Deep Steganography (Keras).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Steganography"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "jGP_5d4HBYTz"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "sN1PP563BYT1"
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "EPOCHS = 100\n",
    "LEARNING_RATE = 1e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ygYSeHxPBYT2",
    "outputId": "9e1e4df3-6b3d-42ed-ddb0-7fcb1ca2cf70"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "tf.config.list_physical_devices('GPU')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "jX1J3f_aBYT3"
   },
   "outputs": [],
   "source": [
    "class ConvLayer(tf.keras.layers.Layer):\n",
    "    def __init__(self, n_layers, filters=50, kernel_size=(3, 3), activation=tf.nn.relu, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.convs = []\n",
    "        for conv in range(n_layers):\n",
    "            self.convs.append(\n",
    "                tf.keras.layers.Conv2D(filters=filters, kernel_size=kernel_size, activation=activation, padding='same')\n",
    "            )\n",
    "\n",
    "    def call(self, input_tensor, training=False):\n",
    "        x = self.convs[0](input_tensor, training=training)\n",
    "        for i in range(1, len(self.convs)):\n",
    "            x = self.convs[i](x, training=training)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "hXTlex7LBYT4"
   },
   "outputs": [],
   "source": [
    "class PrepLayer(tf.keras.layers.Layer):\n",
    "    def __init__(self, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.conv_layer_4_3x3 = ConvLayer(4, filters=50, kernel_size=(3, 3), activation=tf.nn.relu)\n",
    "        self.conv_layer_4_4x4 = ConvLayer(4, filters=50, kernel_size=(4, 4), activation=tf.nn.relu)\n",
    "        self.conv_layer_4_5x5 = ConvLayer(4, filters=50, kernel_size=(5, 5), activation=tf.nn.relu)\n",
    "\n",
    "        self.concat_1 = tf.keras.layers.Concatenate(axis=3)\n",
    "\n",
    "        self.conv_1_3x3 = ConvLayer(1, filters=50, kernel_size=(3, 3), activation=tf.nn.relu)\n",
    "        self.conv_1_4x4 = ConvLayer(1, filters=50, kernel_size=(4, 4), activation=tf.nn.relu)\n",
    "        self.conv_1_5x5 = ConvLayer(1, filters=50, kernel_size=(5, 5), activation=tf.nn.relu)\n",
    "\n",
    "        self.concat_2 = tf.keras.layers.Concatenate(axis=3)\n",
    "\n",
    "    def call(self, input_tensor, training=False):\n",
    "        prep_input = tf.keras.layers.Rescaling(1./255, input_shape=input_tensor.shape)(input_tensor)\n",
    "        conv_4_3x3 = self.conv_layer_4_3x3(prep_input, training=training)\n",
    "        conv_4_4x4 = self.conv_layer_4_4x4(prep_input, training=training)\n",
    "        conv_4_5x5 = self.conv_layer_4_5x5(prep_input, training=training)\n",
    "\n",
    "        concat_1 = self.concat_1([conv_4_3x3, conv_4_4x4, conv_4_5x5])\n",
    "\n",
    "        conv_1_3x3 =  self.conv_1_3x3(concat_1)\n",
    "        conv_1_4x4 =  self.conv_1_4x4(concat_1)\n",
    "        conv_1_5x5 =  self.conv_1_5x5(concat_1)\n",
    "\n",
    "        return self.concat_2([conv_1_3x3, conv_1_4x4, conv_1_5x5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "71Egc2neBYT4"
   },
   "outputs": [],
   "source": [
    "class HideLayer(tf.keras.layers.Layer):\n",
    "    def __init__(self, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.prep_layer = PrepLayer()\n",
