{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"cell 1 imports ans seed"
],
"metadata": {
"id": "l7gfYdXx33MK"
}
},
{
"cell_type": "code",
"source": [
"import os, random, itertools\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"from tensorflow.keras import datasets, layers, models\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from sklearn.metrics import classification_report, confusion_matrix\n",
"\n",
"SEED = 42\n",
"random.seed(SEED)\n",
"np.random.seed(SEED)\n",
"tf.random.set_seed(SEED)\n",
"\n",
"CLASS_NAMES = ['airplane','car','bird','cat','deer','dog','frog','horse','ship','lorry']\n"
],
"metadata": {
"id": "EfQGBo4w5YIF"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"cell 2 hyperparameter"
],
"metadata": {
"id": "_5LEXDXa5Z3L"
}
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"hyperparams = {\n",
" \"Hyperparameter\": [\n",
" \"Optimiser\", \"Learning Rate\", \"Epochs\", \"Batch Size\", \"Validation Split\",\n",
" \"Conv Filters\", \"Pooling\", \"Dense Head\", \"Data Augmentation\",\n",
" \"Training Subset\", \"Label Noise\", \"Seed\"\n",
" ],\n",
" \"Value\": [\n",
" \"Adam\", \"0.001\", \"10\", \"32\", \"0.2\",\n",
" \"32 → 64 (3×3, ReLU, same)\", \"2×2 after each block\",\n",
" \"Dense 64 → Softmax 10\", \"Disabled (switch available)\",\n",
" \"25% of training data\", \"10% random flips\", \"42\"\n",
" ]\n",
"}\n",
"\n",
"df = pd.DataFrame(hyperparams)\n",
"print(df)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "YzB7_ZHV5e-s",
"outputId": "ada7b5d5-b015-4ee6-9122-cf642edfcfdb"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" Hyperparameter Value\n",
"0 Optimiser Adam\n",
"1 Learning Rate 0.001\n",
"2 Epochs 10\n",
"3 Batch Size 32\n",
"4 Validation Split 0.2\n",
"5 Conv Filters 32 → 64 (3×3, ReLU, same)\n",
"6 Pooling 2×2 after each block\n",
"7 Dense Head Dense 64 → Softmax 10\n",
"8 Data Augmentation Disabled (switch available)\n",
"9 Training Subset 25% of training data\n",
"10 Label Noise 10% random flips\n",
"11 Seed 42\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"CELL 3 — Load CIFAR-10"
],
"metadata": {
"id": "xHSIR0i45ukz"
}
},
{
"cell_type": "code",
"source": [
"(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()\n",
"y_train = y_train.flatten()\n",
"y_test = y_test.flatten()\n",
"\n",
"print(\"Loaded CIFAR-10:\")\n",
"print(\"Train:\", x_train.shape, y_train.shape)\n",
"print(\"Test: \", x_test.shape, y_test.shape)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "KLFj_5YZ5xn8",
"outputId": "3c735832-e615-49ab-df86-4e59ad3ceed5"
},
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
"\u001b[1m170498071/170498071\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 0us/step\n",
"Loaded CIFAR-10:\n",
"Train: (50000, 32, 32, 3) (50000,)\n",
"Test: (10000, 32, 32, 3) (10000,)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"CELL 4 — Normalise + UNDERFIT MODE"
],
"metadata": {
"id": "4CbXdDZ16L_K"
}
},
{
"cell_type": "code",
"source": [
"x_train = x_train.astype(\"float32\") / 255.0\n",
"x_test = x_test.astype(\"float32\") / 255.0\n",
"\n",
"UNDERFIT = True\n",
"TRAIN_FRACTION = 0.25\n",
"LABEL_NOISE = 0.10\n",
"\n",
"if UNDERFIT:\n",
" n = int(len(x_train) * TRAIN_FRACTION)\n",
" idx = np.random.choice(len(x_train), n, replace=False)\n",
" x_train = x_train[idx]\n",
" y_train = y_train[idx]\n",
"\n",
" # Label noise\n",
" noise_n = int(len(y_train) * LABEL_NOISE)\n",
" noise_idx = np.random.choice(len(y_train), noise_n, replace=False)\n",
" y_train[noise_idx] = np.random.randint(0, 10, size=noise_n)\n",
"\n",
"print(\"Training subset:\", x_train.shape)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "R1EUf8T96Nbs",
"outputId": "e225b3a2-7a9c-41a8-b3ea-3c985841fbfa"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training subset: (12500, 32, 32, 3)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"CELL 5 — Build CNN Model"
],
"metadata": {
"id": "Bg_8Y1pR6cWP"
}
},
{
"cell_type": "code",
"source": [
"def build_model():\n",
" model = models.Sequential([\n",
" layers.Conv2D(32, (3,3), activation='relu', padding='same', input_shape=(32,32,3)),\n",
" layers.MaxPooling2D(2,2),\n",
" layers.Conv2D(64, (3,3), activation='relu', padding='same'),\n",
" layers.MaxPooling2D(2,2),\n",
