FF + Transformer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8994377d-cf1c-44d0-b7ba-0cc902334440",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers,models\n",
    "from tensorflow.keras.losses import BinaryCrossentropy\n",
    "from tensorflow.keras.optimizers import Adam,SGD\n",
    "from tensorflow.keras.utils import image_dataset_from_directory\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import keras\n",
    "from keras import layers,ops\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9405aff0-8c43-435e-b763-f35510739f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = 2\n",
    "input_shape = (224,224, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0111ebb6-2af8-4262-b827-c5a929aa2c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"Features/img_data.pkl\", \"rb\") as f:\n",
    "    image_data = pickle.load(f)\n",
    "with open (\"Features/labels.pkl\",\"rb\") as f:\n",
    "    labels = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1805bcb5-4765-430f-a62b-65be56ba0f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3000, 224, 224, 3), (3000,))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_500_images = image_data[:1500]\n",
    "last_500_images = image_data[-1500:]\n",
    "images_subset = np.concatenate([first_500_images,last_500_images],axis = 0)\n",
    "\n",
    "first_500_labels = labels[:1500]\n",
    "last_500_labels = labels[-1500:]\n",
    "labels_subset = np.concatenate([first_500_labels,last_500_labels],axis = 0)\n",
    "images_subset.shape, labels_subset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6b7a83ba-14ec-48e7-a266-20c9913355cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2100, 224, 224, 3)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_val, y_train, y_val = train_test_split(images_subset,labels_subset,test_size = 0.3)\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "47d66ba0-f766-434a-b681-d4a2726e8cda",
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.001\n",
    "weight_decay = 0.0001\n",
    "batch_size = 256\n",
    "num_epochs = 50  # For real training, use num_epochs=100. 10 is a test value\n",
    "image_size = 224  # We'll resize input images to this size\n",
    "patch_size = 16  # Size of the patches to be extract from the input images\n",
    "num_patches = (image_size // patch_size) ** 2\n",
    "projection_dim = 64\n",
    "num_heads = 4\n",
    "transformer_units = [\n",
    "    projection_dim * 2,\n",
    "    projection_dim,\n",
    "]\n",
    "transformer_layers = 8\n",
    "mlp_head_units = [\n",
    "    2048,\n",
    "    1024,\n",
    "] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b81bae1c-26f6-48c4-9424-0a2ae6a4bd03",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mlp(x, hidden_units, dropout_rate):\n",
    "    for units in hidden_units:\n",
    "        x = layers.Dense(units, activation=keras.activations.gelu)(x)\n",
    "        x = layers.Dropout(dropout_rate)(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ba4c9b4-3160-4335-b943-224dd3e85781",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Patches(layers.Layer):\n",
    "    def __init__(self, patch_size):\n",
    "        super().__init__()\n",
    "        self.patch_size = patch_size\n",
    "\n",
    "    def call(self, images):\n",
    "        input_shape = ops.shape(images)\n",
    "        batch_size = input_shape[0]\n",
    "        height = input_shape[1]\n",
    "        width = input_shape[2]\n",
    "        channels = input_shape[3]\n",
    "        num_patches_h = height // self.patch_size\n",
    "        num_patches_w = width // self.patch_size\n",
    "        patches = keras.ops.image.extract_patches(images, size=self.patch_size)\n",
    "        patches = ops.reshape(\n",
    "            patches,\n",
    "            (\n",
    "                batch_size,\n",
    "                num_patches_h * num_patches_w,\n",
    "                self.patch_size * self.patch_size * channels,\n",
    "            ),\n",
    "        )\n",
    "        return patches\n",
    "\n",
    "    def get_config(self):\n",
    "        config = super().get_config()\n",
    "        config.update({\"patch_size\": self.patch_size})\n",
    "        return config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "0b7c239a-aa77-4281-a894-0673e9fd843b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image size: 224 X 224\n",
      "Patch size: 16 X 16\n",
      "Patches per image: 196\n",
      "Elements per patch: 768\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 196 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 4))\n",
    "image = x_train[np.random.choice(range(x_train.shape[0]))]\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "\n",
    "resized_image = ops.image.resize(\n",
    "    ops.convert_to_tensor([image]), size=(image_size, image_size)\n",
    ")\n",
    "patches = Patches(patch_size)(resized_image)\n",
    "print(f\"Image size: {image_size} X {image_size}\")\n",
    "print(f\"Patch size: {patch_size} X {patch_size}\")\n",
    "print(f\"Patches per image: {patches.shape[1]}\")\n",
    "print(f\"Elements per patch: {patches.shape[-1]}\")\n",
    "\n",
    "n = int(np.sqrt(patches.shape[1]))\n",
    "plt.figure(figsize=(4, 4))\n",
    "for i, patch in enumerate(patches[0]):\n",
    "    ax = plt.subplot(n, n, i + 1)\n",
    "    patch_img = ops.reshape(patch, (patch_size, patch_size, 3))\n",
    "    plt.imshow(ops.convert_to_numpy(patch_img))\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a243bda-da45-409f-9ee5-3b10d84bb24c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PatchEncoder(layers.Layer):\n",
    "    def __init__(self, num_patches, projection_dim):\n",
    "        super().__init__()\n",
    "        self.num_patches = num_patches\n",
    "        self.projection = layers.Dense(units=projection_dim)\n",
    "        self.position_embedding = layers.Embedding(\n",
    "            input_dim=num_patches, output_dim=projection_dim\n",
    "        )\n",
    "\n",
    "    def call(self, patch):\n",
    "        positions = ops.expand_dims(\n",