    "        self.concat_1 = tf.keras.layers.Concatenate(axis=3)\n",
    "\n",
    "        self.conv_layer_4_3x3 = ConvLayer(4, filters=50, kernel_size=(3, 3), activation=tf.nn.relu)\n",
    "        self.conv_layer_4_4x4 = ConvLayer(4, filters=50, kernel_size=(4, 4), activation=tf.nn.relu)\n",
    "        self.conv_layer_4_5x5 = ConvLayer(4, filters=50, kernel_size=(5, 5), activation=tf.nn.relu)\n",
    "\n",
    "        self.concat_2 = tf.keras.layers.Concatenate(axis=3)\n",
    "\n",
    "        self.conv_1_3x3 = ConvLayer(1, filters=50, kernel_size=(3, 3), activation=tf.nn.relu)\n",
    "        self.conv_1_4x4 = ConvLayer(1, filters=50, kernel_size=(4, 4), activation=tf.nn.relu)\n",
    "        self.conv_1_5x5 = ConvLayer(1, filters=50, kernel_size=(5, 5), activation=tf.nn.relu)\n",
    "\n",
    "        self.concat_3 = tf.keras.layers.Concatenate(axis=3)\n",
    "\n",
    "        self.conv_1_1x1 = ConvLayer(1, filters=3, kernel_size=(1, 1), activation=tf.nn.relu)\n",
    "\n",
    "    def call(self, input_tensor, training=False):\n",
    "        prep_input = input_tensor[0]\n",
    "        hide_input = tf.keras.layers.Rescaling(1./255, input_shape=input_tensor[1].shape)(input_tensor[1])\n",
    "        concat_1 = self.concat_1([prep_input, hide_input])\n",
    "\n",
    "        conv_4_3x3 = self.conv_layer_4_3x3(concat_1, training=training)\n",
    "        conv_4_4x4 = self.conv_layer_4_4x4(concat_1, training=training)\n",
    "        conv_4_5x5 = self.conv_layer_4_5x5(concat_1, training=training)\n",
    "\n",
    "        concat_2 = self.concat_2([conv_4_3x3, conv_4_4x4, conv_4_5x5])\n",
    "\n",
    "        conv_1_3x3 =  self.conv_1_3x3(concat_2)\n",
    "        conv_1_4x4 =  self.conv_1_4x4(concat_2)\n",
    "        conv_1_5x5 =  self.conv_1_5x5(concat_2)\n",
    "\n",
    "        concat_3 = self.concat_3([conv_1_3x3, conv_1_4x4, conv_1_5x5])\n",
    "\n",
    "        return self.conv_1_1x1(concat_3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "ReyvPpNoBYT5"
   },
   "outputs": [],
   "source": [
    "class RevealLayer(tf.keras.layers.Layer):\n",
    "    def __init__(self, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.conv_layer_4_3x3 = ConvLayer(4, filters=50, kernel_size=(3, 3), activation=tf.nn.relu)\n",
    "        self.conv_layer_4_4x4 = ConvLayer(4, filters=50, kernel_size=(4, 4), activation=tf.nn.relu)\n",
    "        self.conv_layer_4_5x5 = ConvLayer(4, filters=50, kernel_size=(5, 5), activation=tf.nn.relu)\n",
    "\n",
    "        self.concat_1 = tf.keras.layers.Concatenate(axis=3)\n",
    "\n",
    "        self.conv_1_3x3 = ConvLayer(1, filters=50, kernel_size=(3, 3), activation=tf.nn.relu)\n",
    "        self.conv_1_4x4 = ConvLayer(1, filters=50, kernel_size=(4, 4), activation=tf.nn.relu)\n",
    "        self.conv_1_5x5 = ConvLayer(1, filters=50, kernel_size=(5, 5), activation=tf.nn.relu)\n",
    "\n",
    "        self.concat_2 = tf.keras.layers.Concatenate(axis=3)\n",
    "\n",
    "        self.conv_1_1x1 = ConvLayer(1, filters=3, kernel_size=(1, 1), activation=tf.nn.relu)\n",
    "\n",
    "    def call(self, input_tensor, training=False):\n",
    "\n",
    "        conv_4_3x3 = self.conv_layer_4_3x3(input_tensor, training=training)\n",
    "        conv_4_4x4 = self.conv_layer_4_4x4(input_tensor, training=training)\n",
    "        conv_4_5x5 = self.conv_layer_4_5x5(input_tensor, training=training)\n",
