{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c9f9473-0b56-4553-b33f-15fcfdd262c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0. IMPORTS, CONTROLS, & SETUP\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models, regularizers\n",
    "from tensorflow.keras.optimizers import schedules\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import os\n",
    "import json\n",
    "import pickle\n",
    "import gc\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9027e49f-c689-47a7-a5fd-6e4f8f662b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0.A - CONTROL SWITCHES\n",
    "# Set these to True/False to control which parts of the script run\n",
    "RUN_MODEL_1_BASELINE = True\n",
    "RUN_MODEL_2_IMPROVED = True\n",
    "RUN_MODEL_3_SOTA_100 = True\n",
    "RUN_MODEL_3_SOTA_150 = True\n",
    "RUN_COMPARISON_PLOTS = True\n",
    "RUN_FAILURE_ANALYSIS = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "71ae10ac-a225-4e18-bd83-d73259e8baf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0.B - CONFIGURATION\n",
    "BATCH_SIZE = 128\n",
    "IMG_SIZE = 32\n",
    "NUM_CLASSES = 10\n",
    "WEIGHT_DECAY = 1e-4\n",
    "RESULTS_FILE = 'all_results.json'\n",
    "CLASS_NAMES = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
    "               'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "\n",
    "# Define model names and epochs\n",
    "MODEL_CONFIG = {\n",
    "    \"Model_1_Baseline\": {\"display\": \"Model 1: Baseline\", \"epochs\": 100, \"patience\": 15},\n",
    "    \"Model_2_Improved\": {\"display\": \"Model 2: Improved\", \"epochs\": 100, \"patience\": 15},\n",
    "    \"Model_3_SOTA_100e\": {\"display\": \"Model 3: SOTA (100e)\", \"epochs\": 100, \"patience\": 15},\n",
    "    \"Model_3_SOTA_150e\": {\"display\": \"Model 3: SOTA (150e)\", \"epochs\": 150, \"patience\": 20}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b0e18d6-9424-42e2-95e5-950e50e1fd7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Mixed precision compatibility check (mixed_float16): OK\n",
      "Your GPU will likely run quickly with dtype policy mixed_float16 as it has compute capability of at least 7.0. Your GPU: NVIDIA GeForce RTX 4060 Ti, compute capability 8.9\n",
      "Mixed Precision (float16) enabled.\n",
      "============================================================\n",
      "CIFAR-10 CLASSIFICATION (4-STAGE EXPERIMENT)\n",
      "TensorFlow Version: 2.10.0\n",
      "Num GPUs Available: 1\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "# 0.C - ENVIRONMENT SETUP\n",
    "os.makedirs('models', exist_ok=True)\n",
    "os.makedirs('plots', exist_ok=True)\n",
    "\n",
    "# Enable Mixed Precision\n",
    "try:\n",
    "    policy = tf.keras.mixed_precision.Policy('mixed_float16')\n",
    "    tf.keras.mixed_precision.set_global_policy(policy)\n",
    "    print(\"Mixed Precision (float16) enabled.\")\n",
    "except Exception as e:\n",
    "    print(f\"Could not enable Mixed Precision: {e}\")\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(\"CIFAR-10 CLASSIFICATION (4-STAGE EXPERIMENT)\")\n",
    "print(f\"TensorFlow Version: {tf.__version__}\")\n",
    "print(f\"Num GPUs Available: {len(tf.config.list_physical_devices('GPU'))}\")\n",
    "print(\"=\"*60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cc1f645d-dfee-4693-a26a-df0b564c1536",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. MODEL & HELPER DEFINITIONS\n",
    "# All functions and classes are defined here, before any execution.\n",
    "\n",
    "# --- DATA HELPERS ---\n",
    "def load_and_preprocess_data():\n",
    "    \"\"\"Loads CIFAR-10 and splits it into train, validation, and test sets.\"\"\"\n",
    "    print(\"Loading and preprocessing data...\")\n",
    "    try:\n",
    "        (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
    "    except Exception as e:\n",
    "        print(f\"CRITICAL: Could not load CIFAR-10 dataset. {e}\")\n",
    "        return None\n",
    "\n",
    "    x_train_norm = x_train.astype('float32') / 255.0\n",
    "    x_test_norm = x_test.astype('float32') / 255.0\n",
    "\n",
    "    val_size = 5000\n",
    "    x_val_norm = x_train_norm[-val_size:]\n",
    "    y_val = y_train[-val_size:]\n",
    "    x_train_norm = x_train_norm[:-val_size]\n",
    "    y_train = y_train[:-val_size]\n",
    "\n",
    "    print(f\"Train: {x_train_norm.shape}, Val: {x_val_norm.shape}, Test: {x_test_norm.shape}\")\n",
    "    return (x_train_norm, y_train), (x_val_norm, y_val), (x_test_norm, y_test), (x_test, y_test)\n",
    "\n",
    "def augment_image(image, label):\n",
    "    \"\"\"Data augmentation function for the training pipeline.\"\"\"\n",
    "    image = tf.image.random_flip_left_right(image)\n",
    "    image = tf.image.resize_with_crop_or_pad(image, IMG_SIZE + 4, IMG_SIZE + 4)\n",
    "    image = tf.image.random_crop(image, [IMG_SIZE, IMG_SIZE, 3])\n",
    "    image = tf.image.random_brightness(image, 0.2)\n",
    "    image = tf.image.random_contrast(image, 0.8, 1.2)\n",
    "    image = tf.clip_by_value(image, 0.0, 1.0)\n",
    "    return image, label\n",
    "\n",
    "def create_dataset(x, y, batch_size, augment=False, shuffle=True):\n",
    "    \"\"\"Creates an efficient tf.data.Dataset pipeline.\"\"\"\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((x, y))\n",
    "    if shuffle:\n",
    "        dataset = dataset.shuffle(10000)\n",
    "    if augment:\n",
    "        dataset = dataset.map(augment_image, num_parallel_calls=tf.data.AUTOTUNE)\n",
    "    dataset = dataset.batch(batch_size).prefetch(tf.data.AUTOTUNE)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef8c71f0-9bf0-40f4-aa67-8db649ec781a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading and preprocessing data...\n",
      "Train: (45000, 32, 32, 3), Val: (5000, 32, 32, 3), Test: (10000, 32, 32, 3)\n",
      "Creating datasets...\n",
      "Data and datasets are ready.\n"
     ]
    }
   ],
   "source": [
    "# 2. LOAD DATA & CREATE DATASETS\n",
    "# This cell calls the functions defined in the cell above.\n",
    "# It must be run before any of the training cells.\n",
    "\n",
    "data = load_and_preprocess_data()\n",
    "if data:\n",
    "    (x_train, y_train), (x_val, y_val), (x_test_norm, y_test), (x_test_orig, y_test_orig) = data\n",
    "    train_size = len(x_train)\n",
    "    \n",
    "    # Create datasets\n",
    "    print(\"Creating datasets...\")\n",
    "    train_ds_basic = create_dataset(x_train, y_train, BATCH_SIZE, augment=False)\n",
    "    train_ds_augmented = create_dataset(x_train, y_train, BATCH_SIZE, augment=True)\n",
    "    val_ds = create_dataset(x_val, y_val, BATCH_SIZE, augment=False, shuffle=False)\n",
    "    test_ds = create_dataset(x_test_norm, y_test, BATCH_SIZE, augment=False, shuffle=False)\n",
    "    \n",
    "    # Load existing results\n",
    "    results = load_results()\n",
    "    print(\"Data and datasets are ready.\")\n",
    "else:\n",
    "    print(\"Failed to load data. Halting script.\")\n",
    "    # In a notebook, you might want to raise an error here\n",
    "    # raise Exception(\"Data loading failed. Cannot proceed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6532fb34-32c8-4d24-bd60-ce29fae25a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- MODEL 1: BASELINE CNN ---\n",
    "def create_model_1_baseline():\n",
    "    model = models.Sequential([\n",
    "        layers.Conv2D(32, 3, padding='same', activation='relu', input_shape=(32, 32, 3)),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.Conv2D(32, 3, padding='same', activation='relu'),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.MaxPooling2D(2),\n",
    "        layers.Dropout(0.2),\n",
    "        layers.Conv2D(64, 3, padding='same', activation='relu'),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.Conv2D(64, 3, padding='same', activation='relu'),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.MaxPooling2D(2),\n",
    "        layers.Dropout(0.3),\n",
    "        layers.Conv2D(128, 3, padding='same', activation='relu'),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.Conv2D(128, 3, padding='same', activation='relu'),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.MaxPooling2D(2),\n",
    "        layers.Dropout(0.4),\n",
    "        layers.Flatten(),\n",
    "        layers.Dense(128, activation='relu'),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.Dropout(0.5),\n",
    "        layers.Dense(NUM_CLASSES, activation='softmax', dtype='float32')\n",
    "    ], name=\"Model_1_Baseline\")\n",
    "    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7048855b-24f1-405d-ac84-2e5f984b9e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- MODEL 2: IMPROVED CNN ---\n",
    "def create_model_2_improved():\n",
    "    model = models.Sequential([\n",
    "        layers.Conv2D(64, 3, padding='same', activation='relu', input_shape=(32, 32, 3)),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.Conv2D(64, 3, padding='same', activation='relu'),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.MaxPooling2D(2),\n",
    "        layers.Dropout(0.2),\n",
    "        layers.Conv2D(128, 3, padding='same', activation='relu'),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.Conv2D(128, 3, padding='same', activation='relu'),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.MaxPooling2D(2),\n",
    "        layers.Dropout(0.3),\n",
    "        layers.Conv2D(256, 3, padding='same', activation='relu'),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.Conv2D(256, 3, padding='same', activation='relu'),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.MaxPooling2D(2),\n",
    "        layers.Dropout(0.4),\n",
    "        layers.GlobalAveragePooling2D(),\n",
    "        layers.Dense(256, activation='relu'),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.Dropout(0.5),\n",
    "        layers.Dense(NUM_CLASSES, activation='softmax', dtype='float32')\n",
    "    ], name=\"Model_2_Improved\")\n",
    "    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),\n",
    "                  loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "afa834b3-8d60-4d6c-beb8-c5069e869e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- MODEL 3: SOTA (ResNet9-inspired) ---\n",
    "class CustomLinearWarmup(schedules.LearningRateSchedule):\n",
    "    def __init__(self, warmup_learning_rate, target_learning_rate, warmup_steps, decay_steps):\n",
    "        super().__init__()\n",
    "        self.warmup_learning_rate = warmup_learning_rate\n",
    "        self.target_learning_rate = target_learning_rate\n",
    "        self.warmup_steps = warmup_steps\n",
    "        self.cosine_decay = schedules.CosineDecay(target_learning_rate, decay_steps, alpha=0.0)\n",
    "\n",
    "    def __call__(self, step):\n",
    "        step_float = tf.cast(step, tf.float32)\n",
    "        warmup_steps_float = tf.cast(self.warmup_steps, tf.float32)\n",
    "        warmup_lr = self.warmup_learning_rate + ((self.target_learning_rate - self.warmup_learning_rate) * (step_float / warmup_steps_float))\n",
    "        is_warmup = tf.cast(step_float < warmup_steps_float, tf.float32)\n",
    "        after_step = step - self.warmup_steps\n",
    "        return (1.0 - is_warmup) * self.cosine_decay(after_step) + (is_warmup * warmup_lr)\n",
    "\n",
    "    def get_config(self):\n",
    "        return {\"warmup_learning_rate\": self.warmup_learning_rate, \"target_learning_rate\": self.target_learning_rate,\n",
    "                \"warmup_steps\": self.warmup_steps, \"decay_steps\": self.cosine_decay.decay_steps}\n",
    "\n",
    "def residual_block(x, filters, stride=1):\n",
    "    shortcut = x\n",
    "    x = layers.Conv2D(filters, 3, strides=stride, padding='same', kernel_regularizer=regularizers.l2(WEIGHT_DECAY))(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation('relu')(x)\n",
    "    x = layers.Conv2D(filters, 3, padding='same', kernel_regularizer=regularizers.l2(WEIGHT_DECAY))(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    if stride != 1 or shortcut.shape[-1] != filters:\n",
    "        shortcut = layers.Conv2D(filters, 1, strides=stride, padding='same', kernel_regularizer=regularizers.l2(WEIGHT_DECAY))(shortcut)\n",
    "        shortcut = layers.BatchNormalization()(shortcut)\n",
    "    x = layers.Add()([x, shortcut])\n",
    "    x = layers.Activation('relu')(x)\n",
    "    return x\n",
    "\n",
    "def create_model_3_sota(train_size, total_epochs):\n",
    "    inputs = layers.Input(shape=(32, 32, 3))\n",
    "    x = layers.Conv2D(64, 3, padding='same', kernel_regularizer=regularizers.l2(WEIGHT_DECAY))(inputs)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation('relu')(x)\n",
    "    x = residual_block(x, 64)\n",
    "    x = layers.MaxPooling2D(2)(x)\n",
    "    x = layers.Dropout(0.2)(x)\n",
    "    x = residual_block(x, 128, stride=1)\n",
    "    x = layers.MaxPooling2D(2)(x)\n",
    "    x = layers.Dropout(0.3)(x)\n",
    "    x = residual_block(x, 256, stride=1)\n",
    "    x = layers.MaxPooling2D(2)(x)\n",
    "    x = layers.Dropout(0.4)(x)\n",
    "    x = layers.GlobalAveragePooling2D()(x)\n",
    "    x = layers.Dense(256, activation='relu', kernel_regularizer=regularizers.l2(WEIGHT_DECAY))(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Dropout(0.5)(x)\n",
    "    outputs = layers.Dense(NUM_CLASSES, activation='softmax', dtype='float32')(x)\n",
    "    \n",
    "    steps_per_epoch = train_size // BATCH_SIZE\n",
    "    warmup_steps = steps_per_epoch * 5\n",
    "    decay_steps = steps_per_epoch * (total_epochs - 5)\n",
    "    \n",
    "    lr_schedule = CustomLinearWarmup(0.0, 1e-3, warmup_steps, decay_steps)\n",
    "    model = models.Model(inputs=inputs, outputs=outputs, name=f\"Model_3_SOTA_{total_epochs}e\")\n",
    "    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr_schedule),\n",
    "                  loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6967f0cf-da9b-46b1-8bf1-59b27dacc646",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All model and helper functions defined.\n"
     ]
    }
   ],
   "source": [
    "# GENERIC HELPERS (TRAIN, PLOT, EVAL)\n",
    "def train_model(model, train_ds, val_ds, epochs, model_name, patience, use_lr_schedule=False):\n",
    "    \"\"\"Trains a given model with standard callbacks.\"\"\"\n",
    "    callbacks = [\n",
    "        tf.keras.callbacks.ModelCheckpoint(\n",
    "            f'models/{model_name}_best.h5', monitor='val_accuracy', save_best_only=True, verbose=1\n",
    "        ),\n",
    "        tf.keras.callbacks.EarlyStopping(\n",
    "            monitor='val_accuracy', patience=patience, restore_best_weights=True, verbose=1\n",
    "        )\n",
    "    ]\n",
    "    if not use_lr_schedule:\n",
    "        print(f\"Using ReduceLROnPlateau scheduler for {model_name}.\")\n",
    "        callbacks.append(\n",
    "            tf.keras.callbacks.ReduceLROnPlateau(\n",
    "                monitor='val_loss', factor=0.5, patience=5, min_lr=1e-6, verbose=1\n",
    "            )\n",
    "        )\n",
    "    else:\n",
    "        print(f\"Using custom learning rate schedule for {model_name}.\")\n",
    "\n",
    "    print(f\"\\n--- Training {model_name} for {epochs} epochs ---\")\n",
    "    start_time = datetime.now()\n",
    "    history = model.fit(\n",
    "        train_ds, validation_data=val_ds, epochs=epochs, callbacks=callbacks, verbose=1\n",
    "    )\n",
    "    print(f\"Training finished in: {datetime.now() - start_time}\")\n",
    "    \n",
    "    history_path = f'models/{model_name}_history.pkl'\n",
    "    try:\n",
    "        with open(history_path, 'wb') as f:\n",
    "            pickle.dump(history.history, f)\n",
    "        print(f\"Successfully saved history to {history_path}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error saving history for {model_name}: {e}\")\n",
    "    return history\n",
    "\n",
    "def plot_history(history, model_name):\n",
    "    \"\"\"Plots and saves accuracy and loss curves.\"\"\"\n",
    "    if history is None or not history.history:\n",
    "        print(f\"No history object to plot for {model_name}.\")\n",
    "        return\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "    axes[0].plot(history.history['accuracy'], label='Train')\n",
    "    axes[0].plot(history.history['val_accuracy'], label='Validation')\n",
    "    axes[0].set_title(f'{model_name} - Accuracy'); axes[0].set_xlabel('Epoch')\n",
    "    axes[0].set_ylabel('Accuracy'); axes[0].legend(); axes[0].grid(True)\n",
    "    axes[1].plot(history.history['loss'], label='Train')\n",
    "    axes[1].plot(history.history['val_loss'], label='Validation')\n",
    "    axes[1].set_title(f'{model_name} - Loss'); axes[1].set_xlabel('Epoch')\n",
    "    axes[1].set_ylabel('Loss'); axes[1].legend(); axes[1].grid(True)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'plots/{model_name}_history.png', dpi=150)\n",
    "    plt.show()\n",
    "\n",
    "def evaluate_model(model_path, test_ds):\n",
    "    \"\"\"Loads and evaluates the best model checkpoint on the test set.\"\"\"\n",
    "    print(f\"\\n{'='*60}\\nEVALUATING: {model_path}\\n{'='*60}\")\n",
    "    try:\n",
    "        model = tf.keras.models.load_model(\n",
    "            model_path, custom_objects={\"CustomLinearWarmup\": CustomLinearWarmup}\n",
    "        )\n",
    "        print(f\"Loaded best weights from {model_path}\")\n",
    "    except Exception as e:\n",
    "        print(f\"CRITICAL: Could not load model {model_path}. Error: {e}\")\n",
    "        return None, None\n",
    "    test_loss, test_acc = model.evaluate(test_ds, verbose=1)\n",
    "    print(f\"\\nTest Accuracy: {test_acc*100:.2f}%\\nTest Loss: {test_loss:.4f}\")\n",
    "    return test_acc, test_loss\n",
    "\n",
    "def load_results():\n",
    "    if os.path.exists(RESULTS_FILE):\n",
    "        try:\n",
    "            with open(RESULTS_FILE, 'r') as f: return json.load(f)\n",
    "        except: return {}\n",
    "    return {}\n",
    "\n",
    "def save_results(results):\n",
    "    with open(RESULTS_FILE, 'w') as f:\n",
    "        json.dump(results, f, indent=4)\n",
    "    print(f\"Results saved to {RESULTS_FILE}\")\n",
    "\n",
    "def load_history(model_name):\n",
    "    \"\"\"Loads a single training history from a .pkl file.\"\"\"\n",
    "    history_path = f'models/{model_name}_history.pkl'\n",
    "    if not os.path.exists(history_path):\n",
    "        print(f\"Warning: History file not found: {history_path}\")\n",
    "        return None\n",
    "    try:\n",
    "        with open(history_path, 'rb') as f: return pickle.load(f)\n",
    "    except Exception as e:\n",
    "        print(f\"Error loading history for {model_name}: {e}\")\n",
    "        return None\n",
    "\n",
    "def plot_combined_learning_curves(results, model_config):\n",
    "    \"\"\"Plots combined accuracy and loss curves for all models.\"\"\"\n",
    "    print(\"\\nGenerating combined learning curves...\")\n",
    "    colors = {\n",
    "        \"Model_1_Baseline\": \"#3498db\", # Blue\n",
    "        \"Model_2_Improved\": \"#2ecc71\", # Green\n",
    "        \"Model_3_SOTA_100e\": \"#e74c3c\", # Red\n",
    "        \"Model_3_SOTA_150e\": \"#f39c12\"  # Orange\n",
    "    }\n",
    "    \n",
    "    fig, axes = plt.subplots(1, 2, figsize=(18, 7))\n",
    "    \n",
    "    for name in model_config:\n",
    "        if name not in results:\n",
    "            continue\n",
    "        \n",
    "        hist = load_history(name)\n",
    "        if hist:\n",
    "            display_name = model_config[name][\"display\"]\n",
    "            color = colors.get(name, 'black')\n",
    "            \n",
    "            # Accuracy Plot\n",
    "            axes[0].plot(hist['val_accuracy'], label=f'{display_name} (Val)', color=color, linestyle='--')\n",
    "            axes[0].plot(hist['accuracy'], label=f'{display_name} (Train)', color=color, alpha=0.3)\n",
    "            \n",
    "            # Loss Plot\n",
    "            axes[1].plot(hist['val_loss'], label=f'{display_name} (Val)', color=color, linestyle='--')\n",
    "            axes[1].plot(hist['loss'], label=f'{display_name} (Train)', color=color, alpha=0.3)\n",
    "\n",