    "            ops.arange(start=0, stop=self.num_patches, step=1), axis=0\n",
    "        )\n",
    "        projected_patches = self.projection(patch)\n",
    "        encoded = projected_patches + self.position_embedding(positions)\n",
    "        return encoded\n",
    "\n",
    "    def get_config(self):\n",
    "        config = super().get_config()\n",
    "        config.update({\"num_patches\": self.num_patches})\n",
    "        return config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "26ea6748-08d7-4be1-9e0f-342c90741d34",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_augmentation = keras.Sequential(\n",
    "    [\n",
    "        layers.Normalization(),\n",
    "        layers.Resizing(image_size, image_size),\n",
    "        layers.RandomFlip(\"horizontal\"),\n",
    "        layers.RandomRotation(factor=0.02),\n",
    "        layers.RandomZoom(height_factor=0.2, width_factor=0.2),\n",
    "    ],\n",
    "    name=\"data_augmentation\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3ad167b-a849-4dd8-85d0-14a670bc9c9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_vit_classifier():\n",
    "    inputs = keras.Input(shape=input_shape)\n",
    "    # Augment data.\n",
    "    augmented = data_augmentation(inputs)\n",
    "    # Create patches.\n",
    "    patches = Patches(patch_size)(augmented)\n",
    "    # Encode patches.\n",
    "    encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)\n",
    "\n",
    "    # Create multiple layers of the Transformer block.\n",
    "    for _ in range(transformer_layers):\n",
    "        # Layer normalization 1.\n",
    "        x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\n",
    "        # Create a multi-head attention layer.\n",
    "        attention_output = layers.MultiHeadAttention(\n",
    "            num_heads=num_heads, key_dim=projection_dim, dropout=0.1\n",
    "        )(x1, x1)\n",
    "        # Skip connection 1.\n",
    "        x2 = layers.Add()([attention_output, encoded_patches])\n",
    "        # Layer normalization 2.\n",
    "        x3 = layers.LayerNormalization(epsilon=1e-6)(x2)\n",
    "        # MLP.\n",
    "        x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)\n",
    "        # Skip connection 2.\n",
    "        encoded_patches = layers.Add()([x3, x2])\n",
    "\n",
    "    # Create a [batch_size, projection_dim] tensor.\n",
    "    representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\n",
    "    representation = layers.Flatten()(representation)\n",
    "    representation = layers.Dropout(0.5)(representation)\n",
    "    # Add MLP.\n",
    "    features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.5)\n",
    "    # Classify outputs.\n",
    "    logits = layers.Dense(num_classes)(features)\n",
    "    # Create the Keras model.\n",
    "    model = keras.Model(inputs=inputs, outputs=logits)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78ead25b-6362-4875-b879-c7a672654fcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\gandl\\anaconda3windows\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\core.py:216: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\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: \"functional_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_1\"\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>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)       │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ data_augmentation             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)       │               <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)                  │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ patches (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Patches</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ data_augmentation[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ patch_encoder (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">PatchEncoder</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │          <span style=\"color: #00af00; text-decoration-color: #00af00\">61,760</span> │ patches[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ patch_encoder[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │          <span style=\"color: #00af00; text-decoration-color: #00af00\">66,368</span> │ layer_normalization[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>], │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MultiHeadAttention</span>)          │                           │                 │ layer_normalization[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multi_head_attention[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                               │                           │                 │ patch_encoder[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_1         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,320</span> │ layer_normalization_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_2 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ dropout_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>], add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_2         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention_1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │          <span style=\"color: #00af00; text-decoration-color: #00af00\">66,368</span> │ layer_normalization_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MultiHeadAttention</span>)          │                           │                 │ layer_normalization_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multi_head_attention_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                               │                           │                 │ add_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_3         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_3 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,320</span> │ layer_normalization_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_4 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_4 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ dropout_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_5 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],           │\n",