    "\n",
    "        concat_1 = self.concat_1([conv_4_3x3, conv_4_4x4, conv_4_5x5])\n",
    "\n",
    "        conv_1_3x3 =  self.conv_1_3x3(concat_1)\n",
    "        conv_1_4x4 =  self.conv_1_4x4(concat_1)\n",
    "        conv_1_5x5 =  self.conv_1_5x5(concat_1)\n",
    "\n",
    "        concat_2 = self.concat_2([conv_1_3x3, conv_1_4x4, conv_1_5x5])\n",
    "\n",
    "        return self.conv_1_1x1(concat_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "zRBAuJEQBYT5"
   },
   "outputs": [],
   "source": [
    "class MyModel(tf.keras.models.Model):\n",
    "    def __init__(self, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.prep_layer = PrepLayer()\n",
    "        self.hide_layer = HideLayer()\n",
    "        self.reveal_layer = RevealLayer()\n",
    "\n",
    "    def call(self, input_tensor, training=False):\n",
    "        secret = input_tensor[0]\n",
    "        cover = input_tensor[1]\n",
    "        prep_output = self.prep_layer(secret)\n",
    "        hide_output = self.hide_layer([prep_output, cover])\n",
    "        reveal_output = self.reveal_layer(hide_output)\n",
    "\n",
    "        return  reveal_output, hide_output\n",
    "\n",
    "    def model(self, inputs):\n",
    "        return tf.keras.Model(inputs=inputs, outputs=self.call(inputs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Loss Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "lcWAPwEMBYT6"
   },
   "outputs": [],
   "source": [
    "class StenographyLoss(tf.keras.losses.Loss):\n",
    "    def __init__(self, beta=1.0, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.beta = beta\n",
    "\n",
    "    def call(self, y_true, y_pred):\n",
    "        beta = tf.constant(self.beta, name='beta')\n",
    "\n",
    "        secret_true = y_true[0]\n",
    "        secret_pred = y_pred[0]\n",
    "\n",
    "        cover_true = y_true[1]\n",
    "        cover_pred = y_pred[1]\n",
    "\n",
    "        secret_mse = tf.losses.MSE(secret_true, secret_pred)\n",
    "        cover_mse = tf.losses.MSE(cover_true, cover_pred)\n",
    "\n",
    "        return tf.reduce_mean(cover_mse + beta * secret_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "EhIAMmPHBYT6"
   },
   "outputs": [],
   "source": [
    "secret_input = tf.keras.layers.Input(shape=(64, 64, 3), name='secret_input')\n",
    "cover_input = tf.keras.layers.Input(shape=(64, 64, 3), name='cover_input')\n",
    "\n",
    "model = MyModel().model(inputs=[secret_input, cover_input])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hKDdzYGjBYT7",
    "outputId": "b4c19b57-e54f-4b49-b39b-481c058e3a35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " secret_input (InputLayer)      [(None, 64, 64, 3)]  0           []                               \n",
      "                                                                                                  \n",
      " prep_layer (PrepLayer)         (None, 64, 64, 150)  758250      ['secret_input[0][0]']           \n",
      "                                                                                                  \n",
      " cover_input (InputLayer)       [(None, 64, 64, 3)]  0           []                               \n",
      "                                                                                                  \n",
      " hide_layer (HideLayer)         (None, 64, 64, 3)    1133703     ['prep_layer[0][0]',             \n",