    "    axes[0].set_title('Combined Model Accuracy Comparison', fontsize=16)\n",
    "    axes[0].set_xlabel('Epoch', fontsize=12); axes[0].set_ylabel('Accuracy', fontsize=12)\n",
    "    axes[0].legend(loc='lower right', fontsize=10); axes[0].grid(True); axes[0].set_ylim(0.5, 1.0)\n",
    "    \n",
    "    axes[1].set_title('Combined Model Loss Comparison', fontsize=16)\n",
    "    axes[1].set_xlabel('Epoch', fontsize=12); axes[1].set_ylabel('Loss', fontsize=12)\n",
    "    axes[1].legend(loc='upper right', fontsize=10); axes[1].grid(True); axes[1].set_ylim(0, 2.5)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('plots/combined_learning_curves_4_models.png', dpi=150)\n",
    "    plt.show()\n",
    "\n",
    "def plot_final_comparison_bar(results, model_config):\n",
    "    \"\"\"Plots the final bar chart of test accuracies for all 4 models.\"\"\"\n",
    "    print(f\"\\n{'='*60}\\nFINAL TEST ACCURACY COMPARISON\\n{'='*60}\")\n",
    "    \n",
    "    display_names = []\n",
    "    accuracies = []\n",
    "    \n",
    "    for name, config in model_config.items():\n",
    "        if name in results:\n",
    "            acc, loss = results[name]\n",
    "            display_names.append(config['display'])\n",
    "            accuracies.append(acc * 100)\n",
    "            print(f\"{config['display']:30s} | Accuracy: {acc*100:6.2f}% | Loss: {loss:.4f}\")\n",
    "        \n",
    "    if not accuracies:\n",
    "        print(\"No results to plot. Run the model scripts first.\")\n",
    "        return\n",
    "\n",
    "    plt.figure(figsize=(12, 7))\n",
    "    colors = ['#3498db', '#2ecc71', '#e74c3c', '#f39c12']\n",
    "    bars = plt.bar(display_names, accuracies, color=colors[:len(accuracies)])\n",
    "    plt.ylabel('Test Accuracy (%)')\n",
    "    plt.title('Final Model Test Accuracy Comparison', fontsize=16, fontweight='bold')\n",
    "    plt.ylim([80, 100]) # Zoom in for better comparison\n",
    "        \n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        if height > 0:\n",
    "            plt.text(bar.get_x() + bar.get_width()/2., height,\n",
    "                     f'{height:.2f}%', ha='center', va='bottom', fontsize=12, fontweight='bold')\n",
    "                 \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('plots/final_comparison_4_models.png', dpi=150)\n",
    "    plt.show()\n",
    "\n",
    "def analyze_failures(model_path, x_test_norm, x_test_orig, y_test_true):\n",
    "    \"\"\"Analyzes the model's failures and plots the top 5 most common confusion pairs.\"\"\"\n",
    "    print(f\"\\n--- Analyzing failures for {model_path} ---\")\n",
    "    \n",
    "    try:\n",
    "        model = tf.keras.models.load_model(\n",
    "            model_path, custom_objects={\"CustomLinearWarmup\": CustomLinearWarmup}\n",
    "        )\n",
    "        print(f\"Loaded best weights from {model_path}\")\n",
    "    except Exception as e:\n",
    "        print(f\"CRITICAL: Could not load model {model_path}. Error: {e}\")\n",
    "        return\n",
    "\n",
    "    y_pred_probs = model.predict(x_test_norm)\n",
    "    y_pred_classes = np.argmax(y_pred_probs, axis=1)\n",
    "    y_true_flat = y_test_true.flatten()\n",
    "    \n",
    "    misclassified_indices = np.where(y_pred_classes != y_true_flat)[0]\n",
    "    print(f\"Found {len(misclassified_indices)} total misclassifications.\")\n",
    "        \n",
    "    confusion_pairs = []\n",
    "    for i in misclassified_indices:\n",
    "        true_label = CLASS_NAMES[y_true_flat[i]]\n",
    "        pred_label = CLASS_NAMES[y_pred_classes[i]]\n",
    "        confusion_pairs.append((true_label, pred_label))\n",
    "            \n",
    "    top_5_confusions = Counter(confusion_pairs).most_common(5)\n",
    "    \n",
    "    print(\"Top 5 confusion pairs (True, Predicted):\")\n",
    "    for (true, pred), count in top_5_confusions:\n",
    "        print(f\"  - True: '{true}', Pred: '{pred}' (Count: {count})\")\n",
    "            \n",
    "    plt.figure(figsize=(20, 5))\n",
    "    plt.suptitle(f'{os.path.basename(model_path)}: Top 5 Most Common Failures', fontsize=16, fontweight='bold')\n",
    "        \n",
    "    plot_index = 1\n",
    "    for (true_label, pred_label), count in top_5_confusions:\n",
    "        example_index = -1\n",
    "        for i in misclassified_indices:\n",
    "            if CLASS_NAMES[y_true_flat[i]] == true_label and CLASS_NAMES[y_pred_classes[i]] == pred_label:\n",
    "                example_index = i\n",
    "                break\n",
    "                \n",
    "        if example_index != -1:\n",
    "            ax = plt.subplot(1, 5, plot_index)\n",
    "            plt.imshow(x_test_orig[example_index]) # Use original image\n",
    "            ax.set_title(f\"True: {true_label}\\nPredicted: {pred_label}\", fontsize=12)\n",
    "            ax.axis('off')\n",
    "            plot_index += 1\n",
    "            \n",
    "    plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "    plt.savefig(f'plots/failure_analysis_{os.path.basename(model_path)}.png', dpi=150)\n",
    "    plt.show()\n",
    "\n",
    "print(\"All model and helper functions defined.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bc370e93-2161-4b8b-9178-07b2daadd0f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "STARTING: Model_1_Baseline\n",
      "============================================================\n",
      "Model: \"Model_1_Baseline\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 32, 32, 32)        896       \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 32, 32, 32)       128       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 32, 32, 32)        9248      \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 32, 32, 32)       128       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 16, 16, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 16, 16, 32)        0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 16, 16, 64)        18496     \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 16, 16, 64)       256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 16, 16, 64)        36928     \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 16, 16, 64)       256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 8, 8, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 8, 8, 64)          0         \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 8, 8, 128)         73856     \n",
      "                                                                 \n",
      " batch_normalization_4 (Batc  (None, 8, 8, 128)        512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 8, 8, 128)         147584    \n",
      "                                                                 \n",
      " batch_normalization_5 (Batc  (None, 8, 8, 128)        512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 4, 4, 128)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 4, 4, 128)         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2048)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               262272    \n",
      "                                                                 \n",
      " batch_normalization_6 (Batc  (None, 128)              512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 552,874\n",
      "Trainable params: 551,722\n",
      "Non-trainable params: 1,152\n",
      "_________________________________________________________________\n",
      "Using ReduceLROnPlateau scheduler for Model_1_Baseline.\n",
      "\n",
      "--- Training Model_1_Baseline for 100 epochs ---\n",
      "Epoch 1/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 1.7273 - accuracy: 0.4230\n",
      "Epoch 1: val_accuracy improved from -inf to 0.16520, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 6s 13ms/step - loss: 1.7273 - accuracy: 0.4230 - val_loss: 3.5430 - val_accuracy: 0.1652 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 1.1338 - accuracy: 0.5985\n",
      "Epoch 2: val_accuracy improved from 0.16520 to 0.65340, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 1.1336 - accuracy: 0.5987 - val_loss: 1.0081 - val_accuracy: 0.6534 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.9477 - accuracy: 0.6685\n",
      "Epoch 3: val_accuracy improved from 0.65340 to 0.68760, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.9473 - accuracy: 0.6686 - val_loss: 0.8774 - val_accuracy: 0.6876 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.8421 - accuracy: 0.7055\n",
      "Epoch 4: val_accuracy did not improve from 0.68760\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.8421 - accuracy: 0.7055 - val_loss: 1.3975 - val_accuracy: 0.5788 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.7699 - accuracy: 0.7309\n",
      "Epoch 5: val_accuracy improved from 0.68760 to 0.75580, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.7697 - accuracy: 0.7312 - val_loss: 0.7205 - val_accuracy: 0.7558 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.7098 - accuracy: 0.7528\n",
      "Epoch 6: val_accuracy improved from 0.75580 to 0.77940, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.7096 - accuracy: 0.7529 - val_loss: 0.6449 - val_accuracy: 0.7794 - lr: 0.0010\n",
      "Epoch 7/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.6538 - accuracy: 0.7723\n",
      "Epoch 7: val_accuracy did not improve from 0.77940\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.6537 - accuracy: 0.7724 - val_loss: 1.0159 - val_accuracy: 0.7706 - lr: 0.0010\n",
      "Epoch 8/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.6155 - accuracy: 0.7862\n",
      "Epoch 8: val_accuracy improved from 0.77940 to 0.80460, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.6159 - accuracy: 0.7862 - val_loss: 0.5665 - val_accuracy: 0.8046 - lr: 0.0010\n",
      "Epoch 9/100\n",
      "347/352 [============================>.] - ETA: 0s - loss: 0.5799 - accuracy: 0.8016\n",
      "Epoch 9: val_accuracy improved from 0.80460 to 0.80960, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.5811 - accuracy: 0.8010 - val_loss: 0.5579 - val_accuracy: 0.8096 - lr: 0.0010\n",
      "Epoch 10/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.5449 - accuracy: 0.8120\n",
      "Epoch 10: val_accuracy did not improve from 0.80960\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.5455 - accuracy: 0.8118 - val_loss: 0.6249 - val_accuracy: 0.7862 - lr: 0.0010\n",
      "Epoch 11/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.5180 - accuracy: 0.8196\n",
      "Epoch 11: val_accuracy did not improve from 0.80960\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.5180 - accuracy: 0.8196 - val_loss: 0.6169 - val_accuracy: 0.7898 - lr: 0.0010\n",
      "Epoch 12/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.4883 - accuracy: 0.8304\n",
      "Epoch 12: val_accuracy did not improve from 0.80960\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.4880 - accuracy: 0.8305 - val_loss: 0.6249 - val_accuracy: 0.7928 - lr: 0.0010\n",
      "Epoch 13/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4653 - accuracy: 0.8389\n",
      "Epoch 13: val_accuracy improved from 0.80960 to 0.82180, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.4652 - accuracy: 0.8390 - val_loss: 0.5539 - val_accuracy: 0.8218 - lr: 0.0010\n",
      "Epoch 14/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.4461 - accuracy: 0.8441\n",
      "Epoch 14: val_accuracy improved from 0.82180 to 0.82820, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.4461 - accuracy: 0.8441 - val_loss: 0.5218 - val_accuracy: 0.8282 - lr: 0.0010\n",
      "Epoch 15/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4318 - accuracy: 0.8490\n",
      "Epoch 15: val_accuracy did not improve from 0.82820\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.4316 - accuracy: 0.8491 - val_loss: 0.5706 - val_accuracy: 0.8156 - lr: 0.0010\n",
      "Epoch 16/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.4133 - accuracy: 0.8566\n",
      "Epoch 16: val_accuracy improved from 0.82820 to 0.83840, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.4130 - accuracy: 0.8566 - val_loss: 0.4783 - val_accuracy: 0.8384 - lr: 0.0010\n",
      "Epoch 17/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3989 - accuracy: 0.8620\n",
      "Epoch 17: val_accuracy improved from 0.83840 to 0.84320, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.3989 - accuracy: 0.8620 - val_loss: 0.4761 - val_accuracy: 0.8432 - lr: 0.0010\n",
      "Epoch 18/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.3834 - accuracy: 0.8674\n",
      "Epoch 18: val_accuracy did not improve from 0.84320\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.3838 - accuracy: 0.8671 - val_loss: 0.5214 - val_accuracy: 0.8318 - lr: 0.0010\n",
      "Epoch 19/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3667 - accuracy: 0.8733\n",
      "Epoch 19: val_accuracy improved from 0.84320 to 0.84700, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.3666 - accuracy: 0.8733 - val_loss: 0.4818 - val_accuracy: 0.8470 - lr: 0.0010\n",
      "Epoch 20/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3593 - accuracy: 0.8764\n",
      "Epoch 20: val_accuracy improved from 0.84700 to 0.85180, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.3593 - accuracy: 0.8764 - val_loss: 0.4524 - val_accuracy: 0.8518 - lr: 0.0010\n",
      "Epoch 21/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3365 - accuracy: 0.8835\n",
      "Epoch 21: val_accuracy did not improve from 0.85180\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.3366 - accuracy: 0.8835 - val_loss: 0.6113 - val_accuracy: 0.8094 - lr: 0.0010\n",
      "Epoch 22/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3346 - accuracy: 0.8840\n",
      "Epoch 22: val_accuracy did not improve from 0.85180\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.3344 - accuracy: 0.8841 - val_loss: 0.4925 - val_accuracy: 0.8420 - lr: 0.0010\n",
      "Epoch 23/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.3241 - accuracy: 0.8864\n",
      "Epoch 23: val_accuracy did not improve from 0.85180\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.3243 - accuracy: 0.8862 - val_loss: 0.4799 - val_accuracy: 0.8420 - lr: 0.0010\n",
      "Epoch 24/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3116 - accuracy: 0.8899\n",
      "Epoch 24: val_accuracy did not improve from 0.85180\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.3116 - accuracy: 0.8899 - val_loss: 0.5100 - val_accuracy: 0.8410 - lr: 0.0010\n",
      "Epoch 25/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3030 - accuracy: 0.8945\n",
      "Epoch 25: val_accuracy did not improve from 0.85180\n",
      "\n",
      "Epoch 25: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.3030 - accuracy: 0.8945 - val_loss: 0.4724 - val_accuracy: 0.8484 - lr: 0.0010\n",
      "Epoch 26/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2512 - accuracy: 0.9127\n",
      "Epoch 26: val_accuracy improved from 0.85180 to 0.86060, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 0.2505 - accuracy: 0.9130 - val_loss: 0.4464 - val_accuracy: 0.8606 - lr: 5.0000e-04\n",
      "Epoch 27/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.2389 - accuracy: 0.9175\n",
      "Epoch 27: val_accuracy improved from 0.86060 to 0.86940, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.2381 - accuracy: 0.9177 - val_loss: 0.4264 - val_accuracy: 0.8694 - lr: 5.0000e-04\n",
      "Epoch 28/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2304 - accuracy: 0.9207\n",
      "Epoch 28: val_accuracy did not improve from 0.86940\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.2304 - accuracy: 0.9207 - val_loss: 0.4863 - val_accuracy: 0.8562 - lr: 5.0000e-04\n",
      "Epoch 29/100\n",
      "347/352 [============================>.] - ETA: 0s - loss: 0.2201 - accuracy: 0.9236\n",
      "Epoch 29: val_accuracy improved from 0.86940 to 0.86980, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.2200 - accuracy: 0.9237 - val_loss: 0.4309 - val_accuracy: 0.8698 - lr: 5.0000e-04\n",
      "Epoch 30/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2138 - accuracy: 0.9250\n",
      "Epoch 30: val_accuracy improved from 0.86980 to 0.87240, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.2137 - accuracy: 0.9250 - val_loss: 0.4358 - val_accuracy: 0.8724 - lr: 5.0000e-04\n",
      "Epoch 31/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2077 - accuracy: 0.9272\n",
      "Epoch 31: val_accuracy did not improve from 0.87240\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.2077 - accuracy: 0.9272 - val_loss: 0.4428 - val_accuracy: 0.8722 - lr: 5.0000e-04\n",
      "Epoch 32/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.2095 - accuracy: 0.9258\n",
      "Epoch 32: val_accuracy did not improve from 0.87240\n",
      "\n",
      "Epoch 32: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.2097 - accuracy: 0.9256 - val_loss: 0.4436 - val_accuracy: 0.8712 - lr: 5.0000e-04\n",
      "Epoch 33/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1864 - accuracy: 0.9350\n",
      "Epoch 33: val_accuracy improved from 0.87240 to 0.87420, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1866 - accuracy: 0.9348 - val_loss: 0.4349 - val_accuracy: 0.8742 - lr: 2.5000e-04\n",
      "Epoch 34/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.1756 - accuracy: 0.9377\n",
      "Epoch 34: val_accuracy did not improve from 0.87420\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 0.1756 - accuracy: 0.9377 - val_loss: 0.4360 - val_accuracy: 0.8742 - lr: 2.5000e-04\n",
      "Epoch 35/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1718 - accuracy: 0.9400\n",
      "Epoch 35: val_accuracy did not improve from 0.87420\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1719 - accuracy: 0.9400 - val_loss: 0.4450 - val_accuracy: 0.8716 - lr: 2.5000e-04\n",
      "Epoch 36/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.1654 - accuracy: 0.9419\n",
      "Epoch 36: val_accuracy did not improve from 0.87420\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1651 - accuracy: 0.9419 - val_loss: 0.4733 - val_accuracy: 0.8696 - lr: 2.5000e-04\n",
      "Epoch 37/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1610 - accuracy: 0.9436\n",
      "Epoch 37: val_accuracy did not improve from 0.87420\n",
      "\n",
      "Epoch 37: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1613 - accuracy: 0.9435 - val_loss: 0.4744 - val_accuracy: 0.8652 - lr: 2.5000e-04\n",
      "Epoch 38/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1513 - accuracy: 0.9475\n",
      "Epoch 38: val_accuracy did not improve from 0.87420\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 0.1512 - accuracy: 0.9474 - val_loss: 0.4478 - val_accuracy: 0.8712 - lr: 1.2500e-04\n",
      "Epoch 39/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1478 - accuracy: 0.9480\n",
      "Epoch 39: val_accuracy did not improve from 0.87420\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.1475 - accuracy: 0.9481 - val_loss: 0.4493 - val_accuracy: 0.8732 - lr: 1.2500e-04\n",
      "Epoch 40/100\n",
      "347/352 [============================>.] - ETA: 0s - loss: 0.1450 - accuracy: 0.9492\n",
      "Epoch 40: val_accuracy did not improve from 0.87420\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1454 - accuracy: 0.9491 - val_loss: 0.4434 - val_accuracy: 0.8730 - lr: 1.2500e-04\n",
      "Epoch 41/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1436 - accuracy: 0.9501\n",
      "Epoch 41: val_accuracy improved from 0.87420 to 0.87600, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1433 - accuracy: 0.9502 - val_loss: 0.4460 - val_accuracy: 0.8760 - lr: 1.2500e-04\n",
      "Epoch 42/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1396 - accuracy: 0.9507\n",
      "Epoch 42: val_accuracy improved from 0.87600 to 0.87760, saving model to models\\Model_1_Baseline_best.h5\n",
      "\n",
      "Epoch 42: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1395 - accuracy: 0.9508 - val_loss: 0.4451 - val_accuracy: 0.8776 - lr: 1.2500e-04\n",
      "Epoch 43/100\n",
      "347/352 [============================>.] - ETA: 0s - loss: 0.1371 - accuracy: 0.9518\n",
      "Epoch 43: val_accuracy improved from 0.87760 to 0.87820, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1367 - accuracy: 0.9520 - val_loss: 0.4480 - val_accuracy: 0.8782 - lr: 6.2500e-05\n",
