{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "96db2046",
   "metadata": {},
   "source": [
    "First Lets Import Necessary Things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41042839",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow Version: 2.19.0\n",
      "No GPU detected. Training will run on CPU, which might be slow.\n",
      "\n",
      "Setup complete. Ready to load data.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import shutil # Used for temporary cleanup if needed, but not strictly for the core logic\n",
    "\n",
    "print(f\"TensorFlow Version: {tf.__version__}\")\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    try:\n",
    "        for gpu in gpus:\n",
    "            tf.config.experimental.set_memory_growth(gpu, True)\n",
    "        logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
    "        print(f\"{len(gpus)} Physical GPUs, {len(logical_gpus)} Logical GPUs\")\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "else:\n",
    "    print(\"No GPU detected. Training will run on CPU, which might be slow.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81d1ec79",
   "metadata": {},
   "source": [
    "Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9c5fcfd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Setup complete. Ready to load data.\n"
     ]
    }
   ],
   "source": [
    "# --- Define Constants and Paths ---\n",
    "TRAIN_DIR = os.path.join('train')\n",
    "TEST_DIR = os.path.join('test')\n",
    "\n",
    "IMG_HEIGHT = 48\n",
    "IMG_WIDTH = 48\n",
    "BATCH_SIZE = 64 # Adjust based on your GPU/CPU memory\n",
    "NUM_EPOCHS = 50 # Set a reasonable max, EarlyStopping will prevent overfitting\n",
    "NUM_CLASSES = 7 # angry, disgust, fear, happy, neutral, sad, surprise\n",
    "\n",
    "emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']\n",
    "\n",
    "# Create 'models' directory if it doesn't exist to store the best model\n",
    "if not os.path.exists('models'):\n",
    "    os.makedirs('models')\n",
    "\n",
    "print(\"\\nSetup complete. Ready to load data.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4f2eb747",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e4543ea",
   "metadata": {},
   "source": [
    "Data Preprocessing and Data Splitting using Generators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3f54cf79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For training data: apply augmentation and normalization\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255, # Normalize pixel values to [0, 1]\n",
    "    rotation_range=15, # Randomly rotate images by up to 15 degrees\n",
    "    width_shift_range=0.1, # Randomly shift images horizontally\n",
    "    height_shift_range=0.1, # Randomly shift images vertically\n",
    "    shear_range=0.1, # Apply shear transformation\n",
    "    zoom_range=0.1, # Randomly zoom into images\n",
    "    horizontal_flip=True, # Randomly flip images horizontally\n",
    "    fill_mode='nearest' # Fill new pixels created by transformations\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcd26f7e",
   "metadata": {},
   "source": [
    "Apply to Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e8ac4c1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading training data from: train\n",
      "Found 28709 images belonging to 7 classes.\n",
      "\n",
      "Loading testing data from: test\n",
      "Found 7178 images belonging to 7 classes.\n",
      "\n",
      "Class Indices (mapping numerical labels to emotion names):\n",
      "{'angry': 0, 'disgust': 1, 'fear': 2, 'happy': 3, 'neutral': 4, 'sad': 5, 'surprise': 6}\n",
      "\n",
      "Reverse mapping (index to label):\n",
      "{0: 'angry', 1: 'disgust', 2: 'fear', 3: 'happy', 4: 'neutral', 5: 'sad', 6: 'surprise'}\n"
     ]
    }
   ],
   "source": [
    "# For testing data: only normalize (no augmentation)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "print(f\"Loading training data from: {TRAIN_DIR}\")\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    TRAIN_DIR,\n",
    "    target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    color_mode='grayscale', # FER-2013 is grayscale\n",
    "    class_mode='categorical', # For one-hot encoded labels (e.g., [0,0,1,0,0,0,0])\n",
    "    shuffle=True # Shuffle data for training\n",
    ")\n",
    "\n",
    "print(f\"\\nLoading testing data from: {TEST_DIR}\")\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    TEST_DIR,\n",
    "    target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    color_mode='grayscale',\n",
    "    class_mode='categorical',\n",
    "    shuffle=False # No need to shuffle test data, consistent evaluation\n",
    ")\n",
    "\n",
    "# Verify class indices mapping (important for understanding predictions)\n",
    "print(\"\\nClass Indices (mapping numerical labels to emotion names):\")\n",
    "print(train_generator.class_indices)\n",
    "\n",
    "# Create a reverse mapping for easy lookup\n",
    "idx_to_label = {v: k for k, v in train_generator.class_indices.items()}\n",
    "print(\"\\nReverse mapping (index to label):\")\n",
    "print(idx_to_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ea0284",
   "metadata": {},
   "source": [
    "Visualization (Original Image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b8169eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualization of original images complete.\n"
     ]
    }
   ],
   "source": [
    "# Get one batch of training images and labels\n",
    "images, labels = next(train_generator)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.suptitle(\"Sample Original Images (from Train Generator)\", fontsize=16)\n",
    "\n",
    "# Display up to 9 images\n",
    "for i in range(min(9, len(images))):\n",
    "    plt.subplot(3, 3, i + 1)\n",
    "    # Remove the channel dimension (1) for grayscale image display\n",
    "    plt.imshow(images[i].reshape(IMG_HEIGHT, IMG_WIDTH), cmap='gray')\n",
    "    \n",
    "    # Convert one-hot encoded label back to emotion name\n",
