7698\n", "1 MobileNetV2 0.5631 0.5036 0.8705 0.6381" ], "text/html": [ "\n", "
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ModelAccuracyPrecisionRecallF1 Score
3VGG160.87150.92280.77430.8421
0Custom CNN0.85870.88660.78060.8302
2ResNet500.79630.76980.76980.7698
1MobileNetV20.56310.50360.87050.6381
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "comparison", "summary": "{\n \"name\": \"comparison\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"Model\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Custom CNN\",\n \"MobileNetV2\",\n \"VGG16\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Accuracy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.14334829379289218,\n \"min\": 0.5631,\n \"max\": 0.8715,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.8587,\n 0.5631,\n 0.8715\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Precision\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.1896579728528876,\n \"min\": 0.5036,\n \"max\": 0.9228,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.8866,\n 0.5036,\n 0.9228\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Recall\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.048004791427523175,\n \"min\": 0.7698,\n \"max\": 0.8705,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.7806,\n 0.8705,\n 0.7743\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"F1 Score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.09348812758848046,\n \"min\": 0.6381,\n \"max\": 0.8421,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.8302,\n 0.6381,\n 0.8421\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 192 } ] } ] }