      "Epoch 44/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1325 - accuracy: 0.9526\n",
      "Epoch 44: val_accuracy improved from 0.87820 to 0.87880, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1325 - accuracy: 0.9526 - val_loss: 0.4474 - val_accuracy: 0.8788 - lr: 6.2500e-05\n",
      "Epoch 45/100\n",
      "347/352 [============================>.] - ETA: 0s - loss: 0.1330 - accuracy: 0.9539\n",
      "Epoch 45: val_accuracy did not improve from 0.87880\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.1334 - accuracy: 0.9537 - val_loss: 0.4460 - val_accuracy: 0.8782 - lr: 6.2500e-05\n",
      "Epoch 46/100\n",
      "347/352 [============================>.] - ETA: 0s - loss: 0.1329 - accuracy: 0.9534\n",
      "Epoch 46: val_accuracy did not improve from 0.87880\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1328 - accuracy: 0.9534 - val_loss: 0.4458 - val_accuracy: 0.8766 - lr: 6.2500e-05\n",
      "Epoch 47/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.1309 - accuracy: 0.9533\n",
      "Epoch 47: val_accuracy did not improve from 0.87880\n",
      "\n",
      "Epoch 47: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1309 - accuracy: 0.9532 - val_loss: 0.4456 - val_accuracy: 0.8758 - lr: 6.2500e-05\n",
      "Epoch 48/100\n",
      "347/352 [============================>.] - ETA: 0s - loss: 0.1272 - accuracy: 0.9552\n",
      "Epoch 48: val_accuracy improved from 0.87880 to 0.87900, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1274 - accuracy: 0.9551 - val_loss: 0.4434 - val_accuracy: 0.8790 - lr: 3.1250e-05\n",
      "Epoch 49/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.1258 - accuracy: 0.9558\n",
      "Epoch 49: val_accuracy improved from 0.87900 to 0.87960, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1255 - accuracy: 0.9559 - val_loss: 0.4448 - val_accuracy: 0.8796 - lr: 3.1250e-05\n",
      "Epoch 50/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1252 - accuracy: 0.9554\n",
      "Epoch 50: val_accuracy did not improve from 0.87960\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1252 - accuracy: 0.9554 - val_loss: 0.4490 - val_accuracy: 0.8796 - lr: 3.1250e-05\n",
      "Epoch 51/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1234 - accuracy: 0.9572\n",
      "Epoch 51: val_accuracy did not improve from 0.87960\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1236 - accuracy: 0.9571 - val_loss: 0.4481 - val_accuracy: 0.8782 - lr: 3.1250e-05\n",
      "Epoch 52/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.9558\n",
      "Epoch 52: val_accuracy did not improve from 0.87960\n",
      "\n",
      "Epoch 52: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.1242 - accuracy: 0.9558 - val_loss: 0.4486 - val_accuracy: 0.8778 - lr: 3.1250e-05\n",
      "Epoch 53/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.1234 - accuracy: 0.9563\n",
      "Epoch 53: val_accuracy did not improve from 0.87960\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.1233 - accuracy: 0.9563 - val_loss: 0.4491 - val_accuracy: 0.8778 - lr: 1.5625e-05\n",
      "Epoch 54/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1210 - accuracy: 0.9568\n",
      "Epoch 54: val_accuracy did not improve from 0.87960\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.1210 - accuracy: 0.9568 - val_loss: 0.4501 - val_accuracy: 0.8796 - lr: 1.5625e-05\n",
      "Epoch 55/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1198 - accuracy: 0.9576\n",
      "Epoch 55: val_accuracy did not improve from 0.87960\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1198 - accuracy: 0.9577 - val_loss: 0.4496 - val_accuracy: 0.8792 - lr: 1.5625e-05\n",
      "Epoch 56/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1213 - accuracy: 0.9565\n",
      "Epoch 56: val_accuracy did not improve from 0.87960\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1213 - accuracy: 0.9564 - val_loss: 0.4505 - val_accuracy: 0.8786 - lr: 1.5625e-05\n",
      "Epoch 57/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.1257 - accuracy: 0.9557\n",
      "Epoch 57: val_accuracy did not improve from 0.87960\n",
      "\n",
      "Epoch 57: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06.\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1256 - accuracy: 0.9558 - val_loss: 0.4503 - val_accuracy: 0.8794 - lr: 1.5625e-05\n",
      "Epoch 58/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1206 - accuracy: 0.9574\n",
      "Epoch 58: val_accuracy did not improve from 0.87960\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.1206 - accuracy: 0.9574 - val_loss: 0.4517 - val_accuracy: 0.8794 - lr: 7.8125e-06\n",
      "Epoch 59/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1216 - accuracy: 0.9581\n",
      "Epoch 59: val_accuracy improved from 0.87960 to 0.88020, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1215 - accuracy: 0.9580 - val_loss: 0.4519 - val_accuracy: 0.8802 - lr: 7.8125e-06\n",
      "Epoch 60/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.1208 - accuracy: 0.9571\n",
      "Epoch 60: val_accuracy did not improve from 0.88020\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1207 - accuracy: 0.9572 - val_loss: 0.4509 - val_accuracy: 0.8798 - lr: 7.8125e-06\n",
      "Epoch 61/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1204 - accuracy: 0.9577\n",
      "Epoch 61: val_accuracy did not improve from 0.88020\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.1204 - accuracy: 0.9577 - val_loss: 0.4513 - val_accuracy: 0.8800 - lr: 7.8125e-06\n",
      "Epoch 62/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1185 - accuracy: 0.9588\n",
      "Epoch 62: val_accuracy improved from 0.88020 to 0.88060, saving model to models\\Model_1_Baseline_best.h5\n",
      "\n",
      "Epoch 62: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06.\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 0.1186 - accuracy: 0.9588 - val_loss: 0.4515 - val_accuracy: 0.8806 - lr: 7.8125e-06\n",
      "Epoch 63/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1206 - accuracy: 0.9579\n",
      "Epoch 63: val_accuracy did not improve from 0.88060\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 0.1206 - accuracy: 0.9579 - val_loss: 0.4512 - val_accuracy: 0.8800 - lr: 3.9063e-06\n",
      "Epoch 64/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1214 - accuracy: 0.9566\n",
      "Epoch 64: val_accuracy did not improve from 0.88060\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1214 - accuracy: 0.9566 - val_loss: 0.4507 - val_accuracy: 0.8804 - lr: 3.9063e-06\n",
      "Epoch 65/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.1217 - accuracy: 0.9580\n",
      "Epoch 65: val_accuracy improved from 0.88060 to 0.88100, saving model to models\\Model_1_Baseline_best.h5\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1218 - accuracy: 0.9580 - val_loss: 0.4509 - val_accuracy: 0.8810 - lr: 3.9063e-06\n",
      "Epoch 66/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1207 - accuracy: 0.9572\n",
      "Epoch 66: val_accuracy did not improve from 0.88100\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1207 - accuracy: 0.9572 - val_loss: 0.4516 - val_accuracy: 0.8796 - lr: 3.9063e-06\n",
      "Epoch 67/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1237 - accuracy: 0.9561\n",
      "Epoch 67: val_accuracy did not improve from 0.88100\n",
      "\n",
      "Epoch 67: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06.\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 0.1236 - accuracy: 0.9562 - val_loss: 0.4512 - val_accuracy: 0.8806 - lr: 3.9063e-06\n",
      "Epoch 68/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1170 - accuracy: 0.9595\n",
      "Epoch 68: val_accuracy did not improve from 0.88100\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 0.1169 - accuracy: 0.9595 - val_loss: 0.4523 - val_accuracy: 0.8798 - lr: 1.9531e-06\n",
      "Epoch 69/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1174 - accuracy: 0.9581\n",
      "Epoch 69: val_accuracy did not improve from 0.88100\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1174 - accuracy: 0.9581 - val_loss: 0.4529 - val_accuracy: 0.8804 - lr: 1.9531e-06\n",
      "Epoch 70/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1216 - accuracy: 0.9569\n",
      "Epoch 70: val_accuracy did not improve from 0.88100\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1217 - accuracy: 0.9569 - val_loss: 0.4517 - val_accuracy: 0.8800 - lr: 1.9531e-06\n",
      "Epoch 71/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1207 - accuracy: 0.9565\n",
      "Epoch 71: val_accuracy did not improve from 0.88100\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1208 - accuracy: 0.9566 - val_loss: 0.4515 - val_accuracy: 0.8790 - lr: 1.9531e-06\n",
      "Epoch 72/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.1182 - accuracy: 0.9575\n",
      "Epoch 72: val_accuracy did not improve from 0.88100\n",
      "\n",
      "Epoch 72: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1181 - accuracy: 0.9575 - val_loss: 0.4512 - val_accuracy: 0.8802 - lr: 1.9531e-06\n",
      "Epoch 73/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.1223 - accuracy: 0.9569\n",
      "Epoch 73: val_accuracy did not improve from 0.88100\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1223 - accuracy: 0.9568 - val_loss: 0.4517 - val_accuracy: 0.8800 - lr: 1.0000e-06\n",
      "Epoch 74/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1213 - accuracy: 0.9571\n",
      "Epoch 74: val_accuracy did not improve from 0.88100\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.1212 - accuracy: 0.9571 - val_loss: 0.4515 - val_accuracy: 0.8802 - lr: 1.0000e-06\n",
      "Epoch 75/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1223 - accuracy: 0.9565\n",
      "Epoch 75: val_accuracy did not improve from 0.88100\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 0.1222 - accuracy: 0.9566 - val_loss: 0.4514 - val_accuracy: 0.8800 - lr: 1.0000e-06\n",
      "Epoch 76/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1226 - accuracy: 0.9566\n",
      "Epoch 76: val_accuracy did not improve from 0.88100\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1228 - accuracy: 0.9565 - val_loss: 0.4523 - val_accuracy: 0.8808 - lr: 1.0000e-06\n",
      "Epoch 77/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1211 - accuracy: 0.9567\n",
      "Epoch 77: val_accuracy did not improve from 0.88100\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1212 - accuracy: 0.9567 - val_loss: 0.4523 - val_accuracy: 0.8806 - lr: 1.0000e-06\n",
      "Epoch 78/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1215 - accuracy: 0.9570\n",
      "Epoch 78: val_accuracy did not improve from 0.88100\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1215 - accuracy: 0.9570 - val_loss: 0.4519 - val_accuracy: 0.8802 - lr: 1.0000e-06\n",
      "Epoch 79/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1197 - accuracy: 0.9578\n",
      "Epoch 79: val_accuracy did not improve from 0.88100\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1196 - accuracy: 0.9578 - val_loss: 0.4522 - val_accuracy: 0.8800 - lr: 1.0000e-06\n",
      "Epoch 80/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1218 - accuracy: 0.9565\n",
      "Epoch 80: val_accuracy did not improve from 0.88100\n",
      "Restoring model weights from the end of the best epoch: 65.\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 0.1218 - accuracy: 0.9565 - val_loss: 0.4521 - val_accuracy: 0.8800 - lr: 1.0000e-06\n",
      "Epoch 80: early stopping\n",
      "Training finished in: 0:05:06.256899\n",
      "Successfully saved history to models/Model_1_Baseline_history.pkl\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "EVALUATING: models/Model_1_Baseline_best.h5\n",
      "============================================================\n",
      "Loaded best weights from models/Model_1_Baseline_best.h5\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.4773 - accuracy: 0.8763\n",
      "\n",
      "Test Accuracy: 87.63%\n",
      "Test Loss: 0.4773\n",
      "Results saved to all_results.json\n"
     ]
    }
   ],
   "source": [
    "# RUN MODEL 1: BASELINE\n",
    "if RUN_MODEL_1_BASELINE and 'data' in locals():\n",
    "    model_name = \"Model_1_Baseline\"\n",
    "    print(f\"\\n{'='*60}\\nSTARTING: {model_name}\\n{'='*60}\")\n",
    "    tf.keras.backend.clear_session(); gc.collect()\n",
    "    model_1 = create_model_1_baseline()\n",
    "    model_1.summary()\n",
    "    history_1 = train_model(\n",
    "        model_1, train_ds_basic, val_ds, \n",
    "        MODEL_CONFIG[model_name][\"epochs\"], model_name, \n",
    "        MODEL_CONFIG[model_name][\"patience\"], use_lr_schedule=False\n",
    "    )\n",
    "    plot_history(history_1, model_name)\n",
    "    acc, loss = evaluate_model(f'models/{model_name}_best.h5', test_ds)\n",
    "    if acc: results[model_name] = (acc, loss); save_results(results)\n",
    "    del model_1, history_1\n",
    "else:\n",
    "    print(\"Skipping Model 1 training.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0c186642-587c-4689-aed4-6541a72a98a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "STARTING: Model_2_Improved\n",
      "============================================================\n",
      "Model: \"Model_2_Improved\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 32, 32, 64)        1792      \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 32, 32, 64)       256       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 32, 32, 64)        36928     \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 32, 32, 64)       256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 16, 16, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 16, 16, 64)        0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 16, 16, 128)       73856     \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 16, 16, 128)      512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 16, 16, 128)       147584    \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 16, 16, 128)      512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 8, 8, 128)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 8, 8, 128)         0         \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 8, 8, 256)         295168    \n",
      "                                                                 \n",
      " batch_normalization_4 (Batc  (None, 8, 8, 256)        1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 8, 8, 256)         590080    \n",
      "                                                                 \n",
      " batch_normalization_5 (Batc  (None, 8, 8, 256)        1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 4, 4, 256)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 4, 4, 256)         0         \n",
      "                                                                 \n",
      " global_average_pooling2d (G  (None, 256)              0         \n",
      " lobalAveragePooling2D)                                          \n",
      "                                                                 \n",
      " dense (Dense)               (None, 256)               65792     \n",
      "                                                                 \n",
      " batch_normalization_6 (Batc  (None, 256)              1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                2570      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,218,378\n",
      "Trainable params: 1,216,074\n",
      "Non-trainable params: 2,304\n",
      "_________________________________________________________________\n",
      "Using ReduceLROnPlateau scheduler for Model_2_Improved.\n",
      "\n",
      "--- Training Model_2_Improved for 100 epochs ---\n",
      "Epoch 1/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 1.8715 - accuracy: 0.3720\n",
      "Epoch 1: val_accuracy improved from -inf to 0.10680, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 9s 17ms/step - loss: 1.8715 - accuracy: 0.3720 - val_loss: 3.0519 - val_accuracy: 0.1068 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 1.1660 - accuracy: 0.5846\n",
      "Epoch 2: val_accuracy improved from 0.10680 to 0.65700, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 1.1639 - accuracy: 0.5853 - val_loss: 0.9820 - val_accuracy: 0.6570 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.9399 - accuracy: 0.6701\n",
      "Epoch 3: val_accuracy improved from 0.65700 to 0.70160, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.9392 - accuracy: 0.6701 - val_loss: 0.8337 - val_accuracy: 0.7016 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.7912 - accuracy: 0.7228\n",
      "Epoch 4: val_accuracy improved from 0.70160 to 0.76840, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.7914 - accuracy: 0.7228 - val_loss: 0.6815 - val_accuracy: 0.7684 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.6931 - accuracy: 0.7588\n",
      "Epoch 5: val_accuracy improved from 0.76840 to 0.78260, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.6927 - accuracy: 0.7589 - val_loss: 0.6527 - val_accuracy: 0.7826 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.6241 - accuracy: 0.7850\n",
      "Epoch 6: val_accuracy did not improve from 0.78260\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.6241 - accuracy: 0.7850 - val_loss: 0.8775 - val_accuracy: 0.7160 - lr: 0.0010\n",
      "Epoch 7/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.5795 - accuracy: 0.8011\n",
      "Epoch 7: val_accuracy improved from 0.78260 to 0.80280, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.5793 - accuracy: 0.8012 - val_loss: 0.5870 - val_accuracy: 0.8028 - lr: 0.0010\n",
      "Epoch 8/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.5332 - accuracy: 0.8200\n",
      "Epoch 8: val_accuracy improved from 0.80280 to 0.83180, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.5332 - accuracy: 0.8200 - val_loss: 0.5044 - val_accuracy: 0.8318 - lr: 0.0010\n",
      "Epoch 9/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.4995 - accuracy: 0.8286\n",
      "Epoch 9: val_accuracy did not improve from 0.83180\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.5000 - accuracy: 0.8285 - val_loss: 0.7166 - val_accuracy: 0.7624 - lr: 0.0010\n",
      "Epoch 10/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.4727 - accuracy: 0.8382\n",
      "Epoch 10: val_accuracy improved from 0.83180 to 0.83380, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.4727 - accuracy: 0.8382 - val_loss: 0.4891 - val_accuracy: 0.8338 - lr: 0.0010\n",
      "Epoch 11/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4445 - accuracy: 0.8481\n",
      "Epoch 11: val_accuracy did not improve from 0.83380\n",
      "352/352 [==============================] - 5s 13ms/step - loss: 0.4448 - accuracy: 0.8479 - val_loss: 0.6033 - val_accuracy: 0.8150 - lr: 0.0010\n",
      "Epoch 12/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.4220 - accuracy: 0.8561\n",
      "Epoch 12: val_accuracy improved from 0.83380 to 0.85780, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.4220 - accuracy: 0.8561 - val_loss: 0.4146 - val_accuracy: 0.8578 - lr: 0.0010\n",
      "Epoch 13/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4126 - accuracy: 0.8596\n",
      "Epoch 13: val_accuracy did not improve from 0.85780\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.4127 - accuracy: 0.8596 - val_loss: 0.4654 - val_accuracy: 0.8456 - lr: 0.0010\n",
      "Epoch 14/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3963 - accuracy: 0.8638\n",
      "Epoch 14: val_accuracy did not improve from 0.85780\n",
      "352/352 [==============================] - 5s 13ms/step - loss: 0.3960 - accuracy: 0.8638 - val_loss: 0.4978 - val_accuracy: 0.8406 - lr: 0.0010\n",
      "Epoch 15/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.3715 - accuracy: 0.8731\n",
      "Epoch 15: val_accuracy did not improve from 0.85780\n",
      "352/352 [==============================] - 5s 13ms/step - loss: 0.3718 - accuracy: 0.8728 - val_loss: 0.4388 - val_accuracy: 0.8540 - lr: 0.0010\n",
      "Epoch 16/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3633 - accuracy: 0.8756\n",