    "    predicted_label_index = np.argmax(labels[i])\n",
    "    emotion_name = idx_to_label[predicted_label_index]\n",
    "    \n",
    "    plt.title(f\"Emotion: {emotion_name}\")\n",
    "    plt.axis('off')\n",
    "\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95]) # Adjust layout to prevent title overlap\n",
    "plt.show()\n",
    "\n",
    "print(\"Visualization of original images complete.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "262185c3",
   "metadata": {},
   "source": [
    "Visualization (Augmented Iamges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5689a38b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dd7Oiu31T61wZdI+re1WKpNG4hJZIksGkSZNKl/fr1y+7H3XpX45qa1yfqNJgXEqMaodcQoVKblKphO48uLbTJSqpe921+a6eKCqZxdXTupKWIqlJ7j7eunVrZalbLsFDXTtXRj3vueQc9VzgnkXRfa7NU/egex5S19jVZXUPuDqm2taVK1fKMnfccUfp8mc961myzOzZs7PrskvLU33PvffeK8v893//d9bxJIMHDy5dPn369OxnQldH7rzzzsoS8aLvvqotdAmNKmm0vb09+7n9oosukmVUolLkXd5x1yhyvptFf+6eqyIJqeoed+8hI0aMyN63ruAbMQAAAAAAADVhIAYAAAAAAKAmDMQAAAAAAADUhIEYAAAAAACAmjAQAwAAAAAAUBMGYgAAAAAAAHpbfLWKm3bxiC42WXERmip2y0VHqYiqSESli8JS61ScoovCSgYOHJgdq6fOt7sO6vPcdlR8nooyTpqamrKO81gR2uPHj8+OvF68eHF2xJqK5IycUxc7qPbBRdqpqDkXze5ibdU+7NixQ5ZR58FFFY4bNy47elFF5Lk4S1Umcu+ja6qO3q2Si23csmVL1nIXmeiiltU+uH1z8ZDqfLu2WO3fnj17aomvdu2g2gfXH7n4ahU/XnV8tboOrp9Q59TdQ+45rMr4ardO7Z9ri1U/6tp8Vcb1YeqZwdU5dY+7vhfd59q8SHx15D1EtSuujNqOi7295ZZbso9HPY+tXr1alnHtvnpPWr9+fXa0+9y5c2WZK6+8snT5jBkzZJm2trbse121he6+dbHSmzZtylru+hH3DKDWqeeJ5L777suKCk9mzZqVvW+q3rs+KfIM7iLlm01/njtusG3btuxjde/l6v6KjHUcim/EAAAAAAAA1ISBGAAAAAAAgJowEAMAAAAAAFATBmIAAAAAAABqwkAMAAAAAABAb0tNcjOcR9J61IzXLjVJzc7sUmLUrOguCUbN1u9melbH6hIvXAKC+jxXRiUQuMQJNVN3ZCZsV0fUvrmZ5t3s2WqdS3tQM5y77QwbNix7OyqhwZ1Txd1DakZ5dz+4a6RmlHezxqv9U6lWx5rpXVH1PpI24+5JPHm5tjOSmqRSLVydVIkF0dQk1bbXlZrk2ieVqOT6UbUPkb4gWbVqVXZqkur/XZpSJH1FJVFUnTymrrd7LnD9m7p+kQQYd0+6NENl6NCh2XVEPQtu3749e/voOteuqbrkyqg21NUxtc7dg2qde645++yzs5+TVALYhg0bQqlAI0eOLF0+ZswYWWb27NnZ/cv5559funzixImyjGonXaqaunaunXbrVEKUO6fq+cCl9ajPW7lypSyzaNGi7OeGSP+i+gr3Xr527drs+3jChAmyzECTpJvbZ7t7X/UVLulY9Umur2ptbW0cC9+IAQAAAAAAqAkDMQAAAAAAADVhIAYAAAAAAKAmDMQAAAAAAADUhIEYAAAAAACAmjAQAwAAAAAAUJNTOly+EwAAAAAAACrDN2IAAAAAAABqwkAMAAAAAABATRiIAQAAAAAAqAkDMQAAAAAAADVhIAYAAAAAAKAmDMQAAAAAAADUhIEYAAAAAACAmjAQAwAAAAAAUBMGYgAAAAAAAGrCQAwAAAAAAEBNGIgBAAAAAACoCQMxAAAAAAAANWEgBgAAAAAAoCYMxAAAAAAAANSEgRggw7/92781TjnllMby5ct7elcAAL0Q/QQA4FjoK8BAzJPIZz7zmeKGPvfccxsns927dzfe9773NW666aZGb/HGN76xuDbXXnttT+8KgJMY/UTv6Sc6H8LL/qxbt67H9gsA6Ct6T1/R6Wc/+1nj8ssvbwwcOLDR3NzcWLhwYeMb3/hGT+8WuuH07hRG7/K1r32tMWHChMZtt93WeOihhxpTpkxpnKyN5vvf//7i/1966aU9vTuN22+/vXjg7tu3b0/vCoCTHP1E7+snPvCBDzQmTpx42LJBgwb12P4AAH1F7+orvvSlLzXe8IY3NJ7+9Kc3PvzhDzdOO+20xtKlSxsrV67ssX1C9zEQ8ySxbNmyxi233NL4zne+03jTm95UNKDvfe97e3q3TnodHR2NP/mTP2m8+tWvbtxwww09vTsATmL0E73TM5/5zMZTnvKUnt4NACjQV/Qu6adLf/zHf9x429ve1vi///f/9vTuoEL8NOlJIjWSra2tjWuuuabxwhe+sPj3I6Wv1aWvGR759bp0g6fl6Vsbh/rmN7/ZmDVrVvFNjjlz5jS++93vNl772tcWI+RHlv3Yxz7W+PSnP92YNGlSo3///o1nPOMZxShtGoj44Ac/2BgzZkyjX79+jeuuu66xZcuWo/btf/7nfxpPe9rTGgMGDCi+bpeO49577z3s76RtNzU1NVavXt147nOfW/z/oUOHNt75znc2Hn/88YP7k5YlaQS782ve6WuFnZYsWVKco7a2tuLY0gPw9ddff9Q+pe2nrwCm/U77/6EPfajxxBNPZFyVRuMrX/lKY/HixY2/+Zu/ySoHAFWjn+id/USyc+fOg/sHAD2JvqJ39RX//M//XOxT+vZk0t7eXpwLPAl04ElhxowZHW94wxuK/3/zzTenu7PjtttuO+zv3HjjjcXy9M9DLVu2rFj+pS996eCy73//+x2nnHJKx7x58zr+4R/+oeM973lPR2tra8ecOXM6xo8ff1TZs846q2PWrFnF3/3rv/7rjjPPPLPjvPPO6/jLv/zLjgsuuKDjn/7pnzr+5E/+pPjM173udYdt/8tf/nKx/Oqrr+745Cc/2fGRj3ykY8KECR2DBg0qPr/Ta17zmo6+fft2zJ49u+P1r399x2c/+9mOF7zgBcX2P/OZzxR/p729vVielj3vec/r+MpXvlL8ufvuu4v1ixcv7hg4cGCxr2k7n/rUpzouvvjiYvvf+c53Dm5r7dq1HUOHDi2O+X3ve1/HRz/60Y6pU6cW5yN99qH7pezYsaNjxIgRHX/7t39b/Hs6b9dcc00XrygAVIt+onf1E+lcpr/X1NRU/DOdj2c/+9kdDzzwQNZ1BYAq0Vf0rr5i4cKFxd/9j//4j47Ro0cXZdJnpXPz+OOPZ11b9C4MxDwJ3H777cVN+dOf/rT49yeeeKJjzJgxHW9/+9vDjebcuXOLz9i5c+fBZTfddFPx98oazdTAbNu27eDyd7/73cXy+fPndxw4cODg8pe97GVFg7p3797i39Pnp8bxjW9842H7tG7duqJxO3R5ajTTZ37gAx847O8uWLCgaKQ6bdy4sfh7733ve486V1dccUVxbJ3b7zxfqWFPjWKnd7zjHcVn3HrrrQeXbdiwodinrg7EvPOd7+yYOHHiwW0xEAOgp9BP9L5+4hvf+EbHa1/72o5///d/7/jud79bPFT379+/Y8iQIR0rVqywZQHgeKCv6H19RUtLSzHw0qdPn2IQ61vf+lbHy1/+8qLsu971LlsWvRs/TXoSSF8ZHD58eOOyyy4r/j19be4lL3lJ4+tf/3roq85r1qxp3HPPPcW8Jumrep0uueSSxty5c0vLvOhFLypm8e7UOcv6K1/5ysbpp59+2PL9+/cXXwVMfvrTnza2bdvWeNnLXtbYtGnTwT9pEqr0d2+88cajtvXmN7/5sH9PXz985JFHjnlc6euLP//5zxsvfvGLi6+Bd25r8+bNjauuuqrx4IMPHtyvH/7wh43zzjuvcc455xwsn76e+IpXvKLRFQ888EDxO86PfvSjjT59+nSpDAAcL/QTva+fSNtIEzCmc5i+Gp++cv/jH/+42BY/ZwXQE+grel9fkX6KtHXr1uLnUennSS94wQuK63T11VcX7xpp+zgxMVnvCS41iqlxTA1mmlyrU2pwPv7xjxcTxKbfVuZ49NFHi3+WzZCelt1xxx1HLR83btxh/97ZgI4dO7Z0eWpQktRQJel3k2VaWloO+/f0+8vO32t2Sr9j7fw8J836nr4F9p73vKf4U2bDhg2N0aNHF+egLLJv+vTpja54+9vf3rjggguKxhIAehL9RO/sJ8pcdNFFxWemmFIAqBN9Re/sK9K8Mrt27SoGmA6V/v1HP/pR484772xcfPHFXfos9C4MxJzg0mjs2rVri4Yz/TlSGjHtbDTTqHaZKiYITKPNOcs7J5nqnKgqTWo7YsSIo/7eoSPf7vO6onNbaSKuNFpdpop4vnRNUsOYZptPE311euyxxxp79uwplqVJvY7sEADgeKCf6H39hJNeNlIsKQDUib6id/YVo0aNKgaZ0jeVDjVs2LDin10ZOELvxEDMCS41iulGTLOLHykNBKRZydNs22k0NY3yJulre2Wj1Z3Gjx9/cLT3SGXLumPy5MnFP9MxXHnllZV8puoc0uzryRlnnHHMbaVz0DmyfqiuPByvWLGi+Ofzn//8o9alrylOnDix8YlPfKLxjne845ifBQDdRT/R+/oJJ30t/sj/SgsAxxt9Re/sKxYuXHjwp06d2+382VdCf3HiYo6YE1j6dkVqGK+99toiOu3IP29961uL3w12xqilhiCN/t58882Hfc5nPvOZo0ZeU7Tcl7/85eJ3iZ1+8YtfFL/zrFIaRU7fDPnwhz/cOHDgwFHrN27cmP2ZKequrHNIDfOll17a+NznPleM+LttPetZz2r89re/bdx2222HrS+L8DtS+kpk6qyO/JMayhRrl/7/s5/97OzjAoBc9BO9s59Q+53mEvj9739f/PYfAOpCX9F7+4o0R0/yxS9+8bBv5KQ5xtI37NNADU5MfCPmBJYaw9QoPuc5zyldnyaGSi//6UZPN3H6LWWaAOuTn/xkMcKbRo6///3vF79hPFJqxK677rrGhRde2Hjd615XfO3tU5/6VNGYHtqQdldqMD/72c82XvWqVzXOPvvsxktf+tJin9O3Sn7wgx8U20/bzZFG6mfNmtX4xje+0Zg2bVrRSKX9Tn/SKH/6DX6aIOyNb3xjMbK8fv36xm9+85vGqlWrGnfffXfxGX/+539efLUxPQyn+V4GDBj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      "text/plain": [
       "<Figure size 1200x1200 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualization of augmented images complete.\n"
     ]
    }
   ],
   "source": [
    "# Pick a single image from the generator to demonstrate augmentation\n",
    "# We'll take the first image from a batch to apply augmentation to\n",
    "sample_image_batch, sample_label_batch = next(train_generator)\n",