      "                                                                  'cover_input[0][0]']            \n",
      "                                                                                                  \n",
      " reveal_layer (RevealLayer)     (None, 64, 64, 3)    758703      ['hide_layer[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 2,650,656\n",
      "Trainable params: 2,650,656\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "2FM6oKlXBYT7",
    "outputId": "9ddb9350-f897-42b6-9ec1-389ff30fb4ed"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(\n",
    "    model,\n",
    "    show_shapes=True,\n",
    "    show_dtype=False,\n",
    "    show_layer_names=True,\n",
    "    rankdir='TB',\n",
    "    expand_nested=False,\n",
    "    dpi=96,\n",
    "    layer_range=None,\n",
    "    show_layer_activations=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "kGnVd9aWBYT8"
   },
   "outputs": [],
   "source": [
    "optmizer = tf.optimizers.Adam(LEARNING_RATE)\n",
    "stenography_loss = StenographyLoss(beta=1.0)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=optmizer,\n",
    "    loss=stenography_loss,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "QfwY0vl9BYT8"
   },
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    tf.keras.callbacks.EarlyStopping(patience=10),\n",
    "    tf.keras.callbacks.ModelCheckpoint(filepath='./checkpoints/model.{epoch:02d}-{val_loss:.2f}.h5'),\n",
    "    tf.keras.callbacks.TensorBoard(log_dir='./logs')\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Tiny ImageNet data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66,
     "referenced_widgets": [
      "1cece1ea8af947ffb1d094df6ba5bb3c",
      "a9864e2189894df4aa9a205d80708931",
      "3af3feb5b0e848c2a6ec65dddb10bd06",
      "9f8d7884768e4b8c821194c918b7a387",
      "08247855086944968fa02c8366d0d1b9",
      "8aca562f7bd14cbe8faef455f2eba703",
      "a77e545e4c8a477fa23fc24532373e7e",
      "cab5f845c0f7442e91ed81324fd6240b",
      "10833ce3156a497bbcd7a857cfbf7f96",
      "746208597d004f54ba8ebea90052d58a",
      "32763fc1ffbb42d28b363a94aa2e9f25"
     ]
    },
    "id": "MikMhWoTcxji",
    "outputId": "4cbaf6b8-61ce-4f8c-b4cc-700f1d7caf0f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.builder:Found cached dataset parquet (/root/.cache/huggingface/datasets/Maysee___parquet/Maysee--tiny-imagenet-35af7c46a941f08e/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1cece1ea8af947ffb1d094df6ba5bb3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "dataset = load_dataset(\"Maysee/tiny-imagenet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "rnusEbUigMQU"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_data, _ = train_test_split(dataset['train']['image'], train_size=.15) # Getting a subset of the Tiny ImageNet dataset.\n",
    "\n",
    "X_train, X_test = train_test_split(train_data, test_size=.1)\n",
    "X_train_secret, X_train_cover = train_test_split(X_train, test_size=.5)\n",
    "X_test_secret, X_test_cover = train_test_split(X_test, test_size=.5)\n",
    "X_val_secret, X_val_cover = train_test_split(dataset['valid']['image'], test_size=.5)\n",
    "\n",
    "del X_train\n",
    "del X_test\n",
    "del train_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "3Fo10wNjBYT-"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def show_image(arr):\n",