      "Epoch 16: val_accuracy did not improve from 0.85780\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.3633 - accuracy: 0.8756 - val_loss: 0.4383 - val_accuracy: 0.8530 - lr: 0.0010\n",
      "Epoch 17/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3525 - accuracy: 0.8780\n",
      "Epoch 17: val_accuracy did not improve from 0.85780\n",
      "\n",
      "Epoch 17: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "352/352 [==============================] - 5s 13ms/step - loss: 0.3526 - accuracy: 0.8780 - val_loss: 0.4634 - val_accuracy: 0.8492 - lr: 0.0010\n",
      "Epoch 18/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.2954 - accuracy: 0.8983\n",
      "Epoch 18: val_accuracy improved from 0.85780 to 0.88080, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.2956 - accuracy: 0.8982 - val_loss: 0.3658 - val_accuracy: 0.8808 - lr: 5.0000e-04\n",
      "Epoch 19/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.2722 - accuracy: 0.9051\n",
      "Epoch 19: val_accuracy did not improve from 0.88080\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.2721 - accuracy: 0.9053 - val_loss: 0.3713 - val_accuracy: 0.8796 - lr: 5.0000e-04\n",
      "Epoch 20/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2679 - accuracy: 0.9069\n",
      "Epoch 20: val_accuracy improved from 0.88080 to 0.89460, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.2679 - accuracy: 0.9069 - val_loss: 0.3210 - val_accuracy: 0.8946 - lr: 5.0000e-04\n",
      "Epoch 21/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.2549 - accuracy: 0.9115\n",
      "Epoch 21: val_accuracy did not improve from 0.89460\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.2545 - accuracy: 0.9116 - val_loss: 0.3410 - val_accuracy: 0.8892 - lr: 5.0000e-04\n",
      "Epoch 22/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2464 - accuracy: 0.9142\n",
      "Epoch 22: val_accuracy did not improve from 0.89460\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.2469 - accuracy: 0.9142 - val_loss: 0.3266 - val_accuracy: 0.8932 - lr: 5.0000e-04\n",
      "Epoch 23/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.2377 - accuracy: 0.9157\n",
      "Epoch 23: val_accuracy did not improve from 0.89460\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.2380 - accuracy: 0.9157 - val_loss: 0.3453 - val_accuracy: 0.8860 - lr: 5.0000e-04\n",
      "Epoch 24/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2359 - accuracy: 0.9175\n",
      "Epoch 24: val_accuracy improved from 0.89460 to 0.89680, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.2358 - accuracy: 0.9176 - val_loss: 0.3208 - val_accuracy: 0.8968 - lr: 5.0000e-04\n",
      "Epoch 25/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2299 - accuracy: 0.9201\n",
      "Epoch 25: val_accuracy did not improve from 0.89680\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.2299 - accuracy: 0.9201 - val_loss: 0.3249 - val_accuracy: 0.8942 - lr: 5.0000e-04\n",
      "Epoch 26/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2246 - accuracy: 0.9220\n",
      "Epoch 26: val_accuracy did not improve from 0.89680\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.2246 - accuracy: 0.9220 - val_loss: 0.3253 - val_accuracy: 0.8950 - lr: 5.0000e-04\n",
      "Epoch 27/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2146 - accuracy: 0.9260\n",
      "Epoch 27: val_accuracy did not improve from 0.89680\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.2147 - accuracy: 0.9260 - val_loss: 0.3479 - val_accuracy: 0.8924 - lr: 5.0000e-04\n",
      "Epoch 28/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.2181 - accuracy: 0.9242\n",
      "Epoch 28: val_accuracy did not improve from 0.89680\n",
      "352/352 [==============================] - 5s 13ms/step - loss: 0.2180 - accuracy: 0.9242 - val_loss: 0.3365 - val_accuracy: 0.8936 - lr: 5.0000e-04\n",
      "Epoch 29/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2053 - accuracy: 0.9287\n",
      "Epoch 29: val_accuracy did not improve from 0.89680\n",
      "\n",
      "Epoch 29: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.2053 - accuracy: 0.9287 - val_loss: 0.3423 - val_accuracy: 0.8938 - lr: 5.0000e-04\n",
      "Epoch 30/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1820 - accuracy: 0.9365\n",
      "Epoch 30: val_accuracy improved from 0.89680 to 0.90420, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1820 - accuracy: 0.9365 - val_loss: 0.3051 - val_accuracy: 0.9042 - lr: 2.5000e-04\n",
      "Epoch 31/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1740 - accuracy: 0.9391\n",
      "Epoch 31: val_accuracy improved from 0.90420 to 0.90580, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1740 - accuracy: 0.9391 - val_loss: 0.3127 - val_accuracy: 0.9058 - lr: 2.5000e-04\n",
      "Epoch 32/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1703 - accuracy: 0.9418\n",
      "Epoch 32: val_accuracy did not improve from 0.90580\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1702 - accuracy: 0.9417 - val_loss: 0.3075 - val_accuracy: 0.9044 - lr: 2.5000e-04\n",
      "Epoch 33/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1648 - accuracy: 0.9421\n",
      "Epoch 33: val_accuracy did not improve from 0.90580\n",
      "352/352 [==============================] - 5s 13ms/step - loss: 0.1647 - accuracy: 0.9421 - val_loss: 0.3086 - val_accuracy: 0.9050 - lr: 2.5000e-04\n",
      "Epoch 34/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1591 - accuracy: 0.9441\n",
      "Epoch 34: val_accuracy did not improve from 0.90580\n",
      "352/352 [==============================] - 5s 13ms/step - loss: 0.1593 - accuracy: 0.9440 - val_loss: 0.3085 - val_accuracy: 0.9056 - lr: 2.5000e-04\n",
      "Epoch 35/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1581 - accuracy: 0.9454\n",
      "Epoch 35: val_accuracy improved from 0.90580 to 0.90840, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1580 - accuracy: 0.9454 - val_loss: 0.3008 - val_accuracy: 0.9084 - lr: 2.5000e-04\n",
      "Epoch 36/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.1553 - accuracy: 0.9463\n",
      "Epoch 36: val_accuracy did not improve from 0.90840\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1551 - accuracy: 0.9465 - val_loss: 0.3087 - val_accuracy: 0.9056 - lr: 2.5000e-04\n",
      "Epoch 37/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1489 - accuracy: 0.9463\n",
      "Epoch 37: val_accuracy did not improve from 0.90840\n",
      "352/352 [==============================] - 5s 13ms/step - loss: 0.1492 - accuracy: 0.9462 - val_loss: 0.3135 - val_accuracy: 0.9052 - lr: 2.5000e-04\n",
      "Epoch 38/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1520 - accuracy: 0.9470\n",
      "Epoch 38: val_accuracy did not improve from 0.90840\n",
      "352/352 [==============================] - 5s 13ms/step - loss: 0.1519 - accuracy: 0.9470 - val_loss: 0.3076 - val_accuracy: 0.9072 - lr: 2.5000e-04\n",
      "Epoch 39/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1434 - accuracy: 0.9488\n",
      "Epoch 39: val_accuracy improved from 0.90840 to 0.91000, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1434 - accuracy: 0.9488 - val_loss: 0.3017 - val_accuracy: 0.9100 - lr: 2.5000e-04\n",
      "Epoch 40/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1445 - accuracy: 0.9496\n",
      "Epoch 40: val_accuracy did not improve from 0.91000\n",
      "\n",
      "Epoch 40: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1442 - accuracy: 0.9496 - val_loss: 0.3173 - val_accuracy: 0.9098 - lr: 2.5000e-04\n",
      "Epoch 41/100\n",
      "348/352 [============================>.] - ETA: 0s - loss: 0.1361 - accuracy: 0.9513\n",
      "Epoch 41: val_accuracy improved from 0.91000 to 0.91280, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1361 - accuracy: 0.9512 - val_loss: 0.3025 - val_accuracy: 0.9128 - lr: 1.2500e-04\n",
      "Epoch 42/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1292 - accuracy: 0.9549\n",
      "Epoch 42: val_accuracy improved from 0.91280 to 0.91300, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1292 - accuracy: 0.9549 - val_loss: 0.2908 - val_accuracy: 0.9130 - lr: 1.2500e-04\n",
      "Epoch 43/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1239 - accuracy: 0.9559\n",
      "Epoch 43: val_accuracy improved from 0.91300 to 0.91700, saving model to models\\Model_2_Improved_best.h5\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1240 - accuracy: 0.9559 - val_loss: 0.2965 - val_accuracy: 0.9170 - lr: 1.2500e-04\n",
      "Epoch 44/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1243 - accuracy: 0.9563\n",
      "Epoch 44: val_accuracy did not improve from 0.91700\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1244 - accuracy: 0.9563 - val_loss: 0.2973 - val_accuracy: 0.9166 - lr: 1.2500e-04\n",
      "Epoch 45/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1210 - accuracy: 0.9575\n",
      "Epoch 45: val_accuracy did not improve from 0.91700\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1210 - accuracy: 0.9575 - val_loss: 0.3059 - val_accuracy: 0.9144 - lr: 1.2500e-04\n",
      "Epoch 46/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1182 - accuracy: 0.9584\n",
      "Epoch 46: val_accuracy did not improve from 0.91700\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1182 - accuracy: 0.9584 - val_loss: 0.2992 - val_accuracy: 0.9152 - lr: 1.2500e-04\n",
      "Epoch 47/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1164 - accuracy: 0.9591\n",
      "Epoch 47: val_accuracy did not improve from 0.91700\n",
      "\n",
      "Epoch 47: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1163 - accuracy: 0.9591 - val_loss: 0.3041 - val_accuracy: 0.9158 - lr: 1.2500e-04\n",
      "Epoch 48/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1150 - accuracy: 0.9605\n",
      "Epoch 48: val_accuracy did not improve from 0.91700\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1148 - accuracy: 0.9606 - val_loss: 0.3021 - val_accuracy: 0.9134 - lr: 6.2500e-05\n",
      "Epoch 49/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1090 - accuracy: 0.9629\n",
      "Epoch 49: val_accuracy did not improve from 0.91700\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1088 - accuracy: 0.9629 - val_loss: 0.3048 - val_accuracy: 0.9154 - lr: 6.2500e-05\n",
      "Epoch 50/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1066 - accuracy: 0.9621\n",
      "Epoch 50: val_accuracy did not improve from 0.91700\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1066 - accuracy: 0.9621 - val_loss: 0.3073 - val_accuracy: 0.9122 - lr: 6.2500e-05\n",
      "Epoch 51/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1081 - accuracy: 0.9626\n",
      "Epoch 51: val_accuracy did not improve from 0.91700\n",
      "352/352 [==============================] - 5s 13ms/step - loss: 0.1082 - accuracy: 0.9625 - val_loss: 0.3024 - val_accuracy: 0.9168 - lr: 6.2500e-05\n",
      "Epoch 52/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1109 - accuracy: 0.9608\n",
      "Epoch 52: val_accuracy did not improve from 0.91700\n",
      "\n",
      "Epoch 52: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1109 - accuracy: 0.9608 - val_loss: 0.3043 - val_accuracy: 0.9140 - lr: 6.2500e-05\n",
      "Epoch 53/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1058 - accuracy: 0.9630\n",
      "Epoch 53: val_accuracy did not improve from 0.91700\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1058 - accuracy: 0.9630 - val_loss: 0.3007 - val_accuracy: 0.9164 - lr: 3.1250e-05\n",
      "Epoch 54/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1066 - accuracy: 0.9628\n",
      "Epoch 54: val_accuracy did not improve from 0.91700\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1068 - accuracy: 0.9628 - val_loss: 0.3023 - val_accuracy: 0.9162 - lr: 3.1250e-05\n",
      "Epoch 55/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1031 - accuracy: 0.9641\n",
      "Epoch 55: val_accuracy did not improve from 0.91700\n",
      "352/352 [==============================] - 5s 15ms/step - loss: 0.1031 - accuracy: 0.9641 - val_loss: 0.3009 - val_accuracy: 0.9154 - lr: 3.1250e-05\n",
      "Epoch 56/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1009 - accuracy: 0.9646\n",
      "Epoch 56: val_accuracy did not improve from 0.91700\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1007 - accuracy: 0.9647 - val_loss: 0.3035 - val_accuracy: 0.9168 - lr: 3.1250e-05\n",
      "Epoch 57/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1034 - accuracy: 0.9638\n",
      "Epoch 57: val_accuracy did not improve from 0.91700\n",
      "\n",
      "Epoch 57: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.1034 - accuracy: 0.9638 - val_loss: 0.3030 - val_accuracy: 0.9168 - lr: 3.1250e-05\n",
      "Epoch 58/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.0997 - accuracy: 0.9643\n",
      "Epoch 58: val_accuracy did not improve from 0.91700\n",
      "Restoring model weights from the end of the best epoch: 43.\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 0.0997 - accuracy: 0.9643 - val_loss: 0.3014 - val_accuracy: 0.9160 - lr: 1.5625e-05\n",
      "Epoch 58: early stopping\n",
      "Training finished in: 0:04:45.881570\n",
      "Successfully saved history to models/Model_2_Improved_history.pkl\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "EVALUATING: models/Model_2_Improved_best.h5\n",
      "============================================================\n",
      "Loaded best weights from models/Model_2_Improved_best.h5\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.3215 - accuracy: 0.9073\n",
      "\n",
      "Test Accuracy: 90.73%\n",
      "Test Loss: 0.3215\n",
      "Results saved to all_results.json\n"
     ]
    }
   ],
   "source": [
    "# 4. RUN MODEL 2: IMPROVED\n",
    "if RUN_MODEL_2_IMPROVED and 'data' in locals():\n",
    "    model_name = \"Model_2_Improved\"\n",
    "    print(f\"\\n{'='*60}\\nSTARTING: {model_name}\\n{'='*60}\")\n",
    "    tf.keras.backend.clear_session(); gc.collect()\n",
    "    model_2 = create_model_2_improved()\n",
    "    model_2.summary()\n",
    "    history_2 = train_model(\n",
    "        model_2, train_ds_augmented, val_ds, \n",
    "        MODEL_CONFIG[model_name][\"epochs\"], model_name, \n",
    "        MODEL_CONFIG[model_name][\"patience\"], use_lr_schedule=False\n",
    "    )\n",
    "    plot_history(history_2, model_name)\n",
    "    acc, loss = evaluate_model(f'models/{model_name}_best.h5', test_ds)\n",
    "    if acc: results[model_name] = (acc, loss); save_results(results)\n",
    "    del model_2, history_2\n",
    "else:\n",
    "    print(\"Skipping Model 2 training.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2a9aa24d-6487-451f-be82-0a78360fcf67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "STARTING: Model_3_SOTA_100e\n",
      "============================================================\n",
      "Model: \"Model_3_SOTA_100e\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 32, 32, 3)]  0           []                               \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 32, 32, 64)   1792        ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 32, 32, 64)  256         ['conv2d[0][0]']                 \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 32, 32, 64)   0           ['batch_normalization[0][0]']    \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 32, 32, 64)   36928       ['activation[0][0]']             \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 32, 32, 64)  256         ['conv2d_1[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 32, 32, 64)   0           ['batch_normalization_1[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 32, 32, 64)   36928       ['activation_1[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 32, 32, 64)  256         ['conv2d_2[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add (Add)                      (None, 32, 32, 64)   0           ['batch_normalization_2[0][0]',  \n",
      "                                                                  'activation[0][0]']             \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 32, 32, 64)   0           ['add[0][0]']                    \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 16, 16, 64)   0           ['activation_2[0][0]']           \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 16, 16, 64)   0           ['max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 16, 16, 128)  73856       ['dropout[0][0]']                \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 16, 16, 128)  512        ['conv2d_3[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 16, 16, 128)  0           ['batch_normalization_3[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 16, 16, 128)  147584      ['activation_3[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 16, 16, 128)  8320        ['dropout[0][0]']                \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 16, 16, 128)  512        ['conv2d_4[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_5 (BatchNo  (None, 16, 16, 128)  512        ['conv2d_5[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add_1 (Add)                    (None, 16, 16, 128)  0           ['batch_normalization_4[0][0]',  \n",
      "                                                                  'batch_normalization_5[0][0]']  \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, 16, 16, 128)  0           ['add_1[0][0]']                  \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 8, 8, 128)   0           ['activation_4[0][0]']           \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, 8, 8, 128)    0           ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 8, 8, 256)    295168      ['dropout_1[0][0]']              \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 8, 8, 256)   1024        ['conv2d_6[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, 8, 8, 256)    0           ['batch_normalization_6[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, 8, 8, 256)    590080      ['activation_5[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)              (None, 8, 8, 256)    33024       ['dropout_1[0][0]']              \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, 8, 8, 256)   1024        ['conv2d_7[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_8 (BatchNo  (None, 8, 8, 256)   1024        ['conv2d_8[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add_2 (Add)                    (None, 8, 8, 256)    0           ['batch_normalization_7[0][0]',  \n",
      "                                                                  'batch_normalization_8[0][0]']  \n",
      "                                                                                                  \n",
      " activation_6 (Activation)      (None, 8, 8, 256)    0           ['add_2[0][0]']                  \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 4, 4, 256)   0           ['activation_6[0][0]']           \n",
      "                                                                                                  \n",
      " dropout_2 (Dropout)            (None, 4, 4, 256)    0           ['max_pooling2d_2[0][0]']        \n",
      "                                                                                                  \n",
      " global_average_pooling2d (Glob  (None, 256)         0           ['dropout_2[0][0]']              \n",
      " alAveragePooling2D)                                                                              \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 256)          65792       ['global_average_pooling2d[0][0]'\n",
      "                                                                 ]                                \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, 256)         1024        ['dense[0][0]']                  \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " dropout_3 (Dropout)            (None, 256)          0           ['batch_normalization_9[0][0]']  \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 10)           2570        ['dropout_3[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 1,298,442\n",
      "Trainable params: 1,295,242\n",
      "Non-trainable params: 3,200\n",