    "sample_image = sample_image_batch[0] # Take the first image\n",
    "sample_label_index = np.argmax(sample_label_batch[0])\n",
    "sample_emotion_name = idx_to_label[sample_label_index]\n",
    "\n",
    "# Reshape for single image flow (add batch dimension)\n",
    "sample_image_expanded = np.expand_dims(sample_image, 0)\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.suptitle(f\"Augmented Images of '{sample_emotion_name}'\", fontsize=16)\n",
    "\n",
    "# Generate and display augmented versions of the single image\n",
    "for i, batch in enumerate(train_datagen.flow(sample_image_expanded, batch_size=1)):\n",
    "    if i >= 9: # Display 9 augmented versions\n",
    "        break\n",
    "    \n",
    "    plt.subplot(3, 3, i + 1)\n",
    "    # Remove the batch and channel dimensions for grayscale image display\n",
    "    plt.imshow(batch[0].reshape(IMG_HEIGHT, IMG_WIDTH), cmap='gray')\n",
    "    plt.title(f\"Augmented {i+1}\")\n",
    "    plt.axis('off')\n",
    "\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "plt.show()\n",
    "\n",
    "print(\"Visualization of augmented images complete.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fa7406a",
   "metadata": {},
   "source": [
    "Now Let's Build The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "929f5297",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building the CNN Model...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Regino Balogo Jr\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_3           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_4           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_5           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4608</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,179,904</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_6           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>)              │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,799</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m320\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m9,248\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m36,928\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_3           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_4           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │           \u001b[38;5;34m512\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │       \u001b[38;5;34m147,584\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_5           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │           \u001b[38;5;34m512\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4608\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │     \u001b[38;5;34m1,179,904\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_6           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │         \u001b[38;5;34m1,024\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m)              │         \u001b[38;5;34m1,799\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,470,951</span> (5.61 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,470,951\u001b[0m (5.61 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,469,543</span> (5.61 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,469,543\u001b[0m (5.61 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,408</span> (5.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,408\u001b[0m (5.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CNN Model built successfully. Summary displayed above.\n"
     ]
    }
   ],
   "source": [
    "# Cell 5: Build the CNN Model\n",
    "\n",
    "print(\"Building the CNN Model...\")\n",
    "\n",
    "model = Sequential([\n",
    "    # Input Block 1\n",
    "    Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH, 1)),\n",
    "    BatchNormalization(), # Normalizes activations of previous layer\n",
    "    Conv2D(32, (3, 3), padding='same', activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    MaxPooling2D(pool_size=(2, 2)), # Downsamples the feature maps, reducing spatial dimensions\n",
    "    Dropout(0.25), # Randomly sets a fraction of input units to 0 to prevent overfitting\n",
    "\n",
    "    # Block 2\n",
    "    Conv2D(64, (3, 3), padding='same', activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Conv2D(64, (3, 3), padding='same', activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    Dropout(0.25),\n",
    "\n",
    "    # Block 3\n",
    "    Conv2D(128, (3, 3), padding='same', activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Conv2D(128, (3, 3), padding='same', activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    Dropout(0.25),\n",
    "\n",
    "    # Flatten and Dense Layers (Classifier Head)\n",
    "    Flatten(), # Flattens the 2D feature maps into a 1D vector for the fully connected layers\n",
    "    Dense(256, activation='relu'), # A fully connected layer\n",
    "    BatchNormalization(),\n",
    "    Dropout(0.5), # Higher dropout rate for the dense layer as they are more prone to overfitting\n",
    "    Dense(NUM_CLASSES, activation='softmax') # Output layer with 7 classes, softmax for probability distribution\n",
    "])\n",
    "\n",
    "# Display a summary of the model's architecture, including layer types, output shapes, and number of parameters\n",
    "model.summary()\n",
    "\n",
    "print(\"\\nCNN Model built successfully. Summary displayed above.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c39119",
   "metadata": {},
   "source": [
    "Compile The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e7a6ee41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling the model...\n",
      "Model compiled successfully. Ready for training!\n"
     ]
    }
   ],
   "source": [
    "# Cell 6: Compile the Model\n",
    "\n",
    "print(\"Compiling the model...\")\n",
    "\n",
    "# Define the optimizer. Adam is a good general-purpose optimizer.\n",
    "# We set a learning rate to control the step size during optimization.\n",