       "│                               │                           │                 │ add_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_4         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention_2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │          <span style=\"color: #00af00; text-decoration-color: #00af00\">66,368</span> │ layer_normalization_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MultiHeadAttention</span>)          │                           │                 │ layer_normalization_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multi_head_attention_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                               │                           │                 │ add_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_5         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_5 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,320</span> │ layer_normalization_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_7 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_6 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ dropout_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_8 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],           │\n",
       "│                               │                           │                 │ add_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_6         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention_3        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │          <span style=\"color: #00af00; text-decoration-color: #00af00\">66,368</span> │ layer_normalization_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MultiHeadAttention</span>)          │                           │                 │ layer_normalization_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multi_head_attention_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                               │                           │                 │ add_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_7         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_7 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,320</span> │ layer_normalization_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_10 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_8 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ dropout_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_11 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ add_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_8         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention_4        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │          <span style=\"color: #00af00; text-decoration-color: #00af00\">66,368</span> │ layer_normalization_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MultiHeadAttention</span>)          │                           │                 │ layer_normalization_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multi_head_attention_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                               │                           │                 │ add_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_9         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_9 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,320</span> │ layer_normalization_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_13 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_10 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ dropout_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_14 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ add_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_10        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention_5        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │          <span style=\"color: #00af00; text-decoration-color: #00af00\">66,368</span> │ layer_normalization_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MultiHeadAttention</span>)          │                           │                 │ layer_normalization_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multi_head_attention_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                               │                           │                 │ add_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_11        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_11 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,320</span> │ layer_normalization_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_16 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_12 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ dropout_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_17 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ add_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_12        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention_6        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │          <span style=\"color: #00af00; text-decoration-color: #00af00\">66,368</span> │ layer_normalization_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MultiHeadAttention</span>)          │                           │                 │ layer_normalization_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multi_head_attention_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                               │                           │                 │ add_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_13        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_13 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,320</span> │ layer_normalization_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_19 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_14 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ dropout_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_20 