      "__________________________________________________________________________________________________\n",
      "Using custom learning rate schedule for Model_3_SOTA_100e.\n",
      "\n",
      "--- Training Model_3_SOTA_100e for 100 epochs ---\n",
      "Epoch 1/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 2.6835 - accuracy: 0.2145\n",
      "Epoch 1: val_accuracy improved from -inf to 0.16040, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 9s 21ms/step - loss: 2.6835 - accuracy: 0.2145 - val_loss: 2.8243 - val_accuracy: 0.1604\n",
      "Epoch 2/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 1.9767 - accuracy: 0.3551\n",
      "Epoch 2: val_accuracy improved from 0.16040 to 0.38900, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 1.9760 - accuracy: 0.3554 - val_loss: 1.9036 - val_accuracy: 0.3890\n",
      "Epoch 3/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 1.6241 - accuracy: 0.4593\n",
      "Epoch 3: val_accuracy improved from 0.38900 to 0.49060, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 1.6230 - accuracy: 0.4597 - val_loss: 1.5542 - val_accuracy: 0.4906\n",
      "Epoch 4/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 1.3677 - accuracy: 0.5536\n",
      "Epoch 4: val_accuracy did not improve from 0.49060\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 1.3677 - accuracy: 0.5536 - val_loss: 1.6619 - val_accuracy: 0.4842\n",
      "Epoch 5/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 1.2346 - accuracy: 0.6050\n",
      "Epoch 5: val_accuracy did not improve from 0.49060\n",
      "352/352 [==============================] - 7s 21ms/step - loss: 1.2346 - accuracy: 0.6050 - val_loss: 1.7409 - val_accuracy: 0.4896\n",
      "Epoch 6/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 1.1227 - accuracy: 0.6469\n",
      "Epoch 6: val_accuracy improved from 0.49060 to 0.56280, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 21ms/step - loss: 1.1226 - accuracy: 0.6470 - val_loss: 1.5435 - val_accuracy: 0.5628\n",
      "Epoch 7/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 1.0286 - accuracy: 0.6850\n",
      "Epoch 7: val_accuracy improved from 0.56280 to 0.72160, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 1.0286 - accuracy: 0.6850 - val_loss: 0.9343 - val_accuracy: 0.7216\n",
      "Epoch 8/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.9594 - accuracy: 0.7117\n",
      "Epoch 8: val_accuracy did not improve from 0.72160\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.9593 - accuracy: 0.7118 - val_loss: 1.3074 - val_accuracy: 0.6082\n",
      "Epoch 9/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.8938 - accuracy: 0.7345\n",
      "Epoch 9: val_accuracy did not improve from 0.72160\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.8937 - accuracy: 0.7345 - val_loss: 1.0287 - val_accuracy: 0.6856\n",
      "Epoch 10/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.8475 - accuracy: 0.7537\n",
      "Epoch 10: val_accuracy did not improve from 0.72160\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.8475 - accuracy: 0.7537 - val_loss: 1.3472 - val_accuracy: 0.5918\n",
      "Epoch 11/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.8107 - accuracy: 0.7707\n",
      "Epoch 11: val_accuracy did not improve from 0.72160\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.8106 - accuracy: 0.7707 - val_loss: 0.9490 - val_accuracy: 0.7194\n",
      "Epoch 12/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.7792 - accuracy: 0.7815\n",
      "Epoch 12: val_accuracy did not improve from 0.72160\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.7792 - accuracy: 0.7815 - val_loss: 1.0169 - val_accuracy: 0.6894\n",
      "Epoch 13/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.7454 - accuracy: 0.7963\n",
      "Epoch 13: val_accuracy improved from 0.72160 to 0.74700, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.7456 - accuracy: 0.7964 - val_loss: 0.9407 - val_accuracy: 0.7470\n",
      "Epoch 14/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.7292 - accuracy: 0.8026\n",
      "Epoch 14: val_accuracy improved from 0.74700 to 0.77640, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.7296 - accuracy: 0.8023 - val_loss: 0.8288 - val_accuracy: 0.7764\n",
      "Epoch 15/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.7095 - accuracy: 0.8123\n",
      "Epoch 15: val_accuracy improved from 0.77640 to 0.81180, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.7091 - accuracy: 0.8124 - val_loss: 0.7041 - val_accuracy: 0.8118\n",
      "Epoch 16/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.6879 - accuracy: 0.8224\n",
      "Epoch 16: val_accuracy did not improve from 0.81180\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.6878 - accuracy: 0.8224 - val_loss: 1.1456 - val_accuracy: 0.6990\n",
      "Epoch 17/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.6706 - accuracy: 0.8271\n",
      "Epoch 17: val_accuracy did not improve from 0.81180\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.6704 - accuracy: 0.8271 - val_loss: 0.9873 - val_accuracy: 0.7404\n",
      "Epoch 18/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.6626 - accuracy: 0.8330\n",
      "Epoch 18: val_accuracy did not improve from 0.81180\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.6628 - accuracy: 0.8330 - val_loss: 0.9389 - val_accuracy: 0.7542\n",
      "Epoch 19/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.6502 - accuracy: 0.8388\n",
      "Epoch 19: val_accuracy improved from 0.81180 to 0.82440, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.6502 - accuracy: 0.8389 - val_loss: 0.6908 - val_accuracy: 0.8244\n",
      "Epoch 20/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.6335 - accuracy: 0.8441\n",
      "Epoch 20: val_accuracy did not improve from 0.82440\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.6335 - accuracy: 0.8441 - val_loss: 0.8577 - val_accuracy: 0.7876\n",
      "Epoch 21/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.6271 - accuracy: 0.8475\n",
      "Epoch 21: val_accuracy did not improve from 0.82440\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.6274 - accuracy: 0.8474 - val_loss: 0.8268 - val_accuracy: 0.7914\n",
      "Epoch 22/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.6158 - accuracy: 0.8550\n",
      "Epoch 22: val_accuracy did not improve from 0.82440\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.6161 - accuracy: 0.8550 - val_loss: 0.8018 - val_accuracy: 0.7966\n",
      "Epoch 23/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.6112 - accuracy: 0.8557\n",
      "Epoch 23: val_accuracy improved from 0.82440 to 0.84940, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.6112 - accuracy: 0.8557 - val_loss: 0.6317 - val_accuracy: 0.8494\n",
      "Epoch 24/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.5979 - accuracy: 0.8609\n",
      "Epoch 24: val_accuracy did not improve from 0.84940\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5975 - accuracy: 0.8610 - val_loss: 0.7019 - val_accuracy: 0.8380\n",
      "Epoch 25/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.5900 - accuracy: 0.8631\n",
      "Epoch 25: val_accuracy did not improve from 0.84940\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5900 - accuracy: 0.8631 - val_loss: 0.7072 - val_accuracy: 0.8286\n",
      "Epoch 26/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.5808 - accuracy: 0.8702\n",
      "Epoch 26: val_accuracy did not improve from 0.84940\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5809 - accuracy: 0.8702 - val_loss: 0.6889 - val_accuracy: 0.8324\n",
      "Epoch 27/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.5703 - accuracy: 0.8734\n",
      "Epoch 27: val_accuracy improved from 0.84940 to 0.85640, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5703 - accuracy: 0.8734 - val_loss: 0.6245 - val_accuracy: 0.8564\n",
      "Epoch 28/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.5679 - accuracy: 0.8758\n",
      "Epoch 28: val_accuracy did not improve from 0.85640\n",
      "352/352 [==============================] - 7s 21ms/step - loss: 0.5676 - accuracy: 0.8759 - val_loss: 0.6558 - val_accuracy: 0.8512\n",
      "Epoch 29/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.5565 - accuracy: 0.8804\n",
      "Epoch 29: val_accuracy did not improve from 0.85640\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5565 - accuracy: 0.8804 - val_loss: 0.6761 - val_accuracy: 0.8418\n",
      "Epoch 30/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.5573 - accuracy: 0.8800\n",
      "Epoch 30: val_accuracy improved from 0.85640 to 0.85720, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5568 - accuracy: 0.8801 - val_loss: 0.6197 - val_accuracy: 0.8572\n",
      "Epoch 31/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.5449 - accuracy: 0.8836\n",
      "Epoch 31: val_accuracy improved from 0.85720 to 0.86560, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5449 - accuracy: 0.8836 - val_loss: 0.6186 - val_accuracy: 0.8656\n",
      "Epoch 32/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.5358 - accuracy: 0.8887\n",
      "Epoch 32: val_accuracy did not improve from 0.86560\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5361 - accuracy: 0.8887 - val_loss: 0.6895 - val_accuracy: 0.8440\n",
      "Epoch 33/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.5312 - accuracy: 0.8898\n",
      "Epoch 33: val_accuracy did not improve from 0.86560\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5309 - accuracy: 0.8899 - val_loss: 0.6154 - val_accuracy: 0.8608\n",
      "Epoch 34/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.5337 - accuracy: 0.8907\n",
      "Epoch 34: val_accuracy did not improve from 0.86560\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5334 - accuracy: 0.8907 - val_loss: 0.6413 - val_accuracy: 0.8636\n",
      "Epoch 35/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.5161 - accuracy: 0.8969\n",
      "Epoch 35: val_accuracy improved from 0.86560 to 0.88200, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5161 - accuracy: 0.8969 - val_loss: 0.5724 - val_accuracy: 0.8820\n",
      "Epoch 36/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.5111 - accuracy: 0.8981\n",
      "Epoch 36: val_accuracy improved from 0.88200 to 0.88560, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5111 - accuracy: 0.8981 - val_loss: 0.5542 - val_accuracy: 0.8856\n",
      "Epoch 37/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.5050 - accuracy: 0.8999\n",
      "Epoch 37: val_accuracy did not improve from 0.88560\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5052 - accuracy: 0.8998 - val_loss: 0.6197 - val_accuracy: 0.8712\n",
      "Epoch 38/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.5002 - accuracy: 0.9034\n",
      "Epoch 38: val_accuracy did not improve from 0.88560\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5001 - accuracy: 0.9034 - val_loss: 0.6637 - val_accuracy: 0.8590\n",
      "Epoch 39/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4945 - accuracy: 0.9032\n",
      "Epoch 39: val_accuracy did not improve from 0.88560\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4944 - accuracy: 0.9032 - val_loss: 0.6680 - val_accuracy: 0.8634\n",
      "Epoch 40/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.4868 - accuracy: 0.9048\n",
      "Epoch 40: val_accuracy did not improve from 0.88560\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4866 - accuracy: 0.9048 - val_loss: 0.6220 - val_accuracy: 0.8706\n",
      "Epoch 41/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4829 - accuracy: 0.9066\n",
      "Epoch 41: val_accuracy did not improve from 0.88560\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4828 - accuracy: 0.9066 - val_loss: 0.6034 - val_accuracy: 0.8706\n",
      "Epoch 42/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4745 - accuracy: 0.9103\n",
      "Epoch 42: val_accuracy did not improve from 0.88560\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4746 - accuracy: 0.9102 - val_loss: 0.5660 - val_accuracy: 0.8830\n",
      "Epoch 43/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.4658 - accuracy: 0.9123\n",
      "Epoch 43: val_accuracy improved from 0.88560 to 0.88780, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4657 - accuracy: 0.9124 - val_loss: 0.5551 - val_accuracy: 0.8878\n",
      "Epoch 44/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4599 - accuracy: 0.9162\n",
      "Epoch 44: val_accuracy improved from 0.88780 to 0.89300, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4598 - accuracy: 0.9163 - val_loss: 0.5462 - val_accuracy: 0.8930\n",
      "Epoch 45/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.4544 - accuracy: 0.9162\n",
      "Epoch 45: val_accuracy improved from 0.89300 to 0.89320, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4546 - accuracy: 0.9161 - val_loss: 0.5536 - val_accuracy: 0.8932\n",
      "Epoch 46/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4508 - accuracy: 0.9160\n",
      "Epoch 46: val_accuracy did not improve from 0.89320\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4507 - accuracy: 0.9160 - val_loss: 0.5519 - val_accuracy: 0.8912\n",
      "Epoch 47/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.4401 - accuracy: 0.9203\n",
      "Epoch 47: val_accuracy did not improve from 0.89320\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4401 - accuracy: 0.9203 - val_loss: 0.5497 - val_accuracy: 0.8928\n",
      "Epoch 48/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.4333 - accuracy: 0.9226\n",
      "Epoch 48: val_accuracy did not improve from 0.89320\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4330 - accuracy: 0.9227 - val_loss: 0.5937 - val_accuracy: 0.8820\n",
      "Epoch 49/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.4254 - accuracy: 0.9261\n",
      "Epoch 49: val_accuracy improved from 0.89320 to 0.89400, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4254 - accuracy: 0.9261 - val_loss: 0.5436 - val_accuracy: 0.8940\n",
      "Epoch 50/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4190 - accuracy: 0.9265\n",
      "Epoch 50: val_accuracy did not improve from 0.89400\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4192 - accuracy: 0.9265 - val_loss: 0.5608 - val_accuracy: 0.8906\n",
      "Epoch 51/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.4167 - accuracy: 0.9282\n",
      "Epoch 51: val_accuracy improved from 0.89400 to 0.90280, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4166 - accuracy: 0.9282 - val_loss: 0.5227 - val_accuracy: 0.9028\n",
      "Epoch 52/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4057 - accuracy: 0.9294\n",
      "Epoch 52: val_accuracy did not improve from 0.90280\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4057 - accuracy: 0.9293 - val_loss: 0.5503 - val_accuracy: 0.8916\n",
      "Epoch 53/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.3991 - accuracy: 0.9324\n",
      "Epoch 53: val_accuracy did not improve from 0.90280\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3992 - accuracy: 0.9324 - val_loss: 0.5208 - val_accuracy: 0.8994\n",
      "Epoch 54/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.3979 - accuracy: 0.9307\n",
      "Epoch 54: val_accuracy did not improve from 0.90280\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3978 - accuracy: 0.9308 - val_loss: 0.5489 - val_accuracy: 0.8970\n",
      "Epoch 55/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.3931 - accuracy: 0.9331\n",
      "Epoch 55: val_accuracy did not improve from 0.90280\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3932 - accuracy: 0.9330 - val_loss: 0.5148 - val_accuracy: 0.9022\n",
      "Epoch 56/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.3758 - accuracy: 0.9383\n",
      "Epoch 56: val_accuracy did not improve from 0.90280\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3764 - accuracy: 0.9381 - val_loss: 0.5275 - val_accuracy: 0.9012\n",
      "Epoch 57/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.3732 - accuracy: 0.9393\n",
      "Epoch 57: val_accuracy did not improve from 0.90280\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3732 - accuracy: 0.9392 - val_loss: 0.5496 - val_accuracy: 0.8958\n",
      "Epoch 58/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3660 - accuracy: 0.9406\n",
      "Epoch 58: val_accuracy improved from 0.90280 to 0.91500, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3660 - accuracy: 0.9406 - val_loss: 0.4879 - val_accuracy: 0.9150\n",
      "Epoch 59/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3605 - accuracy: 0.9424\n",
      "Epoch 59: val_accuracy did not improve from 0.91500\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3605 - accuracy: 0.9424 - val_loss: 0.4766 - val_accuracy: 0.9090\n",
      "Epoch 60/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3536 - accuracy: 0.9436\n",
      "Epoch 60: val_accuracy did not improve from 0.91500\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3536 - accuracy: 0.9436 - val_loss: 0.5172 - val_accuracy: 0.9050\n",
      "Epoch 61/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.3470 - accuracy: 0.9456\n",
      "Epoch 61: val_accuracy did not improve from 0.91500\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3472 - accuracy: 0.9456 - val_loss: 0.5297 - val_accuracy: 0.9030\n",
      "Epoch 62/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3382 - accuracy: 0.9478\n",
      "Epoch 62: val_accuracy did not improve from 0.91500\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3384 - accuracy: 0.9477 - val_loss: 0.4794 - val_accuracy: 0.9106\n",
      "Epoch 63/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3343 - accuracy: 0.9485\n",
      "Epoch 63: val_accuracy did not improve from 0.91500\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3343 - accuracy: 0.9485 - val_loss: 0.4885 - val_accuracy: 0.9116\n",
      "Epoch 64/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3282 - accuracy: 0.9506\n",
      "Epoch 64: val_accuracy did not improve from 0.91500\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3282 - accuracy: 0.9506 - val_loss: 0.5181 - val_accuracy: 0.9002\n",
      "Epoch 65/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3217 - accuracy: 0.9524\n",
      "Epoch 65: val_accuracy did not improve from 0.91500\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3217 - accuracy: 0.9524 - val_loss: 0.4818 - val_accuracy: 0.9122\n",
      "Epoch 66/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.3131 - accuracy: 0.9534\n",
      "Epoch 66: val_accuracy improved from 0.91500 to 0.92280, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3132 - accuracy: 0.9533 - val_loss: 0.4515 - val_accuracy: 0.9228\n",
      "Epoch 67/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3087 - accuracy: 0.9558\n",
      "Epoch 67: val_accuracy improved from 0.92280 to 0.92420, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3087 - accuracy: 0.9558 - val_loss: 0.4406 - val_accuracy: 0.9242\n",
      "Epoch 68/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.3011 - accuracy: 0.9567\n",
      "Epoch 68: val_accuracy did not improve from 0.92420\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3010 - accuracy: 0.9567 - val_loss: 0.4734 - val_accuracy: 0.9164\n",
      "Epoch 69/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2976 - accuracy: 0.9580\n",
      "Epoch 69: val_accuracy did not improve from 0.92420\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2974 - accuracy: 0.9581 - val_loss: 0.4626 - val_accuracy: 0.9182\n",
      "Epoch 70/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2870 - accuracy: 0.9608\n",
      "Epoch 70: val_accuracy did not improve from 0.92420\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2873 - accuracy: 0.9608 - val_loss: 0.4587 - val_accuracy: 0.9198\n",
      "Epoch 71/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2776 - accuracy: 0.9641\n",
      "Epoch 71: val_accuracy improved from 0.92420 to 0.92700, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2776 - accuracy: 0.9641 - val_loss: 0.4392 - val_accuracy: 0.9270\n",
      "Epoch 72/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2775 - accuracy: 0.9633\n",