    "optimizer = Adam(learning_rate=0.001)\n",
    "\n",
    "model.compile(optimizer=optimizer,\n",
    "              loss='categorical_crossentropy', # Suitable for multi-class classification with one-hot encoded labels\n",
    "              metrics=['accuracy']) # We want to track the accuracy of the model during training\n",
    "\n",
    "print(\"Model compiled successfully. Ready for training!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c78a1e18",
   "metadata": {},
   "source": [
    "Define Callbacks to Avoid Overfitting and Underfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3fb43b00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defining training callbacks...\n",
      "Callbacks defined successfully. The model will be saved, early stopped, and learning rate adjusted automatically.\n"
     ]
    }
   ],
   "source": [
    "# Cell 7: Define Callbacks\n",
    "\n",
    "print(\"Defining training callbacks...\")\n",
    "\n",
    "# --- 1. ModelCheckpoint ---\n",
    "# Saves the model after every epoch if the 'val_accuracy' has improved.\n",
    "# This ensures we keep the model that performed best on the validation set.\n",
    "checkpoint_path = 'models/emotion_model_best.h5' # The path where the best model will be saved\n",
    "checkpoint = ModelCheckpoint(filepath=checkpoint_path,\n",
    "                             monitor='val_accuracy', # Metric to monitor for improvement\n",
    "                             verbose=1, # Show messages when checkpoint is saved\n",
    "                             save_best_only=True, # Only save if the monitored metric is better than before\n",
    "                             mode='max') # 'max' mode means we want to maximize 'val_accuracy'\n",
    "\n",
    "# --- 2. EarlyStopping ---\n",
    "# Stops training if the monitored metric (val_accuracy) doesn't improve for 'patience' epochs.\n",
    "# This helps prevent overfitting and saves training time.\n",
    "early_stopping = EarlyStopping(monitor='val_accuracy',\n",
    "                               patience=15, # Number of epochs with no improvement after which training will be stopped\n",
    "                               verbose=1,\n",
    "                               restore_best_weights=True) # Restore model weights from the epoch with the best value of the monitored quantity\n",
    "\n",
    "# --- 3. ReduceLROnPlateau ---\n",
    "# Reduces the learning rate when a metric (val_accuracy) has stopped improving.\n",
    "# This can help the model find a better local minimum by taking smaller steps.\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_accuracy',\n",
    "                              factor=0.2, # Factor by which the learning rate will be reduced (new_lr = lr * factor)\n",
    "                              patience=7, # Number of epochs with no improvement after which learning rate will be reduced\n",
    "                              verbose=1,\n",
    "                              min_lr=0.00001) # Lower bound on the learning rate\n",
    "\n",
    "# Combine all callbacks into a list\n",
    "callbacks_list = [checkpoint, early_stopping, reduce_lr]\n",
    "\n",
    "print(\"Callbacks defined successfully. The model will be saved, early stopped, and learning rate adjusted automatically.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83bee5d1",
   "metadata": {},
   "source": [
    "Now We Train The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "33c19f20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Starting model training...\n",
      "Training for a maximum of 50 epochs, with EarlyStopping (patience=15) and ReduceLROnPlateau (patience=7).\n",
      "Best model will be saved to: models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Regino Balogo Jr\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 597ms/step - accuracy: 0.2254 - loss: 2.3194\n",
      "Epoch 1: val_accuracy improved from -inf to 0.27888, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m288s\u001b[0m 625ms/step - accuracy: 0.2254 - loss: 2.3188 - val_accuracy: 0.2789 - val_loss: 1.7452 - learning_rate: 0.0010\n",
      "Epoch 2/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:47\u001b[0m 374ms/step - accuracy: 0.3281 - loss: 1.8048"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Regino Balogo Jr\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\trainers\\epoch_iterator.py:107: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
      "  self._interrupted_warning()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 2: val_accuracy improved from 0.27888 to 0.29171, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 20ms/step - accuracy: 0.3281 - loss: 1.8048 - val_accuracy: 0.2917 - val_loss: 1.7171 - learning_rate: 0.0010\n",
      "Epoch 3/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 430ms/step - accuracy: 0.3562 - loss: 1.6674\n",
      "Epoch 3: val_accuracy improved from 0.29171 to 0.47628, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m206s\u001b[0m 459ms/step - accuracy: 0.3562 - loss: 1.6672 - val_accuracy: 0.4763 - val_loss: 1.3661 - learning_rate: 0.0010\n",
      "Epoch 4/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5:16\u001b[0m 708ms/step - accuracy: 0.4688 - loss: 1.4236\n",
      "Epoch 4: val_accuracy did not improve from 0.47628\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 20ms/step - accuracy: 0.4688 - loss: 1.4236 - val_accuracy: 0.4747 - val_loss: 1.3666 - learning_rate: 0.0010\n",
      "Epoch 5/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 405ms/step - accuracy: 0.4460 - loss: 1.4357\n",
      "Epoch 5: val_accuracy did not improve from 0.47628\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m190s\u001b[0m 424ms/step - accuracy: 0.4461 - loss: 1.4357 - val_accuracy: 0.3361 - val_loss: 1.9926 - learning_rate: 0.0010\n",
      "Epoch 6/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:43\u001b[0m 367ms/step - accuracy: 0.5469 - loss: 1.2140\n",
      "Epoch 6: val_accuracy did not improve from 0.47628\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 22ms/step - accuracy: 0.5469 - loss: 1.2140 - val_accuracy: 0.3297 - val_loss: 2.0193 - learning_rate: 0.0010\n",