    "    plt.imshow(np.array(arr, np.int32))\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 268
    },
    "id": "tUIIjGBBf6Ix",
    "outputId": "27bff807-c41c-4a10-a54e-25970d123c8e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image(X_train_cover[0]).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "-QZfCM0zBYT-"
   },
   "outputs": [],
   "source": [
    "class DataGenerator(tf.keras.utils.Sequence):\n",
    "    def __init__(self, secret, cover, batch_size, shuffle=True):\n",
    "        self.secret = secret\n",
    "        self.cover = cover\n",
    "        self.batch_size = batch_size\n",
    "        self.shuffle = shuffle\n",
    "        self.datalen = len(secret)\n",
    "        self.indexes = np.arange(self.datalen)\n",
    "        if self.shuffle:\n",
    "            np.random.shuffle(self.indexes)\n",
    "\n",
    "    def __getitem__(self, index: int):\n",
    "        batch_indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n",
    "        secret_batch = np.array(self.secret)[batch_indexes]\n",
    "        cover_batch = np.array(self.cover)[batch_indexes]\n",
    "\n",
    "        return [secret_batch, cover_batch], [secret_batch, cover_batch]\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.datalen // self.batch_size\n",
    "\n",
    "    def on_epoch_end(self):\n",
    "        self.indexes = np.arange(self.datalen)\n",
    "        if self.shuffle:\n",
    "            np.random.shuffle(self.indexes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "l0v4Zlcf59Po"
   },
   "outputs": [],
   "source": [
    "for i in range(len(X_train_secret)):\n",
    "    X_train_secret[i] = tf.keras.utils.img_to_array(X_train_secret[i].convert('RGB'))\n",
    "    X_train_cover[i] = tf.keras.utils.img_to_array(X_train_cover[i].convert('RGB'))\n",
    "\n",
    "for i in range(len(X_test_secret)):\n",
    "    X_test_secret[i] = tf.keras.utils.img_to_array(X_test_secret[i].convert('RGB'))\n",
    "    X_test_cover[i] = tf.keras.utils.img_to_array(X_test_cover[i].convert('RGB'))\n",
    "\n",
    "for i in range(len(X_val_secret)):\n",
    "    X_val_secret[i] = tf.keras.utils.img_to_array(X_val_secret[i].convert('RGB'))\n",
    "    X_val_cover[i] = tf.keras.utils.img_to_array(X_val_cover[i].convert('RGB'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "b09_bBbuBYT_"
   },
   "outputs": [],
   "source": [
    "train_gen = DataGenerator(X_train_secret, X_train_cover, BATCH_SIZE)\n",
    "test_gen = DataGenerator(X_test_secret, X_test_cover, BATCH_SIZE, shuffle=False)\n",
    "val_gen = DataGenerator(X_val_secret, X_val_cover, BATCH_SIZE, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XGOh0h-sBYT_",
    "outputId": "7cf4a49a-f2ae-4c22-be25-8767ec2dcc5b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "210/210 [==============================] - 148s 603ms/step - loss: 22374.6875 - reveal_layer_loss: 11623.3174 - hide_layer_loss: 10751.3643 - val_loss: 12903.2881 - val_reveal_layer_loss: 6660.7192 - val_hide_layer_loss: 6242.5659\n",
      "Epoch 2/100\n",
      "210/210 [==============================] - 126s 599ms/step - loss: 10670.2510 - reveal_layer_loss: 6095.0859 - hide_layer_loss: 4575.1650 - val_loss: 5686.1655 - val_reveal_layer_loss: 3139.2361 - val_hide_layer_loss: 2546.9299\n",
      "Epoch 3/100\n",