      "Epoch 72: val_accuracy did not improve from 0.92700\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2775 - accuracy: 0.9633 - val_loss: 0.4705 - val_accuracy: 0.9172\n",
      "Epoch 73/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2721 - accuracy: 0.9648\n",
      "Epoch 73: val_accuracy did not improve from 0.92700\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2721 - accuracy: 0.9648 - val_loss: 0.4413 - val_accuracy: 0.9266\n",
      "Epoch 74/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2674 - accuracy: 0.9659\n",
      "Epoch 74: val_accuracy did not improve from 0.92700\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2674 - accuracy: 0.9658 - val_loss: 0.4440 - val_accuracy: 0.9244\n",
      "Epoch 75/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2619 - accuracy: 0.9672\n",
      "Epoch 75: val_accuracy did not improve from 0.92700\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2619 - accuracy: 0.9672 - val_loss: 0.4340 - val_accuracy: 0.9260\n",
      "Epoch 76/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.2535 - accuracy: 0.9691\n",
      "Epoch 76: val_accuracy did not improve from 0.92700\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2534 - accuracy: 0.9691 - val_loss: 0.4433 - val_accuracy: 0.9242\n",
      "Epoch 77/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2469 - accuracy: 0.9705\n",
      "Epoch 77: val_accuracy did not improve from 0.92700\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2469 - accuracy: 0.9705 - val_loss: 0.4470 - val_accuracy: 0.9230\n",
      "Epoch 78/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2467 - accuracy: 0.9712\n",
      "Epoch 78: val_accuracy did not improve from 0.92700\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2467 - accuracy: 0.9712 - val_loss: 0.4300 - val_accuracy: 0.9250\n",
      "Epoch 79/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2425 - accuracy: 0.9715\n",
      "Epoch 79: val_accuracy did not improve from 0.92700\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2425 - accuracy: 0.9715 - val_loss: 0.4338 - val_accuracy: 0.9266\n",
      "Epoch 80/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2358 - accuracy: 0.9738\n",
      "Epoch 80: val_accuracy improved from 0.92700 to 0.93240, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2357 - accuracy: 0.9738 - val_loss: 0.4226 - val_accuracy: 0.9324\n",
      "Epoch 81/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2343 - accuracy: 0.9744\n",
      "Epoch 81: val_accuracy did not improve from 0.93240\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2342 - accuracy: 0.9744 - val_loss: 0.4322 - val_accuracy: 0.9302\n",
      "Epoch 82/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2286 - accuracy: 0.9760\n",
      "Epoch 82: val_accuracy did not improve from 0.93240\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2287 - accuracy: 0.9760 - val_loss: 0.4220 - val_accuracy: 0.9294\n",
      "Epoch 83/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2286 - accuracy: 0.9748\n",
      "Epoch 83: val_accuracy did not improve from 0.93240\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2286 - accuracy: 0.9748 - val_loss: 0.4318 - val_accuracy: 0.9284\n",
      "Epoch 84/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2229 - accuracy: 0.9760\n",
      "Epoch 84: val_accuracy did not improve from 0.93240\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2229 - accuracy: 0.9760 - val_loss: 0.4177 - val_accuracy: 0.9316\n",
      "Epoch 85/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2194 - accuracy: 0.9781\n",
      "Epoch 85: val_accuracy did not improve from 0.93240\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2194 - accuracy: 0.9780 - val_loss: 0.4281 - val_accuracy: 0.9304\n",
      "Epoch 86/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2154 - accuracy: 0.9790\n",
      "Epoch 86: val_accuracy did not improve from 0.93240\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2154 - accuracy: 0.9790 - val_loss: 0.4219 - val_accuracy: 0.9278\n",
      "Epoch 87/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2143 - accuracy: 0.9803\n",
      "Epoch 87: val_accuracy did not improve from 0.93240\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2143 - accuracy: 0.9803 - val_loss: 0.4255 - val_accuracy: 0.9296\n",
      "Epoch 88/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2136 - accuracy: 0.9795\n",
      "Epoch 88: val_accuracy did not improve from 0.93240\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2136 - accuracy: 0.9795 - val_loss: 0.4272 - val_accuracy: 0.9316\n",
      "Epoch 89/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2105 - accuracy: 0.9806\n",
      "Epoch 89: val_accuracy improved from 0.93240 to 0.93340, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2105 - accuracy: 0.9806 - val_loss: 0.4220 - val_accuracy: 0.9334\n",
      "Epoch 90/100\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2076 - accuracy: 0.9817\n",
      "Epoch 90: val_accuracy did not improve from 0.93340\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2076 - accuracy: 0.9817 - val_loss: 0.4147 - val_accuracy: 0.9316\n",
      "Epoch 91/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2073 - accuracy: 0.9810\n",
      "Epoch 91: val_accuracy improved from 0.93340 to 0.93380, saving model to models\\Model_3_SOTA_100e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2074 - accuracy: 0.9809 - val_loss: 0.4133 - val_accuracy: 0.9338\n",
      "Epoch 92/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2095 - accuracy: 0.9804\n",
      "Epoch 92: val_accuracy did not improve from 0.93380\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2095 - accuracy: 0.9804 - val_loss: 0.4148 - val_accuracy: 0.9328\n",
      "Epoch 93/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2046 - accuracy: 0.9820\n",
      "Epoch 93: val_accuracy did not improve from 0.93380\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2046 - accuracy: 0.9820 - val_loss: 0.4136 - val_accuracy: 0.9338\n",
      "Epoch 94/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.2053 - accuracy: 0.9818\n",
      "Epoch 94: val_accuracy did not improve from 0.93380\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2053 - accuracy: 0.9818 - val_loss: 0.4149 - val_accuracy: 0.9336\n",
      "Epoch 95/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2022 - accuracy: 0.9829\n",
      "Epoch 95: val_accuracy did not improve from 0.93380\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2023 - accuracy: 0.9829 - val_loss: 0.4168 - val_accuracy: 0.9324\n",
      "Epoch 96/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2045 - accuracy: 0.9817\n",
      "Epoch 96: val_accuracy did not improve from 0.93380\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2045 - accuracy: 0.9818 - val_loss: 0.4135 - val_accuracy: 0.9328\n",
      "Epoch 97/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.2028 - accuracy: 0.9828\n",
      "Epoch 97: val_accuracy did not improve from 0.93380\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2028 - accuracy: 0.9827 - val_loss: 0.4143 - val_accuracy: 0.9320\n",
      "Epoch 98/100\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.2008 - accuracy: 0.9831\n",
      "Epoch 98: val_accuracy did not improve from 0.93380\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2008 - accuracy: 0.9831 - val_loss: 0.4136 - val_accuracy: 0.9320\n",
      "Epoch 99/100\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2027 - accuracy: 0.9823\n",
      "Epoch 99: val_accuracy did not improve from 0.93380\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2026 - accuracy: 0.9823 - val_loss: 0.4137 - val_accuracy: 0.9318\n",
      "Epoch 100/100\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2030 - accuracy: 0.9821\n",
      "Epoch 100: val_accuracy did not improve from 0.93380\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2029 - accuracy: 0.9821 - val_loss: 0.4130 - val_accuracy: 0.9316\n",
      "Training finished in: 0:11:29.335226\n",
      "Successfully saved history to models/Model_3_SOTA_100e_history.pkl\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "EVALUATING: models/Model_3_SOTA_100e_best.h5\n",
      "============================================================\n",
      "Loaded best weights from models/Model_3_SOTA_100e_best.h5\n",
      "79/79 [==============================] - 1s 6ms/step - loss: 0.4316 - accuracy: 0.9294\n",
      "\n",
      "Test Accuracy: 92.94%\n",
      "Test Loss: 0.4316\n",
      "Results saved to all_results.json\n"
     ]
    }
   ],
   "source": [
    "# 5. RUN MODEL 3: SOTA @ 100 EPOCHS\n",
    "if RUN_MODEL_3_SOTA_100 and 'data' in locals():\n",
    "    model_name = \"Model_3_SOTA_100e\"\n",
    "    epochs = MODEL_CONFIG[model_name][\"epochs\"]\n",
    "    print(f\"\\n{'='*60}\\nSTARTING: {model_name}\\n{'='*60}\")\n",
    "    tf.keras.backend.clear_session(); gc.collect()\n",
    "    model_3_100 = create_model_3_sota(train_size=train_size, total_epochs=epochs)\n",
    "    model_3_100.summary()\n",
    "    history_3_100 = train_model(\n",
    "        model_3_100, train_ds_augmented, val_ds, \n",
    "        epochs, model_name, \n",
    "        MODEL_CONFIG[model_name][\"patience\"], use_lr_schedule=True\n",
    "    )\n",
    "    plot_history(history_3_100, model_name)\n",
    "    acc, loss = evaluate_model(f'models/{model_name}_best.h5', test_ds)\n",
    "    if acc: results[model_name] = (acc, loss); save_results(results)\n",
    "    del model_3_100, history_3_100\n",
    "else:\n",
    "    print(\"Skipping Model 3 (100 Epochs) training.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b56394b6-b465-474b-8b9e-d0cd887c1b95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "STARTING: Model_3_SOTA_150e\n",
      "============================================================\n",
      "Model: \"Model_3_SOTA_150e\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 32, 32, 3)]  0           []                               \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 32, 32, 64)   1792        ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 32, 32, 64)  256         ['conv2d[0][0]']                 \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 32, 32, 64)   0           ['batch_normalization[0][0]']    \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 32, 32, 64)   36928       ['activation[0][0]']             \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 32, 32, 64)  256         ['conv2d_1[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 32, 32, 64)   0           ['batch_normalization_1[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 32, 32, 64)   36928       ['activation_1[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 32, 32, 64)  256         ['conv2d_2[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add (Add)                      (None, 32, 32, 64)   0           ['batch_normalization_2[0][0]',  \n",
      "                                                                  'activation[0][0]']             \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 32, 32, 64)   0           ['add[0][0]']                    \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 16, 16, 64)   0           ['activation_2[0][0]']           \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 16, 16, 64)   0           ['max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 16, 16, 128)  73856       ['dropout[0][0]']                \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 16, 16, 128)  512        ['conv2d_3[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 16, 16, 128)  0           ['batch_normalization_3[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 16, 16, 128)  147584      ['activation_3[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 16, 16, 128)  8320        ['dropout[0][0]']                \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 16, 16, 128)  512        ['conv2d_4[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_5 (BatchNo  (None, 16, 16, 128)  512        ['conv2d_5[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add_1 (Add)                    (None, 16, 16, 128)  0           ['batch_normalization_4[0][0]',  \n",
      "                                                                  'batch_normalization_5[0][0]']  \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, 16, 16, 128)  0           ['add_1[0][0]']                  \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 8, 8, 128)   0           ['activation_4[0][0]']           \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, 8, 8, 128)    0           ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 8, 8, 256)    295168      ['dropout_1[0][0]']              \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 8, 8, 256)   1024        ['conv2d_6[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, 8, 8, 256)    0           ['batch_normalization_6[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, 8, 8, 256)    590080      ['activation_5[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)              (None, 8, 8, 256)    33024       ['dropout_1[0][0]']              \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, 8, 8, 256)   1024        ['conv2d_7[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_8 (BatchNo  (None, 8, 8, 256)   1024        ['conv2d_8[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add_2 (Add)                    (None, 8, 8, 256)    0           ['batch_normalization_7[0][0]',  \n",
      "                                                                  'batch_normalization_8[0][0]']  \n",
      "                                                                                                  \n",
      " activation_6 (Activation)      (None, 8, 8, 256)    0           ['add_2[0][0]']                  \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 4, 4, 256)   0           ['activation_6[0][0]']           \n",
      "                                                                                                  \n",
      " dropout_2 (Dropout)            (None, 4, 4, 256)    0           ['max_pooling2d_2[0][0]']        \n",
      "                                                                                                  \n",
      " global_average_pooling2d (Glob  (None, 256)         0           ['dropout_2[0][0]']              \n",
      " alAveragePooling2D)                                                                              \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 256)          65792       ['global_average_pooling2d[0][0]'\n",
      "                                                                 ]                                \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, 256)         1024        ['dense[0][0]']                  \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " dropout_3 (Dropout)            (None, 256)          0           ['batch_normalization_9[0][0]']  \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 10)           2570        ['dropout_3[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 1,298,442\n",
      "Trainable params: 1,295,242\n",
      "Non-trainable params: 3,200\n",
      "__________________________________________________________________________________________________\n",
      "Using custom learning rate schedule for Model_3_SOTA_150e.\n",
      "\n",
      "--- Training Model_3_SOTA_150e for 150 epochs ---\n",
      "Epoch 1/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 2.7179 - accuracy: 0.2113\n",
      "Epoch 1: val_accuracy improved from -inf to 0.09620, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 9s 21ms/step - loss: 2.7159 - accuracy: 0.2117 - val_loss: 3.6688 - val_accuracy: 0.0962\n",
      "Epoch 2/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 2.0109 - accuracy: 0.3482\n",
      "Epoch 2: val_accuracy improved from 0.09620 to 0.25540, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 2.0102 - accuracy: 0.3482 - val_loss: 2.5449 - val_accuracy: 0.2554\n",
      "Epoch 3/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 1.6369 - accuracy: 0.4583\n",
      "Epoch 3: val_accuracy improved from 0.25540 to 0.31860, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 1.6363 - accuracy: 0.4584 - val_loss: 2.1871 - val_accuracy: 0.3186\n",
      "Epoch 4/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 1.3804 - accuracy: 0.5496\n",
      "Epoch 4: val_accuracy did not improve from 0.31860\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 1.3804 - accuracy: 0.5497 - val_loss: 4.2723 - val_accuracy: 0.2116\n",
      "Epoch 5/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 1.2315 - accuracy: 0.6051\n",
      "Epoch 5: val_accuracy improved from 0.31860 to 0.65220, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 1.2317 - accuracy: 0.6050 - val_loss: 1.1588 - val_accuracy: 0.6522\n",
      "Epoch 6/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 1.1141 - accuracy: 0.6527\n",
      "Epoch 6: val_accuracy did not improve from 0.65220\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 1.1136 - accuracy: 0.6528 - val_loss: 1.3993 - val_accuracy: 0.5864\n",
      "Epoch 7/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 1.0268 - accuracy: 0.6837\n",
      "Epoch 7: val_accuracy improved from 0.65220 to 0.66140, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 1.0265 - accuracy: 0.6840 - val_loss: 1.1170 - val_accuracy: 0.6614\n",
      "Epoch 8/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.9588 - accuracy: 0.7129\n",
      "Epoch 8: val_accuracy improved from 0.66140 to 0.69140, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.9585 - accuracy: 0.7129 - val_loss: 1.0147 - val_accuracy: 0.6914\n",
      "Epoch 9/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.8927 - accuracy: 0.7373\n",
      "Epoch 9: val_accuracy improved from 0.69140 to 0.71940, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.8931 - accuracy: 0.7371 - val_loss: 0.9191 - val_accuracy: 0.7194\n",
      "Epoch 10/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.8395 - accuracy: 0.7567\n",
      "Epoch 10: val_accuracy did not improve from 0.71940\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.8401 - accuracy: 0.7566 - val_loss: 1.1938 - val_accuracy: 0.6536\n",
      "Epoch 11/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.8055 - accuracy: 0.7699\n",
      "Epoch 11: val_accuracy improved from 0.71940 to 0.73160, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.8055 - accuracy: 0.7699 - val_loss: 0.9577 - val_accuracy: 0.7316\n",
      "Epoch 12/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.7711 - accuracy: 0.7845\n",
      "Epoch 12: val_accuracy did not improve from 0.73160\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.7712 - accuracy: 0.7846 - val_loss: 0.9854 - val_accuracy: 0.7310\n",
      "Epoch 13/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.7465 - accuracy: 0.7912\n",
      "Epoch 13: val_accuracy did not improve from 0.73160\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.7463 - accuracy: 0.7913 - val_loss: 1.0008 - val_accuracy: 0.7134\n",
      "Epoch 14/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.7223 - accuracy: 0.8053\n",
      "Epoch 14: val_accuracy improved from 0.73160 to 0.75800, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.7222 - accuracy: 0.8053 - val_loss: 0.8620 - val_accuracy: 0.7580\n",
      "Epoch 15/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.7108 - accuracy: 0.8101\n",
      "Epoch 15: val_accuracy improved from 0.75800 to 0.80780, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.7105 - accuracy: 0.8102 - val_loss: 0.7205 - val_accuracy: 0.8078\n",
      "Epoch 16/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.6882 - accuracy: 0.8210\n",
      "Epoch 16: val_accuracy improved from 0.80780 to 0.80820, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.6882 - accuracy: 0.8210 - val_loss: 0.7211 - val_accuracy: 0.8082\n",
      "Epoch 17/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.6660 - accuracy: 0.8289\n",
      "Epoch 17: val_accuracy did not improve from 0.80820\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.6660 - accuracy: 0.8289 - val_loss: 1.4188 - val_accuracy: 0.6542\n",