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_20[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ add_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_14        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention_7        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │          <span style=\"color: #00af00; text-decoration-color: #00af00\">66,368</span> │ layer_normalization_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MultiHeadAttention</span>)          │                           │                 │ layer_normalization_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multi_head_attention_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                               │                           │                 │ add_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_15        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_15 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,320</span> │ layer_normalization_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_22 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_16 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ dropout_22[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_23 (<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\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ add_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_16        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</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\">12544</span>)             │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ layer_normalization_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_24 (<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\">12544</span>)             │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ flatten[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_17 (<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\">2048</span>)              │      <span style=\"color: #00af00; text-decoration-color: #00af00\">25,692,160</span> │ dropout_24[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_25 (<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\">2048</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_18 (<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\">1024</span>)              │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,098,176</span> │ dropout_25[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_26 (<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\">1024</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_19 (<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\">2</span>)                 │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,050</span> │ dropout_26[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</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┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m)       │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ data_augmentation             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m)       │               \u001b[38;5;34m7\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)                  │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ patches (\u001b[38;5;33mPatches\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m768\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ data_augmentation[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ patch_encoder (\u001b[38;5;33mPatchEncoder\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │          \u001b[38;5;34m61,760\u001b[0m │ patches[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ patch_encoder[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │          \u001b[38;5;34m66,368\u001b[0m │ layer_normalization[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│ (\u001b[38;5;33mMultiHeadAttention\u001b[0m)          │                           │                 │ layer_normalization[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add (\u001b[38;5;33mAdd\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ multi_head_attention[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
       "│                               │                           │                 │ patch_encoder[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_1         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                  │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │           \u001b[38;5;34m8,320\u001b[0m │ layer_normalization_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ dense_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │           \u001b[38;5;34m8,256\u001b[0m │ dropout_1[\u001b[38;5;34m0\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;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dense_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_1 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_2         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention_1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │          \u001b[38;5;34m66,368\u001b[0m │ layer_normalization_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mMultiHeadAttention\u001b[0m)          │                           │                 │ layer_normalization_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_2 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ multi_head_attention_1[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                               │                           │                 │ add_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_3         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │           \u001b[38;5;34m8,320\u001b[0m │ layer_normalization_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │           \u001b[38;5;34m8,256\u001b[0m │ dropout_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dense_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_3 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dropout_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],           │\n",