      "210/210 [==============================] - 126s 598ms/step - loss: 5692.9023 - reveal_layer_loss: 3280.5112 - hide_layer_loss: 2412.3896 - val_loss: 4556.5547 - val_reveal_layer_loss: 2510.4983 - val_hide_layer_loss: 2046.0577\n",
      "Epoch 4/100\n",
      "210/210 [==============================] - 126s 598ms/step - loss: 5832.9673 - reveal_layer_loss: 3505.5029 - hide_layer_loss: 2327.4639 - val_loss: 4551.8472 - val_reveal_layer_loss: 2663.8020 - val_hide_layer_loss: 1888.0482\n",
      "Epoch 5/100\n",
      "210/210 [==============================] - 126s 601ms/step - loss: 4467.1558 - reveal_layer_loss: 2724.1870 - hide_layer_loss: 1742.9681 - val_loss: 3801.6470 - val_reveal_layer_loss: 2159.9541 - val_hide_layer_loss: 1641.6925\n",
      "Epoch 6/100\n",
      "210/210 [==============================] - 127s 604ms/step - loss: 3932.2161 - reveal_layer_loss: 2295.0493 - hide_layer_loss: 1637.1664 - val_loss: 3787.0244 - val_reveal_layer_loss: 2289.9624 - val_hide_layer_loss: 1497.0614\n",
      "Epoch 7/100\n",
      "210/210 [==============================] - 126s 602ms/step - loss: 5804.9956 - reveal_layer_loss: 3778.6680 - hide_layer_loss: 2026.3240 - val_loss: 3823.0220 - val_reveal_layer_loss: 2269.7271 - val_hide_layer_loss: 1553.2963\n",
      "Epoch 8/100\n",
      "210/210 [==============================] - 127s 602ms/step - loss: 3556.8164 - reveal_layer_loss: 2045.8213 - hide_layer_loss: 1510.9937 - val_loss: 3178.8135 - val_reveal_layer_loss: 1776.3616 - val_hide_layer_loss: 1402.4523\n",
      "Epoch 9/100\n",
      "210/210 [==============================] - 126s 601ms/step - loss: 3313.7639 - reveal_layer_loss: 1992.2705 - hide_layer_loss: 1321.4946 - val_loss: 5347.8804 - val_reveal_layer_loss: 4080.3357 - val_hide_layer_loss: 1267.5444\n",
      "Epoch 10/100\n",
      "210/210 [==============================] - 126s 600ms/step - loss: 2966.1069 - reveal_layer_loss: 1689.7866 - hide_layer_loss: 1276.3206 - val_loss: 2615.9192 - val_reveal_layer_loss: 1533.5593 - val_hide_layer_loss: 1082.3588\n",
      "Epoch 11/100\n",
      "210/210 [==============================] - 126s 600ms/step - loss: 3040.6931 - reveal_layer_loss: 1868.5745 - hide_layer_loss: 1172.1188 - val_loss: 2894.4414 - val_reveal_layer_loss: 1681.3730 - val_hide_layer_loss: 1213.0679\n",
      "Epoch 12/100\n",
      "210/210 [==============================] - 127s 602ms/step - loss: 3126.7849 - reveal_layer_loss: 2024.1235 - hide_layer_loss: 1102.6610 - val_loss: 2581.4170 - val_reveal_layer_loss: 1450.5380 - val_hide_layer_loss: 1130.8788\n",
      "Epoch 13/100\n",
      "210/210 [==============================] - 127s 604ms/step - loss: 2684.8206 - reveal_layer_loss: 1653.8965 - hide_layer_loss: 1030.9240 - val_loss: 3310.7236 - val_reveal_layer_loss: 1886.5516 - val_hide_layer_loss: 1424.1727\n",
      "Epoch 14/100\n",
      "210/210 [==============================] - 127s 605ms/step - loss: 2816.9080 - reveal_layer_loss: 1770.6257 - hide_layer_loss: 1046.2825 - val_loss: 2481.3333 - val_reveal_layer_loss: 1609.8862 - val_hide_layer_loss: 871.4469\n",
      "Epoch 15/100\n",
      "210/210 [==============================] - 127s 603ms/step - loss: 2249.4609 - reveal_layer_loss: 1474.5593 - hide_layer_loss: 774.9008 - val_loss: 1792.5560 - val_reveal_layer_loss: 1106.1473 - val_hide_layer_loss: 686.4086\n",
      "Epoch 16/100\n",