      "Epoch 18/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.6611 - accuracy: 0.8322\n",
      "Epoch 18: val_accuracy did not improve from 0.80820\n",
      "352/352 [==============================] - 7s 21ms/step - loss: 0.6611 - accuracy: 0.8322 - val_loss: 0.9517 - val_accuracy: 0.7396\n",
      "Epoch 19/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.6503 - accuracy: 0.8379\n",
      "Epoch 19: val_accuracy did not improve from 0.80820\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.6503 - accuracy: 0.8379 - val_loss: 0.7768 - val_accuracy: 0.8014\n",
      "Epoch 20/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.6347 - accuracy: 0.8444\n",
      "Epoch 20: val_accuracy improved from 0.80820 to 0.81200, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.6347 - accuracy: 0.8444 - val_loss: 0.7305 - val_accuracy: 0.8120\n",
      "Epoch 21/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.6289 - accuracy: 0.8471\n",
      "Epoch 21: val_accuracy did not improve from 0.81200\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.6289 - accuracy: 0.8471 - val_loss: 0.8274 - val_accuracy: 0.7980\n",
      "Epoch 22/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.6202 - accuracy: 0.8529\n",
      "Epoch 22: val_accuracy improved from 0.81200 to 0.84240, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.6205 - accuracy: 0.8527 - val_loss: 0.6452 - val_accuracy: 0.8424\n",
      "Epoch 23/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.6166 - accuracy: 0.8565\n",
      "Epoch 23: val_accuracy did not improve from 0.84240\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.6166 - accuracy: 0.8565 - val_loss: 0.6629 - val_accuracy: 0.8332\n",
      "Epoch 24/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.6017 - accuracy: 0.8612\n",
      "Epoch 24: val_accuracy did not improve from 0.84240\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.6018 - accuracy: 0.8612 - val_loss: 0.8105 - val_accuracy: 0.7990\n",
      "Epoch 25/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.6029 - accuracy: 0.8636\n",
      "Epoch 25: val_accuracy improved from 0.84240 to 0.84460, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.6029 - accuracy: 0.8636 - val_loss: 0.6604 - val_accuracy: 0.8446\n",
      "Epoch 26/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.5945 - accuracy: 0.8653\n",
      "Epoch 26: val_accuracy improved from 0.84460 to 0.84960, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5948 - accuracy: 0.8654 - val_loss: 0.6478 - val_accuracy: 0.8496\n",
      "Epoch 27/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.5874 - accuracy: 0.8681\n",
      "Epoch 27: val_accuracy did not improve from 0.84960\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5875 - accuracy: 0.8681 - val_loss: 0.7386 - val_accuracy: 0.8250\n",
      "Epoch 28/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.5830 - accuracy: 0.8717\n",
      "Epoch 28: val_accuracy did not improve from 0.84960\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5830 - accuracy: 0.8717 - val_loss: 0.6997 - val_accuracy: 0.8380\n",
      "Epoch 29/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.5716 - accuracy: 0.8763\n",
      "Epoch 29: val_accuracy did not improve from 0.84960\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5718 - accuracy: 0.8762 - val_loss: 0.6967 - val_accuracy: 0.8352\n",
      "Epoch 30/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.5703 - accuracy: 0.8782\n",
      "Epoch 30: val_accuracy improved from 0.84960 to 0.85180, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5703 - accuracy: 0.8782 - val_loss: 0.6649 - val_accuracy: 0.8518\n",
      "Epoch 31/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.5673 - accuracy: 0.8809\n",
      "Epoch 31: val_accuracy did not improve from 0.85180\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5671 - accuracy: 0.8808 - val_loss: 0.6687 - val_accuracy: 0.8428\n",
      "Epoch 32/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.5603 - accuracy: 0.8842\n",
      "Epoch 32: val_accuracy did not improve from 0.85180\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5604 - accuracy: 0.8842 - val_loss: 0.7414 - val_accuracy: 0.8282\n",
      "Epoch 33/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.5571 - accuracy: 0.8863\n",
      "Epoch 33: val_accuracy did not improve from 0.85180\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5571 - accuracy: 0.8863 - val_loss: 0.6948 - val_accuracy: 0.8506\n",
      "Epoch 34/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.5548 - accuracy: 0.8857\n",
      "Epoch 34: val_accuracy improved from 0.85180 to 0.86840, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5548 - accuracy: 0.8857 - val_loss: 0.6106 - val_accuracy: 0.8684\n",
      "Epoch 35/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.5531 - accuracy: 0.8877\n",
      "Epoch 35: val_accuracy improved from 0.86840 to 0.87440, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5529 - accuracy: 0.8878 - val_loss: 0.5896 - val_accuracy: 0.8744\n",
      "Epoch 36/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.5464 - accuracy: 0.8892\n",
      "Epoch 36: val_accuracy did not improve from 0.87440\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5464 - accuracy: 0.8892 - val_loss: 0.6940 - val_accuracy: 0.8492\n",
      "Epoch 37/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.5416 - accuracy: 0.8912\n",
      "Epoch 37: val_accuracy did not improve from 0.87440\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5414 - accuracy: 0.8913 - val_loss: 0.6471 - val_accuracy: 0.8610\n",
      "Epoch 38/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.5342 - accuracy: 0.8942\n",
      "Epoch 38: val_accuracy did not improve from 0.87440\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5341 - accuracy: 0.8943 - val_loss: 0.6883 - val_accuracy: 0.8540\n",
      "Epoch 39/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.5304 - accuracy: 0.8973\n",
      "Epoch 39: val_accuracy did not improve from 0.87440\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5305 - accuracy: 0.8973 - val_loss: 0.7123 - val_accuracy: 0.8554\n",
      "Epoch 40/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.5308 - accuracy: 0.8988\n",
      "Epoch 40: val_accuracy did not improve from 0.87440\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5308 - accuracy: 0.8988 - val_loss: 0.6523 - val_accuracy: 0.8664\n",
      "Epoch 41/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.5247 - accuracy: 0.9002\n",
      "Epoch 41: val_accuracy did not improve from 0.87440\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5244 - accuracy: 0.9003 - val_loss: 0.6464 - val_accuracy: 0.8672\n",
      "Epoch 42/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.5152 - accuracy: 0.9025\n",
      "Epoch 42: val_accuracy did not improve from 0.87440\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5157 - accuracy: 0.9023 - val_loss: 0.6668 - val_accuracy: 0.8594\n",
      "Epoch 43/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.5194 - accuracy: 0.9021\n",
      "Epoch 43: val_accuracy improved from 0.87440 to 0.87520, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 21ms/step - loss: 0.5193 - accuracy: 0.9021 - val_loss: 0.6283 - val_accuracy: 0.8752\n",
      "Epoch 44/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.5095 - accuracy: 0.9065\n",
      "Epoch 44: val_accuracy improved from 0.87520 to 0.88900, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5093 - accuracy: 0.9065 - val_loss: 0.5894 - val_accuracy: 0.8890\n",
      "Epoch 45/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.5051 - accuracy: 0.9066\n",
      "Epoch 45: val_accuracy did not improve from 0.88900\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.5051 - accuracy: 0.9066 - val_loss: 0.6219 - val_accuracy: 0.8676\n",
      "Epoch 46/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.5052 - accuracy: 0.9063\n",
      "Epoch 46: val_accuracy did not improve from 0.88900\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5049 - accuracy: 0.9065 - val_loss: 0.5775 - val_accuracy: 0.8866\n",
      "Epoch 47/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.5041 - accuracy: 0.9069\n",
      "Epoch 47: val_accuracy did not improve from 0.88900\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5042 - accuracy: 0.9068 - val_loss: 0.6270 - val_accuracy: 0.8708\n",
      "Epoch 48/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.5024 - accuracy: 0.9072\n",
      "Epoch 48: val_accuracy did not improve from 0.88900\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.5024 - accuracy: 0.9072 - val_loss: 0.5932 - val_accuracy: 0.8786\n",
      "Epoch 49/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4937 - accuracy: 0.9108\n",
      "Epoch 49: val_accuracy improved from 0.88900 to 0.89400, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4941 - accuracy: 0.9107 - val_loss: 0.5843 - val_accuracy: 0.8940\n",
      "Epoch 50/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4880 - accuracy: 0.9123\n",
      "Epoch 50: val_accuracy did not improve from 0.89400\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4881 - accuracy: 0.9123 - val_loss: 0.5790 - val_accuracy: 0.8884\n",
      "Epoch 51/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4852 - accuracy: 0.9140\n",
      "Epoch 51: val_accuracy did not improve from 0.89400\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4855 - accuracy: 0.9139 - val_loss: 0.6084 - val_accuracy: 0.8806\n",
      "Epoch 52/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.4878 - accuracy: 0.9124\n",
      "Epoch 52: val_accuracy did not improve from 0.89400\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4878 - accuracy: 0.9124 - val_loss: 0.5625 - val_accuracy: 0.8882\n",
      "Epoch 53/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4773 - accuracy: 0.9155\n",
      "Epoch 53: val_accuracy did not improve from 0.89400\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4773 - accuracy: 0.9155 - val_loss: 0.5748 - val_accuracy: 0.8862\n",
      "Epoch 54/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.4766 - accuracy: 0.9165\n",
      "Epoch 54: val_accuracy did not improve from 0.89400\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4765 - accuracy: 0.9165 - val_loss: 0.6451 - val_accuracy: 0.8668\n",
      "Epoch 55/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.4684 - accuracy: 0.9182\n",
      "Epoch 55: val_accuracy did not improve from 0.89400\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4684 - accuracy: 0.9182 - val_loss: 0.5826 - val_accuracy: 0.8884\n",
      "Epoch 56/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.4645 - accuracy: 0.9198\n",
      "Epoch 56: val_accuracy improved from 0.89400 to 0.89540, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4645 - accuracy: 0.9198 - val_loss: 0.5743 - val_accuracy: 0.8954\n",
      "Epoch 57/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4649 - accuracy: 0.9193\n",
      "Epoch 57: val_accuracy did not improve from 0.89540\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4649 - accuracy: 0.9193 - val_loss: 0.6156 - val_accuracy: 0.8836\n",
      "Epoch 58/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.4567 - accuracy: 0.9224\n",
      "Epoch 58: val_accuracy did not improve from 0.89540\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4567 - accuracy: 0.9224 - val_loss: 0.6064 - val_accuracy: 0.8804\n",
      "Epoch 59/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4535 - accuracy: 0.9224\n",
      "Epoch 59: val_accuracy did not improve from 0.89540\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4535 - accuracy: 0.9224 - val_loss: 0.5671 - val_accuracy: 0.8928\n",
      "Epoch 60/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.4524 - accuracy: 0.9235\n",
      "Epoch 60: val_accuracy improved from 0.89540 to 0.90360, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 21ms/step - loss: 0.4522 - accuracy: 0.9235 - val_loss: 0.5355 - val_accuracy: 0.9036\n",
      "Epoch 61/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.4498 - accuracy: 0.9243\n",
      "Epoch 61: val_accuracy did not improve from 0.90360\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4498 - accuracy: 0.9243 - val_loss: 0.6320 - val_accuracy: 0.8814\n",
      "Epoch 62/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4425 - accuracy: 0.9253\n",
      "Epoch 62: val_accuracy improved from 0.90360 to 0.90520, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4427 - accuracy: 0.9252 - val_loss: 0.5233 - val_accuracy: 0.9052\n",
      "Epoch 63/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.4350 - accuracy: 0.9271\n",
      "Epoch 63: val_accuracy did not improve from 0.90520\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4350 - accuracy: 0.9271 - val_loss: 0.6314 - val_accuracy: 0.8788\n",
      "Epoch 64/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.4392 - accuracy: 0.9278\n",
      "Epoch 64: val_accuracy did not improve from 0.90520\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4391 - accuracy: 0.9279 - val_loss: 0.5579 - val_accuracy: 0.8982\n",
      "Epoch 65/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.4318 - accuracy: 0.9278\n",
      "Epoch 65: val_accuracy did not improve from 0.90520\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4317 - accuracy: 0.9279 - val_loss: 0.5324 - val_accuracy: 0.9030\n",
      "Epoch 66/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.4220 - accuracy: 0.9309\n",
      "Epoch 66: val_accuracy did not improve from 0.90520\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4220 - accuracy: 0.9309 - val_loss: 0.5893 - val_accuracy: 0.8950\n",
      "Epoch 67/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4233 - accuracy: 0.9309\n",
      "Epoch 67: val_accuracy did not improve from 0.90520\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4233 - accuracy: 0.9309 - val_loss: 0.5437 - val_accuracy: 0.9026\n",
      "Epoch 68/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.4201 - accuracy: 0.9302\n",
      "Epoch 68: val_accuracy did not improve from 0.90520\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4201 - accuracy: 0.9302 - val_loss: 0.5229 - val_accuracy: 0.9040\n",
      "Epoch 69/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.4145 - accuracy: 0.9323\n",
      "Epoch 69: val_accuracy did not improve from 0.90520\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.4147 - accuracy: 0.9323 - val_loss: 0.5808 - val_accuracy: 0.8908\n",
      "Epoch 70/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.4113 - accuracy: 0.9339\n",
      "Epoch 70: val_accuracy improved from 0.90520 to 0.90760, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4113 - accuracy: 0.9339 - val_loss: 0.5132 - val_accuracy: 0.9076\n",
      "Epoch 71/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.4054 - accuracy: 0.9360\n",
      "Epoch 71: val_accuracy improved from 0.90760 to 0.91340, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.4054 - accuracy: 0.9360 - val_loss: 0.4946 - val_accuracy: 0.9134\n",
      "Epoch 72/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3982 - accuracy: 0.9382\n",
      "Epoch 72: val_accuracy did not improve from 0.91340\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3986 - accuracy: 0.9380 - val_loss: 0.5572 - val_accuracy: 0.8982\n",
      "Epoch 73/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.3984 - accuracy: 0.9367\n",
      "Epoch 73: val_accuracy did not improve from 0.91340\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3986 - accuracy: 0.9366 - val_loss: 0.5183 - val_accuracy: 0.9024\n",
      "Epoch 74/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3958 - accuracy: 0.9391\n",
      "Epoch 74: val_accuracy did not improve from 0.91340\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3958 - accuracy: 0.9391 - val_loss: 0.5589 - val_accuracy: 0.8952\n",
      "Epoch 75/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3807 - accuracy: 0.9427\n",
      "Epoch 75: val_accuracy did not improve from 0.91340\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3807 - accuracy: 0.9427 - val_loss: 0.5668 - val_accuracy: 0.8960\n",
      "Epoch 76/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3820 - accuracy: 0.9403\n",
      "Epoch 76: val_accuracy did not improve from 0.91340\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3820 - accuracy: 0.9403 - val_loss: 0.5205 - val_accuracy: 0.9086\n",
      "Epoch 77/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3788 - accuracy: 0.9415\n",
      "Epoch 77: val_accuracy did not improve from 0.91340\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3788 - accuracy: 0.9415 - val_loss: 0.6168 - val_accuracy: 0.8910\n",
      "Epoch 78/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3709 - accuracy: 0.9435\n",
      "Epoch 78: val_accuracy did not improve from 0.91340\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3709 - accuracy: 0.9435 - val_loss: 0.5231 - val_accuracy: 0.9070\n",
      "Epoch 79/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3746 - accuracy: 0.9424\n",
      "Epoch 79: val_accuracy did not improve from 0.91340\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3746 - accuracy: 0.9424 - val_loss: 0.5757 - val_accuracy: 0.8994\n",
      "Epoch 80/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3700 - accuracy: 0.9438\n",
      "Epoch 80: val_accuracy improved from 0.91340 to 0.91560, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 21ms/step - loss: 0.3701 - accuracy: 0.9438 - val_loss: 0.4904 - val_accuracy: 0.9156\n",
      "Epoch 81/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3588 - accuracy: 0.9471\n",
      "Epoch 81: val_accuracy did not improve from 0.91560\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3588 - accuracy: 0.9471 - val_loss: 0.5069 - val_accuracy: 0.9110\n",
      "Epoch 82/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.3536 - accuracy: 0.9488\n",
      "Epoch 82: val_accuracy did not improve from 0.91560\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3538 - accuracy: 0.9488 - val_loss: 0.5437 - val_accuracy: 0.9058\n",
      "Epoch 83/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3537 - accuracy: 0.9476\n",
      "Epoch 83: val_accuracy improved from 0.91560 to 0.91940, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3537 - accuracy: 0.9476 - val_loss: 0.4740 - val_accuracy: 0.9194\n",
      "Epoch 84/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3516 - accuracy: 0.9484\n",
      "Epoch 84: val_accuracy did not improve from 0.91940\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3516 - accuracy: 0.9484 - val_loss: 0.5117 - val_accuracy: 0.9062\n",
      "Epoch 85/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.3389 - accuracy: 0.9515\n",
      "Epoch 85: val_accuracy did not improve from 0.91940\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3390 - accuracy: 0.9514 - val_loss: 0.4878 - val_accuracy: 0.9156\n",
      "Epoch 86/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.3372 - accuracy: 0.9519\n",
      "Epoch 86: val_accuracy did not improve from 0.91940\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3368 - accuracy: 0.9520 - val_loss: 0.4983 - val_accuracy: 0.9152\n",
      "Epoch 87/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.3330 - accuracy: 0.9530\n",
      "Epoch 87: val_accuracy did not improve from 0.91940\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3331 - accuracy: 0.9529 - val_loss: 0.5061 - val_accuracy: 0.9084\n",
      "Epoch 88/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.3274 - accuracy: 0.9548\n",
      "Epoch 88: val_accuracy improved from 0.91940 to 0.92340, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3273 - accuracy: 0.9547 - val_loss: 0.4512 - val_accuracy: 0.9234\n",
      "Epoch 89/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.3250 - accuracy: 0.9550\n",
      "Epoch 89: val_accuracy did not improve from 0.92340\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3250 - accuracy: 0.9550 - val_loss: 0.5012 - val_accuracy: 0.9130\n",
      "Epoch 90/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.3184 - accuracy: 0.9559\n",