      "Epoch 7/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 435ms/step - accuracy: 0.4832 - loss: 1.3521\n",
      "Epoch 7: val_accuracy improved from 0.47628 to 0.48884, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m208s\u001b[0m 465ms/step - accuracy: 0.4833 - loss: 1.3520 - val_accuracy: 0.4888 - val_loss: 1.3580 - learning_rate: 0.0010\n",
      "Epoch 8/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3:05\u001b[0m 415ms/step - accuracy: 0.5156 - loss: 1.2971\n",
      "Epoch 8: val_accuracy improved from 0.48884 to 0.49219, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 21ms/step - accuracy: 0.5156 - loss: 1.2971 - val_accuracy: 0.4922 - val_loss: 1.3454 - learning_rate: 0.0010\n",
      "Epoch 9/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 408ms/step - accuracy: 0.5162 - loss: 1.2826\n",
      "Epoch 9: val_accuracy improved from 0.49219 to 0.54925, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 432ms/step - accuracy: 0.5162 - loss: 1.2826 - val_accuracy: 0.5492 - val_loss: 1.1803 - learning_rate: 0.0010\n",
      "Epoch 10/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:44\u001b[0m 368ms/step - accuracy: 0.6094 - loss: 1.1516\n",
      "Epoch 10: val_accuracy improved from 0.54925 to 0.55036, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 20ms/step - accuracy: 0.6094 - loss: 1.1516 - val_accuracy: 0.5504 - val_loss: 1.1776 - learning_rate: 0.0010\n",
      "Epoch 11/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 396ms/step - accuracy: 0.5188 - loss: 1.2586\n",
      "Epoch 11: val_accuracy did not improve from 0.55036\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 415ms/step - accuracy: 0.5188 - loss: 1.2585 - val_accuracy: 0.5179 - val_loss: 1.2812 - learning_rate: 0.0010\n",
      "Epoch 12/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:48\u001b[0m 377ms/step - accuracy: 0.4375 - loss: 1.3561\n",
      "Epoch 12: val_accuracy did not improve from 0.55036\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 19ms/step - accuracy: 0.4375 - loss: 1.3561 - val_accuracy: 0.5282 - val_loss: 1.2460 - learning_rate: 0.0010\n",
      "Epoch 13/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 383ms/step - accuracy: 0.5377 - loss: 1.2153\n",
      "Epoch 13: val_accuracy improved from 0.55036 to 0.57031, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m180s\u001b[0m 402ms/step - accuracy: 0.5377 - loss: 1.2153 - val_accuracy: 0.5703 - val_loss: 1.1359 - learning_rate: 0.0010\n",
      "Epoch 14/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:46\u001b[0m 372ms/step - accuracy: 0.4531 - loss: 1.5066\n",
      "Epoch 14: val_accuracy improved from 0.57031 to 0.57143, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 20ms/step - accuracy: 0.4531 - loss: 1.5066 - val_accuracy: 0.5714 - val_loss: 1.1433 - learning_rate: 0.0010\n",
      "Epoch 15/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 387ms/step - accuracy: 0.5493 - loss: 1.1863\n",
      "Epoch 15: val_accuracy did not improve from 0.57143\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m183s\u001b[0m 408ms/step - accuracy: 0.5493 - loss: 1.1863 - val_accuracy: 0.5693 - val_loss: 1.1270 - learning_rate: 0.0010\n",
      "Epoch 16/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:41\u001b[0m 362ms/step - accuracy: 0.5469 - loss: 1.3645\n",
      "Epoch 16: val_accuracy improved from 0.57143 to 0.57268, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 20ms/step - accuracy: 0.5469 - loss: 1.3645 - val_accuracy: 0.5727 - val_loss: 1.1181 - learning_rate: 0.0010\n",
      "Epoch 17/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 392ms/step - accuracy: 0.5595 - loss: 1.1597\n",
      "Epoch 17: val_accuracy did not improve from 0.57268\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m185s\u001b[0m 413ms/step - accuracy: 0.5595 - loss: 1.1597 - val_accuracy: 0.5455 - val_loss: 1.2676 - learning_rate: 0.0010\n",
      "Epoch 18/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:41\u001b[0m 361ms/step - accuracy: 0.5312 - loss: 1.2628\n",
      "Epoch 18: val_accuracy did not improve from 0.57268\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 20ms/step - accuracy: 0.5312 - loss: 1.2628 - val_accuracy: 0.5483 - val_loss: 1.2560 - learning_rate: 0.0010\n",
      "Epoch 19/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 403ms/step - accuracy: 0.5688 - loss: 1.1381\n",
      "Epoch 19: val_accuracy improved from 0.57268 to 0.59780, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m190s\u001b[0m 424ms/step - accuracy: 0.5688 - loss: 1.1382 - val_accuracy: 0.5978 - val_loss: 1.0611 - learning_rate: 0.0010\n",
      "Epoch 20/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:45\u001b[0m 369ms/step - accuracy: 0.5938 - loss: 1.1375\n",
      "Epoch 20: val_accuracy did not improve from 0.59780\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 20ms/step - accuracy: 0.5938 - loss: 1.1375 - val_accuracy: 0.5956 - val_loss: 1.0611 - learning_rate: 0.0010\n",
      "Epoch 21/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 386ms/step - accuracy: 0.5655 - loss: 1.1423\n",
      "Epoch 21: val_accuracy improved from 0.59780 to 0.60003, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m182s\u001b[0m 405ms/step - accuracy: 0.5655 - loss: 1.1422 - val_accuracy: 0.6000 - val_loss: 1.0609 - learning_rate: 0.0010\n",
      "Epoch 22/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:39\u001b[0m 358ms/step - accuracy: 0.6562 - loss: 0.8970\n",
      "Epoch 22: val_accuracy improved from 0.60003 to 0.60031, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 21ms/step - accuracy: 0.6562 - loss: 0.8970 - val_accuracy: 0.6003 - val_loss: 1.0617 - learning_rate: 0.0010\n",
      "Epoch 23/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 421ms/step - accuracy: 0.5898 - loss: 1.0967\n",