       "│                               │                           │                 │ add_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_4         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention_2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │          \u001b[38;5;34m66,368\u001b[0m │ layer_normalization_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mMultiHeadAttention\u001b[0m)          │                           │                 │ layer_normalization_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_4 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ multi_head_attention_2[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                               │                           │                 │ add_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_5         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │           \u001b[38;5;34m8,320\u001b[0m │ layer_normalization_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ dense_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │           \u001b[38;5;34m8,256\u001b[0m │ dropout_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dense_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_5 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dropout_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],           │\n",
       "│                               │                           │                 │ add_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_6         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention_3        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │          \u001b[38;5;34m66,368\u001b[0m │ layer_normalization_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mMultiHeadAttention\u001b[0m)          │                           │                 │ layer_normalization_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_6 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ multi_head_attention_3[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                               │                           │                 │ add_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_7         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │           \u001b[38;5;34m8,320\u001b[0m │ layer_normalization_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_10 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ dense_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │           \u001b[38;5;34m8,256\u001b[0m │ dropout_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_11 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dense_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_7 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dropout_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ add_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_8         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention_4        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │          \u001b[38;5;34m66,368\u001b[0m │ layer_normalization_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mMultiHeadAttention\u001b[0m)          │                           │                 │ layer_normalization_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_8 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ multi_head_attention_4[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                               │                           │                 │ add_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_9         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │           \u001b[38;5;34m8,320\u001b[0m │ layer_normalization_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_13 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ dense_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │           \u001b[38;5;34m8,256\u001b[0m │ dropout_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_14 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dense_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_9 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dropout_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ add_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_10        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention_5        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │          \u001b[38;5;34m66,368\u001b[0m │ layer_normalization_10[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mMultiHeadAttention\u001b[0m)          │                           │                 │ layer_normalization_10[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_10 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ multi_head_attention_5[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                               │                           │                 │ add_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_11        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │           \u001b[38;5;34m8,320\u001b[0m │ layer_normalization_11[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_16 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ dense_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_12 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │           \u001b[38;5;34m8,256\u001b[0m │ dropout_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_17 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dense_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_11 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dropout_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ add_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_12        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention_6        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │          \u001b[38;5;34m66,368\u001b[0m │ layer_normalization_12[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mMultiHeadAttention\u001b[0m)          │                           │                 │ layer_normalization_12[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_12 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ multi_head_attention_6[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                               │                           │                 │ add_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_13        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_13 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │           \u001b[38;5;34m8,320\u001b[0m │ layer_normalization_13[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_19 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ dense_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_14 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │           \u001b[38;5;34m8,256\u001b[0m │ dropout_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_20 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dense_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_13 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dropout_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ add_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_14        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ multi_head_attention_7        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │          \u001b[38;5;34m66,368\u001b[0m │ layer_normalization_14[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mMultiHeadAttention\u001b[0m)          │                           │                 │ layer_normalization_14[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_14 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ multi_head_attention_7[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                               │                           │                 │ add_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_15        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_15 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │           \u001b[38;5;34m8,320\u001b[0m │ layer_normalization_15[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_22 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ dense_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_16 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │           \u001b[38;5;34m8,256\u001b[0m │ dropout_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_23 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dense_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_15 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ dropout_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ add_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ layer_normalization_16        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │ add_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12544\u001b[0m)             │               \u001b[38;5;34m0\u001b[0m │ layer_normalization_16[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_24 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12544\u001b[0m)             │               \u001b[38;5;34m0\u001b[0m │ flatten[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_17 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)              │      \u001b[38;5;34m25,692,160\u001b[0m │ dropout_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_25 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ dense_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_18 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)              │       \u001b[38;5;34m2,098,176\u001b[0m │ dropout_25[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_26 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ dense_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_19 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)                 │           \u001b[38;5;34m2,050\u001b[0m │ dropout_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\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\">28,519,881</span> (108.79 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m28,519,881\u001b[0m (108.79 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\">28,519,874</span> (108.79 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m28,519,874\u001b[0m (108.79 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\">7</span> (32.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m7\u001b[0m (32.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vit_classifier = create_vit_classifier()\n",
    "vit_classifier.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "c0df5216-0575-40a8-800e-e0c58b0d829e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 5s/step - accuracy: 0.4879 - loss: 12.0531 - top-5-accuracy: 1.0000 - val_accuracy: 0.5011 - val_loss: 1.9092 - val_top-5-accuracy: 1.0000\n",
      "Epoch 2/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 4s/step - accuracy: 0.5379 - loss: 2.3535 - top-5-accuracy: 1.0000 - val_accuracy: 0.5011 - val_loss: 1.4470 - val_top-5-accuracy: 1.0000\n",
      "Epoch 3/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 5s/step - accuracy: 0.5301 - loss: 1.6864 - top-5-accuracy: 1.0000 - val_accuracy: 0.6256 - val_loss: 0.6341 - val_top-5-accuracy: 1.0000\n",
      "Epoch 4/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 4s/step - accuracy: 0.5835 - loss: 0.8792 - top-5-accuracy: 1.0000 - val_accuracy: 0.6322 - val_loss: 0.6513 - val_top-5-accuracy: 1.0000\n",
      "Epoch 5/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 6s/step - accuracy: 0.6009 - loss: 0.7724 - top-5-accuracy: 1.0000 - val_accuracy: 0.7544 - val_loss: 0.6270 - val_top-5-accuracy: 1.0000\n",
      "Epoch 6/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 5s/step - accuracy: 0.6467 - loss: 0.6758 - top-5-accuracy: 1.0000 - val_accuracy: 0.7111 - val_loss: 0.6115 - val_top-5-accuracy: 1.0000\n",
      "Epoch 7/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 4s/step - accuracy: 0.6330 - loss: 0.6693 - top-5-accuracy: 1.0000 - val_accuracy: 0.7456 - val_loss: 0.6234 - val_top-5-accuracy: 1.0000\n",
      "Epoch 8/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 4s/step - accuracy: 0.6282 - loss: 0.6533 - top-5-accuracy: 1.0000 - val_accuracy: 0.7122 - val_loss: 0.6058 - val_top-5-accuracy: 1.0000\n",