" layers.Flatten(),\n",
" layers.Dense(64, activation='relu'),\n",
" layers.Dense(10, activation='softmax')\n",
" ])\n",
"\n",
" model.compile(\n",
" optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
" loss=\"sparse_categorical_crossentropy\",\n",
" metrics=[\"accuracy\"]\n",
" )\n",
" return model\n",
"\n",
"model = build_model()\n",
"model.summary()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 404
},
"id": "87sT-hsG6jiB",
"outputId": "e5e214d5-9972-41f5-ab25-33a304aa7f9c"
},
"execution_count": 12,
"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\"\u001b[0m\n"
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"text/html": [
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"\n"
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"┃\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 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m262,208\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
],
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
"┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
"│ conv2d (Conv2D) │ (None, 32, 32, 32) │ 896 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ max_pooling2d (MaxPooling2D) │ (None, 16, 16, 32) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ conv2d_1 (Conv2D) │ (None, 16, 16, 64) │ 18,496 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ max_pooling2d_1 (MaxPooling2D) │ (None, 8, 8, 64) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ flatten (Flatten) │ (None, 4096) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense (Dense) │ (None, 64) │ 262,208 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_1 (Dense) │ (None, 10) │ 650 │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
"\n"
]
},
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},
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},
{
"cell_type": "markdown",
"source": [
"CELL 7 — Train"
],
"metadata": {
"id": "tH5kTHSK7GSI"
}
},
{
"cell_type": "code",
"source": [
"EPOCHS = 10\n",
"BATCH = 32\n",
"VAL_SPLIT = 0.2\n",
"\n",
"history = model.fit(\n",
" x_train, y_train,\n",
" epochs=EPOCHS,\n",
" batch_size=BATCH,\n",
" validation_split=VAL_SPLIT,\n",
" verbose=1\n",
")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "Yt_1bdAW7HK4",
"outputId": "fe6ca8fb-216e-4df8-d74b-3d6b905aa41e"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/10\n",
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 63ms/step - accuracy: 0.2464 - loss: 2.0657 - val_accuracy: 0.3860 - val_loss: 1.7295\n",
"Epoch 2/10\n",
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 59ms/step - accuracy: 0.4456 - loss: 1.6288 - val_accuracy: 0.4580 - val_loss: 1.6021\n",
"Epoch 3/10\n",
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 62ms/step - accuracy: 0.5155 - loss: 1.4822 - val_accuracy: 0.5020 - val_loss: 1.5180\n",
"Epoch 4/10\n",
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 59ms/step - accuracy: 0.5604 - loss: 1.3724 - val_accuracy: 0.5112 - val_loss: 1.4835\n",
"Epoch 5/10\n",
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 60ms/step - accuracy: 0.5921 - loss: 1.2855 - val_accuracy: 0.5216 - val_loss: 1.4652\n",
"Epoch 6/10\n",
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 68ms/step - accuracy: 0.6187 - loss: 1.1998 - val_accuracy: 0.5300 - val_loss: 1.4779\n",
"Epoch 7/10\n",
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 58ms/step - accuracy: 0.6531 - loss: 1.1098 - val_accuracy: 0.5320 - val_loss: 1.5131\n",
"Epoch 8/10\n",
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 59ms/step - accuracy: 0.6842 - loss: 1.0203 - val_accuracy: 0.5140 - val_loss: 1.5952\n",
"Epoch 9/10\n",
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 60ms/step - accuracy: 0.7183 - loss: 0.9331 - val_accuracy: 0.4992 - val_loss: 1.7067\n",
"Epoch 10/10\n",
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 59ms/step - accuracy: 0.7472 - loss: 0.8481 - val_accuracy: 0.4880 - val_loss: 1.8421\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"CELL 8 — SHOW + SAVE Accuracy & Loss Figures\n"
],
"metadata": {
"id": "F6ltVOuQ7-TI"
}
},
{
"cell_type": "code",
"source": [
"\n",
"def show_and_save_training_curves(history):\n",
" # Accuracy\n",
" plt.figure()\n",