      "Epoch 23: val_accuracy improved from 0.60031 to 0.60463, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m197s\u001b[0m 441ms/step - accuracy: 0.5898 - loss: 1.0968 - val_accuracy: 0.6046 - val_loss: 1.0405 - learning_rate: 0.0010\n",
      "Epoch 24/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3:02\u001b[0m 408ms/step - accuracy: 0.5781 - loss: 1.1876\n",
      "Epoch 24: val_accuracy improved from 0.60463 to 0.60589, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 21ms/step - accuracy: 0.5781 - loss: 1.1876 - val_accuracy: 0.6059 - val_loss: 1.0422 - learning_rate: 0.0010\n",
      "Epoch 25/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 405ms/step - accuracy: 0.5898 - loss: 1.0952\n",
      "Epoch 25: val_accuracy did not improve from 0.60589\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m190s\u001b[0m 425ms/step - accuracy: 0.5898 - loss: 1.0952 - val_accuracy: 0.5682 - val_loss: 1.1454 - learning_rate: 0.0010\n",
      "Epoch 26/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:48\u001b[0m 377ms/step - accuracy: 0.6250 - loss: 1.0164\n",
      "Epoch 26: val_accuracy did not improve from 0.60589\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 19ms/step - accuracy: 0.6250 - loss: 1.0164 - val_accuracy: 0.5660 - val_loss: 1.1499 - learning_rate: 0.0010\n",
      "Epoch 27/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 392ms/step - accuracy: 0.5945 - loss: 1.0724\n",
      "Epoch 27: val_accuracy improved from 0.60589 to 0.60840, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m184s\u001b[0m 412ms/step - accuracy: 0.5945 - loss: 1.0724 - val_accuracy: 0.6084 - val_loss: 1.0442 - learning_rate: 0.0010\n",
      "Epoch 28/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:40\u001b[0m 358ms/step - accuracy: 0.6094 - loss: 1.0176\n",
      "Epoch 28: val_accuracy did not improve from 0.60840\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 19ms/step - accuracy: 0.6094 - loss: 1.0176 - val_accuracy: 0.6074 - val_loss: 1.0458 - learning_rate: 0.0010\n",
      "Epoch 29/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 423ms/step - accuracy: 0.5898 - loss: 1.0758\n",
      "Epoch 29: val_accuracy improved from 0.60840 to 0.61537, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 443ms/step - accuracy: 0.5898 - loss: 1.0758 - val_accuracy: 0.6154 - val_loss: 1.0230 - learning_rate: 0.0010\n",
      "Epoch 30/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:38\u001b[0m 354ms/step - accuracy: 0.6094 - loss: 0.9198\n",
      "Epoch 30: val_accuracy did not improve from 0.61537\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 19ms/step - accuracy: 0.6094 - loss: 0.9198 - val_accuracy: 0.6144 - val_loss: 1.0251 - learning_rate: 0.0010\n",
      "Epoch 31/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 390ms/step - accuracy: 0.5995 - loss: 1.0641\n",
      "Epoch 31: val_accuracy did not improve from 0.61537\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m183s\u001b[0m 409ms/step - accuracy: 0.5995 - loss: 1.0641 - val_accuracy: 0.5935 - val_loss: 1.0998 - learning_rate: 0.0010\n",
      "Epoch 32/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:44\u001b[0m 367ms/step - accuracy: 0.5938 - loss: 0.9752\n",
      "Epoch 32: val_accuracy did not improve from 0.61537\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 20ms/step - accuracy: 0.5938 - loss: 0.9752 - val_accuracy: 0.5960 - val_loss: 1.0930 - learning_rate: 0.0010\n",
      "Epoch 33/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 605ms/step - accuracy: 0.6024 - loss: 1.0585\n",
      "Epoch 33: val_accuracy did not improve from 0.61537\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m328s\u001b[0m 733ms/step - accuracy: 0.6024 - loss: 1.0585 - val_accuracy: 0.6003 - val_loss: 1.0683 - learning_rate: 0.0010\n",
      "Epoch 34/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5:27\u001b[0m 733ms/step - accuracy: 0.7188 - loss: 0.9574\n",
      "Epoch 34: val_accuracy did not improve from 0.61537\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 26ms/step - accuracy: 0.7188 - loss: 0.9574 - val_accuracy: 0.6032 - val_loss: 1.0542 - learning_rate: 0.0010\n",
      "Epoch 35/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 499ms/step - accuracy: 0.6058 - loss: 1.0438\n",
      "Epoch 35: val_accuracy did not improve from 0.61537\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m236s\u001b[0m 527ms/step - accuracy: 0.6059 - loss: 1.0438 - val_accuracy: 0.6098 - val_loss: 1.0448 - learning_rate: 0.0010\n",
      "Epoch 36/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3:03\u001b[0m 409ms/step - accuracy: 0.6719 - loss: 0.9467\n",
      "Epoch 36: val_accuracy did not improve from 0.61537\n",
      "\n",
      "Epoch 36: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 23ms/step - accuracy: 0.6719 - loss: 0.9467 - val_accuracy: 0.6116 - val_loss: 1.0410 - learning_rate: 0.0010\n",
      "Epoch 37/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 492ms/step - accuracy: 0.6248 - loss: 1.0050\n",
      "Epoch 37: val_accuracy improved from 0.61537 to 0.62988, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m232s\u001b[0m 518ms/step - accuracy: 0.6248 - loss: 1.0050 - val_accuracy: 0.6299 - val_loss: 0.9779 - learning_rate: 2.0000e-04\n",
      "Epoch 38/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:44\u001b[0m 367ms/step - accuracy: 0.5781 - loss: 1.1921\n",
      "Epoch 38: val_accuracy improved from 0.62988 to 0.63058, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 20ms/step - accuracy: 0.5781 - loss: 1.1921 - val_accuracy: 0.6306 - val_loss: 0.9778 - learning_rate: 2.0000e-04\n",
      "Epoch 39/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 424ms/step - accuracy: 0.6355 - loss: 0.9800\n",
      "Epoch 39: val_accuracy improved from 0.63058 to 0.63923, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 451ms/step - accuracy: 0.6355 - loss: 0.9800 - val_accuracy: 0.6392 - val_loss: 0.9651 - learning_rate: 2.0000e-04\n",
      "Epoch 40/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:42\u001b[0m 364ms/step - accuracy: 0.7500 - loss: 0.7340\n",