      "Epoch 9/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 4s/step - accuracy: 0.6610 - loss: 0.6171 - top-5-accuracy: 1.0000 - val_accuracy: 0.7522 - val_loss: 0.5933 - val_top-5-accuracy: 1.0000\n",
      "Epoch 10/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 4s/step - accuracy: 0.6574 - loss: 0.6334 - top-5-accuracy: 1.0000 - val_accuracy: 0.7511 - val_loss: 0.5747 - val_top-5-accuracy: 1.0000\n",
      "Epoch 11/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 5s/step - accuracy: 0.6693 - loss: 0.6025 - top-5-accuracy: 1.0000 - val_accuracy: 0.7356 - val_loss: 0.5675 - val_top-5-accuracy: 1.0000\n",
      "Epoch 12/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 4s/step - accuracy: 0.6609 - loss: 0.6295 - top-5-accuracy: 1.0000 - val_accuracy: 0.7467 - val_loss: 0.5305 - val_top-5-accuracy: 1.0000\n",
      "Epoch 13/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 4s/step - accuracy: 0.6791 - loss: 0.5983 - top-5-accuracy: 1.0000 - val_accuracy: 0.7478 - val_loss: 0.5307 - val_top-5-accuracy: 1.0000\n",
      "Epoch 14/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 5s/step - accuracy: 0.6920 - loss: 0.5931 - top-5-accuracy: 1.0000 - val_accuracy: 0.7489 - val_loss: 0.5344 - val_top-5-accuracy: 1.0000\n",
      "Epoch 15/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 4s/step - accuracy: 0.6852 - loss: 0.5988 - top-5-accuracy: 1.0000 - val_accuracy: 0.7333 - val_loss: 0.5464 - val_top-5-accuracy: 1.0000\n",
      "Epoch 16/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 5s/step - accuracy: 0.6875 - loss: 0.6025 - top-5-accuracy: 1.0000 - val_accuracy: 0.7278 - val_loss: 0.5656 - val_top-5-accuracy: 1.0000\n",
      "Epoch 17/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 5s/step - accuracy: 0.6772 - loss: 0.5899 - top-5-accuracy: 1.0000 - val_accuracy: 0.7589 - val_loss: 0.5266 - val_top-5-accuracy: 1.0000\n",
      "Epoch 18/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 4s/step - accuracy: 0.7034 - loss: 0.5759 - top-5-accuracy: 1.0000 - val_accuracy: 0.7478 - val_loss: 0.5032 - val_top-5-accuracy: 1.0000\n",
      "Epoch 19/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 4s/step - accuracy: 0.7023 - loss: 0.5740 - top-5-accuracy: 1.0000 - val_accuracy: 0.7733 - val_loss: 0.5192 - val_top-5-accuracy: 1.0000\n",
      "Epoch 20/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 5s/step - accuracy: 0.7211 - loss: 0.5511 - top-5-accuracy: 1.0000 - val_accuracy: 0.7611 - val_loss: 0.5360 - val_top-5-accuracy: 1.0000\n",
      "Epoch 21/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 4s/step - accuracy: 0.7043 - loss: 0.5696 - top-5-accuracy: 1.0000 - val_accuracy: 0.7478 - val_loss: 0.5210 - val_top-5-accuracy: 1.0000\n",
      "Epoch 22/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 5s/step - accuracy: 0.7432 - loss: 0.5386 - top-5-accuracy: 1.0000 - val_accuracy: 0.7844 - val_loss: 0.5478 - val_top-5-accuracy: 1.0000\n",
      "Epoch 23/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 4s/step - accuracy: 0.7096 - loss: 0.5687 - top-5-accuracy: 1.0000 - val_accuracy: 0.7778 - val_loss: 0.5326 - val_top-5-accuracy: 1.0000\n",
      "Epoch 24/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 4s/step - accuracy: 0.7128 - loss: 0.5454 - top-5-accuracy: 1.0000 - val_accuracy: 0.7433 - val_loss: 0.5373 - val_top-5-accuracy: 1.0000\n",
      "Epoch 25/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 4s/step - accuracy: 0.7186 - loss: 0.5561 - top-5-accuracy: 1.0000 - val_accuracy: 0.7800 - val_loss: 0.4879 - val_top-5-accuracy: 1.0000\n",
      "Epoch 26/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 4s/step - accuracy: 0.7560 - loss: 0.5265 - top-5-accuracy: 1.0000 - val_accuracy: 0.7811 - val_loss: 0.4803 - val_top-5-accuracy: 1.0000\n",
      "Epoch 27/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 5s/step - accuracy: 0.7447 - loss: 0.5330 - top-5-accuracy: 1.0000 - val_accuracy: 0.7889 - val_loss: 0.4974 - val_top-5-accuracy: 1.0000\n",
      "Epoch 28/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 4s/step - accuracy: 0.7487 - loss: 0.5198 - top-5-accuracy: 1.0000 - val_accuracy: 0.7633 - val_loss: 0.5143 - val_top-5-accuracy: 1.0000\n",
      "Epoch 29/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 4s/step - accuracy: 0.7094 - loss: 0.5595 - top-5-accuracy: 1.0000 - val_accuracy: 0.7867 - val_loss: 0.4900 - val_top-5-accuracy: 1.0000\n",
      "Epoch 30/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 4s/step - accuracy: 0.7275 - loss: 0.5347 - top-5-accuracy: 1.0000 - val_accuracy: 0.7656 - val_loss: 0.4665 - val_top-5-accuracy: 1.0000\n",
      "Epoch 31/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 4s/step - accuracy: 0.7252 - loss: 0.5376 - top-5-accuracy: 1.0000 - val_accuracy: 0.7789 - val_loss: 0.4724 - val_top-5-accuracy: 1.0000\n",
      "Epoch 32/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 4s/step - accuracy: 0.7546 - loss: 0.5150 - top-5-accuracy: 1.0000 - val_accuracy: 0.7700 - val_loss: 0.5094 - val_top-5-accuracy: 1.0000\n",
      "Epoch 33/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 4s/step - accuracy: 0.7366 - loss: 0.5189 - top-5-accuracy: 1.0000 - val_accuracy: 0.7889 - val_loss: 0.4945 - val_top-5-accuracy: 1.0000\n",
      "Epoch 34/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 4s/step - accuracy: 0.7774 - loss: 0.5063 - top-5-accuracy: 1.0000 - val_accuracy: 0.7922 - val_loss: 0.4813 - val_top-5-accuracy: 1.0000\n",
      "Epoch 35/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 5s/step - accuracy: 0.7637 - loss: 0.4932 - top-5-accuracy: 1.0000 - val_accuracy: 0.7989 - val_loss: 0.4518 - val_top-5-accuracy: 1.0000\n",
      "Epoch 36/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 4s/step - accuracy: 0.7591 - loss: 0.5201 - top-5-accuracy: 1.0000 - val_accuracy: 0.7933 - val_loss: 0.4760 - val_top-5-accuracy: 1.0000\n",
      "Epoch 37/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 4s/step - accuracy: 0.7638 - loss: 0.4972 - top-5-accuracy: 1.0000 - val_accuracy: 0.7833 - val_loss: 0.4943 - val_top-5-accuracy: 1.0000\n",