" plt.plot(history.history['accuracy'], label='Train')\n",
" plt.plot(history.history['val_accuracy'], label='Validation')\n",
" plt.title(\"Training vs Validation Accuracy\")\n",
" plt.xlabel(\"Epoch\"); plt.ylabel(\"Accuracy\")\n",
" plt.legend()\n",
" plt.tight_layout()\n",
" plt.savefig(\"acc.png\", dpi=200)\n",
" plt.show() # <-- SHOWS IMAGE\n",
" plt.close()\n",
"\n",
" # Loss\n",
" plt.figure()\n",
" plt.plot(history.history['loss'], label='Train')\n",
" plt.plot(history.history['val_loss'], label='Validation')\n",
" plt.title(\"Training vs Validation Loss\")\n",
" plt.xlabel(\"Epoch\"); plt.ylabel(\"Loss\")\n",
" plt.legend()\n",
" plt.tight_layout()\n",
" plt.savefig(\"loss.png\", dpi=200)\n",
" plt.show() # <-- SHOWS IMAGE\n",
" plt.close()\n",
"\n",
"show_and_save_training_curves(history)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 956
},
"id": "9E-uyz5n8DeJ",
"outputId": "35c13704-a986-4f13-bf63-ad9f5c3d166f"
},
"execution_count": 14,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"CELL 9 — Test Accuracy + Confusion Matrix (SHOWS IMAGE)"
],
"metadata": {
"id": "mUCuiNvx8ltg"
}
},
{
"cell_type": "code",
"source": [
"test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0)\n",
"print(f\"Test Loss: {test_loss:.4f} | Test Accuracy: {test_acc:.4f}\")\n",
"\n",
"cm = confusion_matrix(y_test, y_pred := np.argmax(model.predict(x_test, verbose=0), axis=1))\n",
"\n",
"plt.figure(figsize=(8,6))\n",
"plt.imshow(cm, cmap='Blues')\n",
"plt.title(\"Confusion Matrix\")\n",
"plt.xticks(range(10), CLASS_NAMES, rotation=45)\n",
"plt.yticks(range(10), CLASS_NAMES)\n",
"plt.colorbar()\n",
"plt.tight_layout()\n",
"plt.savefig(\"confusion.png\", dpi=200)\n",
"plt.show() # <-- SHOWS IMAGE\n",
"plt.close()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 588
},
"id": "NOoCOWx38psU",
"outputId": "d257b2c8-d237-4c92-a068-6e6e1fb28335"
},
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Test Loss: 1.4834 | Test Accuracy: 0.5338\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"CELL 10 — Classification Report"
],
"metadata": {
"id": "AfDlYIgu9CsN"
}
},
{
"cell_type": "code",
"source": [
"print(classification_report(y_test, y_pred, target_names=CLASS_NAMES))\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "bk-fRJwU9Cdy",
"outputId": "56f133bb-3d7d-405e-bd68-6f7b076934ee"
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" airplane 0.72 0.52 0.61 1000\n",
" car 0.76 0.58 0.66 1000\n",
" bird 0.54 0.30 0.39 1000\n",
" cat 0.38 0.33 0.35 1000\n",
" deer 0.50 0.40 0.45 1000\n",
" dog 0.41 0.46 0.44 1000\n",
" frog 0.58 0.64 0.61 1000\n",
" horse 0.37 0.83 0.51 1000\n",
" ship 0.70 0.70 0.70 1000\n",
" lorry 0.69 0.57 0.62 1000\n",
"\n",
" accuracy 0.53 10000\n",
" macro avg 0.56 0.53 0.53 10000\n",
"weighted avg 0.56 0.53 0.53 10000\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"CELL 11 — Misclassified Grid (SHOWS IMAGE)"
],
"metadata": {
"id": "djFq07_y9SkQ"
}
},
{
"cell_type": "code",
"source": [
"def save_and_show_misclassified(x, y_true, y_pred, classes, max_examples=25):\n",
" wrong = np.where(y_true != y_pred)[0]\n",
" if len(wrong) == 0:\n",
" print(\"No misclassified examples.\")\n",
" return\n",
"\n",
" n = min(max_examples, len(wrong))\n",
" sel = np.random.choice(wrong, n, replace=False)\n",
"\n",
" cols = 5\n",
" rows = int(np.ceil(n / cols))\n",
" plt.figure(figsize=(12, 3*rows))\n",
"\n",
" for i, idx in enumerate(sel):\n",
" plt.subplot(rows, cols, i+1)\n",
" plt.imshow(x[idx])\n",
" plt.title(f\"T:{classes[y_true[idx]]}\\nP:{classes[y_pred[idx]]}\", fontsize=8)\n",
" plt.axis(\"off\")\n",
"\n",
" plt.tight_layout()\n",
" plt.savefig(\"misclassified.png\", dpi=200)\n",
" plt.show() # <-- SHOWS IMAGE\n",
" plt.close()\n",
"\n",
"save_and_show_misclassified(x_test, y_test, y_pred, CLASS_NAMES, max_examples=15)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 464
},
"id": "XRAhFzrs9Vqe",
"outputId": "4ffb80cf-9993-4d84-ee41-3e8aa4b7433c"
},
"execution_count": 17,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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}
]
},
{
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"source": [],
"metadata": {
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}
}
]
}