      "Epoch 40: val_accuracy did not improve from 0.63923\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 35ms/step - accuracy: 0.7500 - loss: 0.7340 - val_accuracy: 0.6388 - val_loss: 0.9657 - learning_rate: 2.0000e-04\n",
      "Epoch 41/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 468ms/step - accuracy: 0.6341 - loss: 0.9748\n",
      "Epoch 41: val_accuracy did not improve from 0.63923\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m222s\u001b[0m 494ms/step - accuracy: 0.6341 - loss: 0.9748 - val_accuracy: 0.6309 - val_loss: 0.9740 - learning_rate: 2.0000e-04\n",
      "Epoch 42/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:49\u001b[0m 378ms/step - accuracy: 0.6875 - loss: 0.8144\n",
      "Epoch 42: val_accuracy did not improve from 0.63923\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 19ms/step - accuracy: 0.6875 - loss: 0.8144 - val_accuracy: 0.6317 - val_loss: 0.9746 - learning_rate: 2.0000e-04\n",
      "Epoch 43/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 425ms/step - accuracy: 0.6431 - loss: 0.9629\n",
      "Epoch 43: val_accuracy improved from 0.63923 to 0.64425, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 451ms/step - accuracy: 0.6431 - loss: 0.9629 - val_accuracy: 0.6443 - val_loss: 0.9596 - learning_rate: 2.0000e-04\n",
      "Epoch 44/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3:07\u001b[0m 420ms/step - accuracy: 0.6406 - loss: 0.9533\n",
      "Epoch 44: val_accuracy did not improve from 0.64425\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 23ms/step - accuracy: 0.6406 - loss: 0.9533 - val_accuracy: 0.6440 - val_loss: 0.9593 - learning_rate: 2.0000e-04\n",
      "Epoch 45/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 429ms/step - accuracy: 0.6403 - loss: 0.9647\n",
      "Epoch 45: val_accuracy did not improve from 0.64425\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 458ms/step - accuracy: 0.6403 - loss: 0.9647 - val_accuracy: 0.6412 - val_loss: 0.9598 - learning_rate: 2.0000e-04\n",
      "Epoch 46/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3:11\u001b[0m 427ms/step - accuracy: 0.5938 - loss: 1.1323\n",
      "Epoch 46: val_accuracy did not improve from 0.64425\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 23ms/step - accuracy: 0.5938 - loss: 1.1323 - val_accuracy: 0.6399 - val_loss: 0.9609 - learning_rate: 2.0000e-04\n",
      "Epoch 47/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 484ms/step - accuracy: 0.6466 - loss: 0.9544\n",
      "Epoch 47: val_accuracy did not improve from 0.64425\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m230s\u001b[0m 514ms/step - accuracy: 0.6465 - loss: 0.9544 - val_accuracy: 0.6419 - val_loss: 0.9556 - learning_rate: 2.0000e-04\n",
      "Epoch 48/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3:12\u001b[0m 431ms/step - accuracy: 0.5000 - loss: 1.1488\n",
      "Epoch 48: val_accuracy did not improve from 0.64425\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 21ms/step - accuracy: 0.5000 - loss: 1.1488 - val_accuracy: 0.6416 - val_loss: 0.9546 - learning_rate: 2.0000e-04\n",
      "Epoch 49/50\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 542ms/step - accuracy: 0.6456 - loss: 0.9494\n",
      "Epoch 49: val_accuracy improved from 0.64425 to 0.64662, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m255s\u001b[0m 569ms/step - accuracy: 0.6456 - loss: 0.9494 - val_accuracy: 0.6466 - val_loss: 0.9473 - learning_rate: 2.0000e-04\n",
      "Epoch 50/50\n",
      "\u001b[1m  1/448\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:58\u001b[0m 400ms/step - accuracy: 0.6719 - loss: 0.8287\n",
      "Epoch 50: val_accuracy improved from 0.64662 to 0.64760, saving model to models/emotion_model_best.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 20ms/step - accuracy: 0.6719 - loss: 0.8287 - val_accuracy: 0.6476 - val_loss: 0.9463 - learning_rate: 2.0000e-04\n",
      "Restoring model weights from the end of the best epoch: 50.\n",
      "\n",
      "Training complete. The best model, based on validation accuracy, has been saved to models/emotion_model_best.h5.\n",
      "You can now proceed to evaluate the model's performance and visualize the training history.\n"
     ]
    }
   ],
   "source": [
    "# Cell 8: Train the Model\n",
    "\n",
    "print(\"\\nStarting model training...\")\n",
    "print(f\"Training for a maximum of {NUM_EPOCHS} epochs, with EarlyStopping (patience={early_stopping.patience}) and ReduceLROnPlateau (patience={reduce_lr.patience}).\")\n",
    "print(f\"Best model will be saved to: {checkpoint_path}\")\n",
    "\n",
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // BATCH_SIZE, # Number of batches to draw from the training generator per epoch\n",
    "    epochs=NUM_EPOCHS, # Maximum number of epochs to train for\n",
    "    validation_data=test_generator, # Data on which to evaluate the loss and any model metrics at the end of each epoch\n",
    "    validation_steps=test_generator.samples // BATCH_SIZE, # Number of batches to draw from the validation generator\n",
    "    callbacks=callbacks_list # List of callbacks to apply during training (Checkpoint, EarlyStopping, ReduceLROnPlateau)\n",
    ")\n",
    "\n",
    "print(f\"\\nTraining complete. The best model, based on validation accuracy, has been saved to {checkpoint_path}.\")\n",
    "print(\"You can now proceed to evaluate the model's performance and visualize the training history.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ad23ed9",
   "metadata": {},
   "source": [
    "Model Performance Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a7b74fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Model Evaluation and History Visualization ---\n",
      "\n",
      "Ensuring test_generator and validation_steps are ready...\n",
      "test_generator found in memory.\n",
      "validation_steps not found in memory. Recalculating...\n",
      "validation_steps recalculated: 113\n",
      "\n",
      "Loading the best model for final evaluation...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded best model from models/emotion_model_best.h5\n",