      "Epoch 90: val_accuracy did not improve from 0.92340\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3183 - accuracy: 0.9559 - val_loss: 0.5057 - val_accuracy: 0.9110\n",
      "Epoch 91/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.3211 - accuracy: 0.9548\n",
      "Epoch 91: val_accuracy did not improve from 0.92340\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.3209 - accuracy: 0.9550 - val_loss: 0.4673 - val_accuracy: 0.9172\n",
      "Epoch 92/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3191 - accuracy: 0.9555\n",
      "Epoch 92: val_accuracy did not improve from 0.92340\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3190 - accuracy: 0.9555 - val_loss: 0.4791 - val_accuracy: 0.9130\n",
      "Epoch 93/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.3051 - accuracy: 0.9580\n",
      "Epoch 93: val_accuracy did not improve from 0.92340\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3048 - accuracy: 0.9580 - val_loss: 0.4998 - val_accuracy: 0.9104\n",
      "Epoch 94/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.3008 - accuracy: 0.9607\n",
      "Epoch 94: val_accuracy did not improve from 0.92340\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3008 - accuracy: 0.9607 - val_loss: 0.4735 - val_accuracy: 0.9216\n",
      "Epoch 95/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2946 - accuracy: 0.9615\n",
      "Epoch 95: val_accuracy did not improve from 0.92340\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2944 - accuracy: 0.9616 - val_loss: 0.4631 - val_accuracy: 0.9208\n",
      "Epoch 96/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.3005 - accuracy: 0.9589\n",
      "Epoch 96: val_accuracy did not improve from 0.92340\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.3005 - accuracy: 0.9589 - val_loss: 0.4691 - val_accuracy: 0.9204\n",
      "Epoch 97/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2894 - accuracy: 0.9632\n",
      "Epoch 97: val_accuracy improved from 0.92340 to 0.92440, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2896 - accuracy: 0.9631 - val_loss: 0.4579 - val_accuracy: 0.9244\n",
      "Epoch 98/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2802 - accuracy: 0.9652\n",
      "Epoch 98: val_accuracy did not improve from 0.92440\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2801 - accuracy: 0.9652 - val_loss: 0.4592 - val_accuracy: 0.9220\n",
      "Epoch 99/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2803 - accuracy: 0.9641\n",
      "Epoch 99: val_accuracy did not improve from 0.92440\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2802 - accuracy: 0.9642 - val_loss: 0.4848 - val_accuracy: 0.9116\n",
      "Epoch 100/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2749 - accuracy: 0.9666\n",
      "Epoch 100: val_accuracy did not improve from 0.92440\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2748 - accuracy: 0.9666 - val_loss: 0.4652 - val_accuracy: 0.9224\n",
      "Epoch 101/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2767 - accuracy: 0.9642\n",
      "Epoch 101: val_accuracy improved from 0.92440 to 0.92500, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2765 - accuracy: 0.9643 - val_loss: 0.4379 - val_accuracy: 0.9250\n",
      "Epoch 102/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2699 - accuracy: 0.9676\n",
      "Epoch 102: val_accuracy did not improve from 0.92500\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2699 - accuracy: 0.9676 - val_loss: 0.4617 - val_accuracy: 0.9228\n",
      "Epoch 103/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2663 - accuracy: 0.9670\n",
      "Epoch 103: val_accuracy did not improve from 0.92500\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2665 - accuracy: 0.9668 - val_loss: 0.4599 - val_accuracy: 0.9242\n",
      "Epoch 104/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2615 - accuracy: 0.9681\n",
      "Epoch 104: val_accuracy improved from 0.92500 to 0.92560, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2617 - accuracy: 0.9680 - val_loss: 0.4416 - val_accuracy: 0.9256\n",
      "Epoch 105/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2565 - accuracy: 0.9703\n",
      "Epoch 105: val_accuracy did not improve from 0.92560\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2563 - accuracy: 0.9704 - val_loss: 0.4414 - val_accuracy: 0.9240\n",
      "Epoch 106/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2550 - accuracy: 0.9703\n",
      "Epoch 106: val_accuracy did not improve from 0.92560\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2550 - accuracy: 0.9703 - val_loss: 0.4294 - val_accuracy: 0.9252\n",
      "Epoch 107/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2495 - accuracy: 0.9704\n",
      "Epoch 107: val_accuracy did not improve from 0.92560\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2496 - accuracy: 0.9703 - val_loss: 0.4467 - val_accuracy: 0.9252\n",
      "Epoch 108/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2482 - accuracy: 0.9713\n",
      "Epoch 108: val_accuracy did not improve from 0.92560\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2481 - accuracy: 0.9713 - val_loss: 0.4417 - val_accuracy: 0.9240\n",
      "Epoch 109/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2408 - accuracy: 0.9730\n",
      "Epoch 109: val_accuracy improved from 0.92560 to 0.92800, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2408 - accuracy: 0.9729 - val_loss: 0.4274 - val_accuracy: 0.9280\n",
      "Epoch 110/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.2408 - accuracy: 0.9721\n",
      "Epoch 110: val_accuracy did not improve from 0.92800\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2408 - accuracy: 0.9721 - val_loss: 0.4367 - val_accuracy: 0.9266\n",
      "Epoch 111/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2341 - accuracy: 0.9742\n",
      "Epoch 111: val_accuracy improved from 0.92800 to 0.92860, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2341 - accuracy: 0.9742 - val_loss: 0.4235 - val_accuracy: 0.9286\n",
      "Epoch 112/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2300 - accuracy: 0.9759\n",
      "Epoch 112: val_accuracy improved from 0.92860 to 0.93040, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 21ms/step - loss: 0.2300 - accuracy: 0.9759 - val_loss: 0.4254 - val_accuracy: 0.9304\n",
      "Epoch 113/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.2268 - accuracy: 0.9762\n",
      "Epoch 113: val_accuracy did not improve from 0.93040\n",
      "352/352 [==============================] - 7s 21ms/step - loss: 0.2268 - accuracy: 0.9762 - val_loss: 0.4398 - val_accuracy: 0.9262\n",
      "Epoch 114/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.2240 - accuracy: 0.9770\n",
      "Epoch 114: val_accuracy did not improve from 0.93040\n",
      "352/352 [==============================] - 8s 21ms/step - loss: 0.2240 - accuracy: 0.9770 - val_loss: 0.4094 - val_accuracy: 0.9304\n",
      "Epoch 115/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2217 - accuracy: 0.9774\n",
      "Epoch 115: val_accuracy did not improve from 0.93040\n",
      "352/352 [==============================] - 7s 21ms/step - loss: 0.2217 - accuracy: 0.9774 - val_loss: 0.4354 - val_accuracy: 0.9296\n",
      "Epoch 116/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2194 - accuracy: 0.9778\n",
      "Epoch 116: val_accuracy did not improve from 0.93040\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2199 - accuracy: 0.9777 - val_loss: 0.4309 - val_accuracy: 0.9294\n",
      "Epoch 117/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2184 - accuracy: 0.9777\n",
      "Epoch 117: val_accuracy improved from 0.93040 to 0.93160, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2184 - accuracy: 0.9777 - val_loss: 0.4185 - val_accuracy: 0.9316\n",
      "Epoch 118/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2108 - accuracy: 0.9811\n",
      "Epoch 118: val_accuracy improved from 0.93160 to 0.93180, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2108 - accuracy: 0.9811 - val_loss: 0.4337 - val_accuracy: 0.9318\n",
      "Epoch 119/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.2120 - accuracy: 0.9785\n",
      "Epoch 119: val_accuracy did not improve from 0.93180\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2120 - accuracy: 0.9785 - val_loss: 0.4237 - val_accuracy: 0.9314\n",
      "Epoch 120/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.2049 - accuracy: 0.9819\n",
      "Epoch 120: val_accuracy improved from 0.93180 to 0.93400, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.2049 - accuracy: 0.9819 - val_loss: 0.4079 - val_accuracy: 0.9340\n",
      "Epoch 121/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2030 - accuracy: 0.9821\n",
      "Epoch 121: val_accuracy did not improve from 0.93400\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2029 - accuracy: 0.9821 - val_loss: 0.4172 - val_accuracy: 0.9316\n",
      "Epoch 122/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.2008 - accuracy: 0.9820\n",
      "Epoch 122: val_accuracy did not improve from 0.93400\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.2008 - accuracy: 0.9820 - val_loss: 0.4245 - val_accuracy: 0.9312\n",
      "Epoch 123/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1951 - accuracy: 0.9838\n",
      "Epoch 123: val_accuracy improved from 0.93400 to 0.93540, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1952 - accuracy: 0.9838 - val_loss: 0.4114 - val_accuracy: 0.9354\n",
      "Epoch 124/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1971 - accuracy: 0.9831\n",
      "Epoch 124: val_accuracy did not improve from 0.93540\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.1971 - accuracy: 0.9831 - val_loss: 0.4125 - val_accuracy: 0.9346\n",
      "Epoch 125/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1945 - accuracy: 0.9830\n",
      "Epoch 125: val_accuracy did not improve from 0.93540\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.1946 - accuracy: 0.9830 - val_loss: 0.4130 - val_accuracy: 0.9354\n",
      "Epoch 126/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1923 - accuracy: 0.9848\n",
      "Epoch 126: val_accuracy did not improve from 0.93540\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1923 - accuracy: 0.9848 - val_loss: 0.4136 - val_accuracy: 0.9328\n",
      "Epoch 127/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1917 - accuracy: 0.9835\n",
      "Epoch 127: val_accuracy did not improve from 0.93540\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.1917 - accuracy: 0.9835 - val_loss: 0.4142 - val_accuracy: 0.9342\n",
      "Epoch 128/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1884 - accuracy: 0.9849\n",
      "Epoch 128: val_accuracy did not improve from 0.93540\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1887 - accuracy: 0.9848 - val_loss: 0.4138 - val_accuracy: 0.9324\n",
      "Epoch 129/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1876 - accuracy: 0.9855\n",
      "Epoch 129: val_accuracy improved from 0.93540 to 0.93640, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1878 - accuracy: 0.9854 - val_loss: 0.4118 - val_accuracy: 0.9364\n",
      "Epoch 130/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1842 - accuracy: 0.9855\n",
      "Epoch 130: val_accuracy did not improve from 0.93640\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1842 - accuracy: 0.9855 - val_loss: 0.4152 - val_accuracy: 0.9348\n",
      "Epoch 131/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1833 - accuracy: 0.9856\n",
      "Epoch 131: val_accuracy did not improve from 0.93640\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.1833 - accuracy: 0.9856 - val_loss: 0.4127 - val_accuracy: 0.9352\n",
      "Epoch 132/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1850 - accuracy: 0.9854\n",
      "Epoch 132: val_accuracy did not improve from 0.93640\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.1848 - accuracy: 0.9855 - val_loss: 0.4120 - val_accuracy: 0.9340\n",
      "Epoch 133/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1795 - accuracy: 0.9869\n",
      "Epoch 133: val_accuracy did not improve from 0.93640\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.1795 - accuracy: 0.9869 - val_loss: 0.4097 - val_accuracy: 0.9364\n",
      "Epoch 134/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1812 - accuracy: 0.9871\n",
      "Epoch 134: val_accuracy did not improve from 0.93640\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.1812 - accuracy: 0.9871 - val_loss: 0.4112 - val_accuracy: 0.9356\n",
      "Epoch 135/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1808 - accuracy: 0.9861\n",
      "Epoch 135: val_accuracy improved from 0.93640 to 0.93680, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1808 - accuracy: 0.9861 - val_loss: 0.4039 - val_accuracy: 0.9368\n",
      "Epoch 136/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1800 - accuracy: 0.9861\n",
      "Epoch 136: val_accuracy improved from 0.93680 to 0.93740, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 21ms/step - loss: 0.1800 - accuracy: 0.9862 - val_loss: 0.4040 - val_accuracy: 0.9374\n",
      "Epoch 137/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1788 - accuracy: 0.9866\n",
      "Epoch 137: val_accuracy did not improve from 0.93740\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1787 - accuracy: 0.9866 - val_loss: 0.4037 - val_accuracy: 0.9356\n",
      "Epoch 138/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1787 - accuracy: 0.9868\n",
      "Epoch 138: val_accuracy did not improve from 0.93740\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1788 - accuracy: 0.9868 - val_loss: 0.4017 - val_accuracy: 0.9360\n",
      "Epoch 139/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1764 - accuracy: 0.9874\n",
      "Epoch 139: val_accuracy improved from 0.93740 to 0.93780, saving model to models\\Model_3_SOTA_150e_best.h5\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1764 - accuracy: 0.9874 - val_loss: 0.4017 - val_accuracy: 0.9378\n",
      "Epoch 140/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1756 - accuracy: 0.9874\n",
      "Epoch 140: val_accuracy did not improve from 0.93780\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1755 - accuracy: 0.9874 - val_loss: 0.3993 - val_accuracy: 0.9356\n",
      "Epoch 141/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1732 - accuracy: 0.9883\n",
      "Epoch 141: val_accuracy did not improve from 0.93780\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1733 - accuracy: 0.9883 - val_loss: 0.4003 - val_accuracy: 0.9358\n",
      "Epoch 142/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1736 - accuracy: 0.9886\n",
      "Epoch 142: val_accuracy did not improve from 0.93780\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1736 - accuracy: 0.9886 - val_loss: 0.3998 - val_accuracy: 0.9366\n",
      "Epoch 143/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1739 - accuracy: 0.9886\n",
      "Epoch 143: val_accuracy did not improve from 0.93780\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1740 - accuracy: 0.9886 - val_loss: 0.4000 - val_accuracy: 0.9376\n",
      "Epoch 144/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1736 - accuracy: 0.9884\n",
      "Epoch 144: val_accuracy did not improve from 0.93780\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1737 - accuracy: 0.9883 - val_loss: 0.4001 - val_accuracy: 0.9374\n",
      "Epoch 145/150\n",
      "350/352 [============================>.] - ETA: 0s - loss: 0.1755 - accuracy: 0.9878\n",
      "Epoch 145: val_accuracy did not improve from 0.93780\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1756 - accuracy: 0.9877 - val_loss: 0.4014 - val_accuracy: 0.9366\n",
      "Epoch 146/150\n",
      "352/352 [==============================] - ETA: 0s - loss: 0.1749 - accuracy: 0.9884\n",
      "Epoch 146: val_accuracy did not improve from 0.93780\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1749 - accuracy: 0.9884 - val_loss: 0.4014 - val_accuracy: 0.9372\n",
      "Epoch 147/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1750 - accuracy: 0.9880\n",
      "Epoch 147: val_accuracy did not improve from 0.93780\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1750 - accuracy: 0.9880 - val_loss: 0.4016 - val_accuracy: 0.9372\n",
      "Epoch 148/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1743 - accuracy: 0.9876\n",
      "Epoch 148: val_accuracy did not improve from 0.93780\n",
      "352/352 [==============================] - 7s 20ms/step - loss: 0.1744 - accuracy: 0.9876 - val_loss: 0.4012 - val_accuracy: 0.9376\n",
      "Epoch 149/150\n",
      "351/352 [============================>.] - ETA: 0s - loss: 0.1727 - accuracy: 0.9888\n",
      "Epoch 149: val_accuracy did not improve from 0.93780\n",
      "352/352 [==============================] - 7s 21ms/step - loss: 0.1727 - accuracy: 0.9888 - val_loss: 0.4018 - val_accuracy: 0.9366\n",
      "Epoch 150/150\n",
      "349/352 [============================>.] - ETA: 0s - loss: 0.1739 - accuracy: 0.9884\n",
      "Epoch 150: val_accuracy did not improve from 0.93780\n",
      "352/352 [==============================] - 7s 19ms/step - loss: 0.1738 - accuracy: 0.9884 - val_loss: 0.4013 - val_accuracy: 0.9372\n",
      "Training finished in: 0:17:24.653784\n",
      "Successfully saved history to models/Model_3_SOTA_150e_history.pkl\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "EVALUATING: models/Model_3_SOTA_150e_best.h5\n",
      "============================================================\n",
      "Loaded best weights from models/Model_3_SOTA_150e_best.h5\n",
      "79/79 [==============================] - 1s 6ms/step - loss: 0.4258 - accuracy: 0.9319\n",
      "\n",
      "Test Accuracy: 93.19%\n",
      "Test Loss: 0.4258\n",
      "Results saved to all_results.json\n"
     ]
    }
   ],
   "source": [
    "# 6. RUN MODEL 3: SOTA @ 150 EPOCHS\n",
    "if RUN_MODEL_3_SOTA_150 and 'data' in locals():\n",
    "    model_name = \"Model_3_SOTA_150e\"\n",
    "    epochs = MODEL_CONFIG[model_name][\"epochs\"]\n",
    "    print(f\"\\n{'='*60}\\nSTARTING: {model_name}\\n{'='*60}\")\n",
    "    tf.keras.backend.clear_session(); gc.collect()\n",
    "    model_3_150 = create_model_3_sota(train_size=train_size, total_epochs=epochs)\n",
    "    model_3_150.summary()\n",
    "    history_3_150 = train_model(\n",
    "        model_3_150, train_ds_augmented, val_ds, \n",
    "        epochs, model_name, \n",
    "        MODEL_CONFIG[model_name][\"patience\"], use_lr_schedule=True\n",
    "    )\n",
    "    plot_history(history_3_150, model_name)\n",
    "    acc, loss = evaluate_model(f'models/{model_name}_best.h5', test_ds)\n",
    "    if acc: results[model_name] = (acc, loss); save_results(results)\n",
    "    del model_3_150, history_3_150\n",
    "else:\n",
    "    print(\"Skipping Model 3 (150 Epochs) training.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0e794161-a19c-4008-9db6-52208a96486c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "STARTING: COMPARISON PLOTS\n",
      "============================================================\n",
      "\n",
      "============================================================\n",
      "FINAL TEST ACCURACY COMPARISON\n",
      "============================================================\n",
      "Model 1: Baseline              | Accuracy:  87.63% | Loss: 0.4773\n",
      "Model 2: Improved              | Accuracy:  90.73% | Loss: 0.3215\n",
      "Model 3: SOTA (100e)           | Accuracy:  92.94% | Loss: 0.4316\n",
      "Model 3: SOTA (150e)           | Accuracy:  93.19% | Loss: 0.4258\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generating combined learning curves...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 7. COMPARISON PLOTTING\n",
    "if RUN_COMPARISON_PLOTS and 'data' in locals():\n",
    "    print(\"\\n\" + \"=\"*60)\n",
    "    print(\"STARTING: COMPARISON PLOTS\")\n",
    "    print(\"=\"*60)\n",
    "    # results = load_results() # Results should already be in memory, but load just in case\n",
    "    plot_final_comparison_bar(results, MODEL_CONFIG)\n",
    "    plot_combined_learning_curves(results, MODEL_CONFIG)\n",
    "else:\n",
    "    print(\"Skipping comparison plots.\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}