      "Epoch 38/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 4s/step - accuracy: 0.7641 - loss: 0.5071 - top-5-accuracy: 1.0000 - val_accuracy: 0.7922 - val_loss: 0.4764 - val_top-5-accuracy: 1.0000\n",
      "Epoch 39/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 4s/step - accuracy: 0.7609 - loss: 0.5124 - top-5-accuracy: 1.0000 - val_accuracy: 0.7911 - val_loss: 0.4614 - val_top-5-accuracy: 1.0000\n",
      "Epoch 40/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 4s/step - accuracy: 0.7633 - loss: 0.4899 - top-5-accuracy: 1.0000 - val_accuracy: 0.7756 - val_loss: 0.4914 - val_top-5-accuracy: 1.0000\n",
      "Epoch 41/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 4s/step - accuracy: 0.7616 - loss: 0.4996 - top-5-accuracy: 1.0000 - val_accuracy: 0.8033 - val_loss: 0.4615 - val_top-5-accuracy: 1.0000\n",
      "Epoch 42/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 5s/step - accuracy: 0.7581 - loss: 0.4797 - top-5-accuracy: 1.0000 - val_accuracy: 0.7889 - val_loss: 0.4657 - val_top-5-accuracy: 1.0000\n",
      "Epoch 43/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 4s/step - accuracy: 0.7593 - loss: 0.5053 - top-5-accuracy: 1.0000 - val_accuracy: 0.8000 - val_loss: 0.4351 - val_top-5-accuracy: 1.0000\n",
      "Epoch 44/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 4s/step - accuracy: 0.7714 - loss: 0.4972 - top-5-accuracy: 1.0000 - val_accuracy: 0.7956 - val_loss: 0.4732 - val_top-5-accuracy: 1.0000\n",
      "Epoch 45/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 4s/step - accuracy: 0.7731 - loss: 0.4886 - top-5-accuracy: 1.0000 - val_accuracy: 0.7967 - val_loss: 0.4511 - val_top-5-accuracy: 1.0000\n",
      "Epoch 46/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 4s/step - accuracy: 0.7902 - loss: 0.4625 - top-5-accuracy: 1.0000 - val_accuracy: 0.7800 - val_loss: 0.4774 - val_top-5-accuracy: 1.0000\n",
      "Epoch 47/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 4s/step - accuracy: 0.7800 - loss: 0.4663 - top-5-accuracy: 1.0000 - val_accuracy: 0.8189 - val_loss: 0.4469 - val_top-5-accuracy: 1.0000\n",
      "Epoch 48/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 4s/step - accuracy: 0.7828 - loss: 0.4744 - top-5-accuracy: 1.0000 - val_accuracy: 0.8056 - val_loss: 0.4519 - val_top-5-accuracy: 1.0000\n",
      "Epoch 49/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 4s/step - accuracy: 0.7767 - loss: 0.4690 - top-5-accuracy: 1.0000 - val_accuracy: 0.8144 - val_loss: 0.4456 - val_top-5-accuracy: 1.0000\n",
      "Epoch 50/50\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 4s/step - accuracy: 0.7700 - loss: 0.4882 - top-5-accuracy: 1.0000 - val_accuracy: 0.7911 - val_loss: 0.4751 - val_top-5-accuracy: 1.0000\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 138ms/step - accuracy: 0.8168 - loss: 0.4431 - top-5-accuracy: 1.0000\n",
      "Test accuracy: 81.89%\n",
      "Test top 5 accuracy: 100.0%\n"
     ]
    }
   ],
   "source": [
    "def run_experiment(model):\n",
    "    optimizer = keras.optimizers.AdamW(\n",
    "        learning_rate=learning_rate, weight_decay=weight_decay\n",
    "    )\n",
    "\n",
    "    model.compile(\n",
    "        optimizer=optimizer,\n",
    "        loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "        metrics=[\n",
    "            keras.metrics.SparseCategoricalAccuracy(name=\"accuracy\"),\n",
    "            keras.metrics.SparseTopKCategoricalAccuracy(5, name=\"top-5-accuracy\"),\n",
    "        ],\n",
    "    )\n",
    "\n",
    "    checkpoint_filepath = \"/tmp/checkpoint.weights.h5\"\n",
    "    checkpoint_callback = keras.callbacks.ModelCheckpoint(\n",
    "        checkpoint_filepath,\n",
    "        monitor=\"val_accuracy\",\n",
    "        save_best_only=True,\n",
    "        save_weights_only=True,\n",
    "    )\n",
    "\n",
    "    history = model.fit(\n",
    "        x=x_train,\n",
    "        y=y_train,\n",
    "        batch_size=batch_size,\n",
    "        epochs=num_epochs,\n",
    "        validation_data = (x_val,y_val),\n",
    "        callbacks=[checkpoint_callback],\n",
    "    )\n",
    "\n",
    "    model.load_weights(checkpoint_filepath)\n",
    "    _, accuracy, top_5_accuracy = model.evaluate(x_val, y_val)\n",
    "    print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")\n",
    "    print(f\"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%\")\n",
    "\n",
    "    return history\n",
    "\n",
    "\n",
    "vit_classifier = create_vit_classifier()\n",
    "history = run_experiment(vit_classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "64b39cb7-f509-4fa1-8bea-b3e17bd38e47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_history(item):\n",
    "    plt.plot(history.history[item], label=item)\n",
    "    plt.plot(history.history[\"val_\" + item], label=\"val_\" + item)\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(item)\n",
    "    plt.title(\"Train and Validation {} Over Epochs\".format(item), fontsize=14)\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_history(\"loss\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c696d680-a35d-4bdb-8bad-51b780f7865a",
   "metadata": {},
   "source": [
    "Accuracy  = 0.790\n",
    "Val_acc = 0.820"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "5596a6be-29e2-4f1f-beac-cc2c5e84cc3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_history(item):\n",
    "    plt.plot(history.history[item], label=item)\n",
    "    plt.plot(history.history[\"val_\" + item], label=\"val_\" + item)\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(item)\n",
    "    plt.title(\"Train and Validation {} Over Epochs\".format(item), fontsize=14)\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_history(\"accuracy\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "68c8ecbc-cbf8-4f29-abc6-190832c4f070",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "history_vit = pd.DataFrame(history.history)\n",
    "history_vit[\"epoch\"] = history.epoch\n",
    "history_vit.to_csv(\"History/FF + Transformer.csv\")"
   ]
  }
 ],
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