      "\n",
      "Evaluating the best model on the test set...\n",
      "\u001b[1m113/113\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 122ms/step - accuracy: 0.5978 - loss: 1.0373\n",
      "\n",
      "Final Best Model Test Loss: 0.9451\n",
      "Final Best Model Test Accuracy: 0.6481\n",
      "\n",
      "Plotting training history (Accuracy and Loss over Epochs)...\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training history visualization complete. Review messages above for any skipped sections.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import math # Needed for ceil\n",
    "\n",
    "print(\"\\n--- Model Evaluation and History Visualization ---\")\n",
    "\n",
    "DATASET_PATH = 'FER-2013'\n",
    "TEST_DIR = os.path.join(DATASET_PATH, 'test')\n",
    "IMG_HEIGHT = 48\n",
    "IMG_WIDTH = 48\n",
    "BATCH_SIZE = 64\n",
    "NUM_CLASSES = 7\n",
    "emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']\n",
    "\n",
    "checkpoint_path = 'models/emotion_model_best.h5'\n",
    "\n",
    "print(\"\\nEnsuring test_generator and validation_steps are ready...\")\n",
    "\n",
    "# Check if test_generator is in the current session's memory\n",
    "if 'test_generator' not in locals():\n",
    "    print(\"test_generator not found in memory. Attempting to recreate it...\")\n",
    "    from tensorflow.keras.preprocessing.image import ImageDataGenerator # Import again if needed\n",
    "    test_datagen = ImageDataGenerator(rescale=1./255) # Only normalization for test\n",
    "    test_generator = test_datagen.flow_from_directory(\n",
    "        TEST_DIR,\n",
    "        target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "        batch_size=BATCH_SIZE,\n",
    "        color_mode='grayscale',\n",
    "        class_mode='categorical',\n",
    "        shuffle=False\n",
    "    )\n",
    "    print(\"test_generator recreated.\")\n",
    "else:\n",
    "    print(\"test_generator found in memory.\")\n",
    "\n",
    "# Check if validation_steps is in the current session's memory\n",
    "if 'validation_steps' not in locals():\n",
    "    print(\"validation_steps not found in memory. Recalculating...\")\n",
    "    validation_steps = math.ceil(test_generator.samples / BATCH_SIZE)\n",
    "    print(f\"validation_steps recalculated: {validation_steps}\")\n",
    "else:\n",
    "    print(\"validation_steps found in memory.\")\n",
    "\n",
    "# Re-create idx_to_label if needed for plotting (depends on train_generator being available, or just use emotion_labels)\n",
    "if 'idx_to_label' not in locals() and 'train_generator' in locals():\n",
    "    print(\"idx_to_label not found, trying to recreate from train_generator...\")\n",
    "    idx_to_label = {v: k for k, v in train_generator.class_indices.items()}\n",
    "elif 'idx_to_label' not in locals():\n",
    "    print(\"idx_to_label not found and train_generator is not available. Using emotion_labels for general reference.\")\n",
    "    idx_to_label = {i: label for i, label in enumerate(emotion_labels)} # Fallback for display\n",
    "\n",
    "# --- 2. Load the Best Model for Final Evaluation ---\n",
    "print(\"\\nLoading the best model for final evaluation...\")\n",
    "try:\n",
    "    best_model = tf.keras.models.load_model(checkpoint_path)\n",
    "    print(f\"Successfully loaded best model from {checkpoint_path}\")\n",
    "except Exception as e:\n",
    "    print(f\"Error loading best model from {checkpoint_path}: {e}\")\n",
    "    print(\"Please ensure the model file exists at the specified path and is not corrupted.\")\n",
    "    # If the model can't be loaded, we can't proceed with evaluation\n",
    "    best_model = None # Set to None to skip evaluation\n",
    "\n",
    "if best_model:\n",
    "    print(\"\\nEvaluating the best model on the test set...\")\n",
    "    # Ensure test_generator and validation_steps are now defined\n",
    "    if 'test_generator' in locals() and 'validation_steps' in locals():\n",
    "        loss, accuracy = best_model.evaluate(test_generator, steps=validation_steps, verbose=1)\n",
    "        print(f\"\\nFinal Best Model Test Loss: {loss:.4f}\")\n",
    "        print(f\"Final Best Model Test Accuracy: {accuracy:.4f}\")\n",
    "    else:\n",
    "        print(\"Cannot perform evaluation: test_generator or validation_steps are still missing.\")\n",
    "else:\n",
    "    print(\"Skipping evaluation as the model could not be loaded.\")\n",
    "\n",
    "\n",
    "#  Plotting Training History\n",
    "print(\"\\nPlotting training history (Accuracy and Loss over Epochs)...\")\n",
    "\n",
    "# Check if 'history' object is still in memory. If the kernel was restarted, it likely won't be.\n",
    "if 'history' not in locals():\n",
    "    print(\"Warning: 'history' object not found in memory.\")\n",
    "    print(\"This means the training process (Cell 8) was not run in this session or the kernel was restarted after it completed.\")\n",
    "    print(\"Training plots (accuracy/loss curves) cannot be generated without the 'history' object.\")\n",
    "    print(\"Tip: In future projects, you can save `history.history` (which is a dictionary) to a JSON or pickle file after training, then load it to plot later.\")\n",
    "else:\n",
    "    plt.figure(figsize=(14, 6))\n",
    "\n",
    "    # Subplot 1: Training & Validation Accuracy\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
    "    plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "    plt.title('Accuracy over Epochs')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "\n",
    "    # Subplot 2: Training & Validation Loss\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history.history['loss'], label='Train Loss')\n",
    "    plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "    plt.title('Loss over Epochs')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "print(\"\\nTraining history visualization complete. Review messages above for any skipped sections.\")"
   ]
  }
 ],
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