      "210/210 [==============================] - 126s 600ms/step - loss: 2328.3979 - reveal_layer_loss: 1480.5830 - hide_layer_loss: 847.8149 - val_loss: 1684.5328 - val_reveal_layer_loss: 1049.8876 - val_hide_layer_loss: 634.6451\n",
      "Epoch 17/100\n",
      "210/210 [==============================] - 126s 600ms/step - loss: 2178.2295 - reveal_layer_loss: 1448.1013 - hide_layer_loss: 730.1280 - val_loss: 3975.8337 - val_reveal_layer_loss: 3174.3127 - val_hide_layer_loss: 801.5204\n",
      "Epoch 18/100\n",
      "210/210 [==============================] - 126s 599ms/step - loss: 2380.3069 - reveal_layer_loss: 1694.8267 - hide_layer_loss: 685.4810 - val_loss: 3254.2148 - val_reveal_layer_loss: 2617.6379 - val_hide_layer_loss: 636.5765\n",
      "Epoch 19/100\n",
      "210/210 [==============================] - 126s 600ms/step - loss: 2639.2798 - reveal_layer_loss: 1814.1371 - hide_layer_loss: 825.1424 - val_loss: 1969.9978 - val_reveal_layer_loss: 1252.3102 - val_hide_layer_loss: 717.6882\n",
      "Epoch 20/100\n",
      "210/210 [==============================] - 126s 599ms/step - loss: 2139.3972 - reveal_layer_loss: 1450.7006 - hide_layer_loss: 688.6967 - val_loss: 2399.5645 - val_reveal_layer_loss: 1654.0035 - val_hide_layer_loss: 745.5607\n",
      "Epoch 21/100\n",
      "210/210 [==============================] - 126s 599ms/step - loss: 2100.3452 - reveal_layer_loss: 1541.4883 - hide_layer_loss: 558.8571 - val_loss: 1794.5427 - val_reveal_layer_loss: 1213.6975 - val_hide_layer_loss: 580.8451\n",
      "Epoch 22/100\n",
      "210/210 [==============================] - 138s 658ms/step - loss: 1761.9312 - reveal_layer_loss: 1221.9503 - hide_layer_loss: 539.9800 - val_loss: 1784.1572 - val_reveal_layer_loss: 1248.7429 - val_hide_layer_loss: 535.4141\n",
      "Epoch 23/100\n",
      "210/210 [==============================] - 138s 658ms/step - loss: 1457.6046 - reveal_layer_loss: 982.6314 - hide_layer_loss: 474.9728 - val_loss: 1365.7058 - val_reveal_layer_loss: 895.6603 - val_hide_layer_loss: 470.0458\n",
      "Epoch 24/100\n",
      "210/210 [==============================] - 126s 601ms/step - loss: 1438.7185 - reveal_layer_loss: 990.0437 - hide_layer_loss: 448.6751 - val_loss: 1463.2562 - val_reveal_layer_loss: 1033.0011 - val_hide_layer_loss: 430.2554\n",
      "Epoch 25/100\n",
      "210/210 [==============================] - 126s 601ms/step - loss: 2339.4170 - reveal_layer_loss: 1753.3318 - hide_layer_loss: 586.0859 - val_loss: 1410.4590 - val_reveal_layer_loss: 965.0211 - val_hide_layer_loss: 445.4384\n",
      "Epoch 26/100\n",
      "210/210 [==============================] - 126s 602ms/step - loss: 2034.7451 - reveal_layer_loss: 1462.9237 - hide_layer_loss: 571.8206 - val_loss: 1488.0127 - val_reveal_layer_loss: 1062.1199 - val_hide_layer_loss: 425.8930\n",
      "Epoch 27/100\n",
      "210/210 [==============================] - 127s 603ms/step - loss: 1674.4116 - reveal_layer_loss: 1165.2776 - hide_layer_loss: 509.1340 - val_loss: 1218.9297 - val_reveal_layer_loss: 820.3216 - val_hide_layer_loss: 398.6080\n",
      "Epoch 28/100\n",
      "210/210 [==============================] - 138s 656ms/step - loss: 1608.5956 - reveal_layer_loss: 1180.1527 - hide_layer_loss: 428.4419 - val_loss: 2016.0056 - val_reveal_layer_loss: 1487.5106 - val_hide_layer_loss: 528.4948\n",
      "Epoch 29/100\n",
      "210/210 [==============================] - 126s 601ms/step - loss: 1515.8082 - reveal_layer_loss: 1067.9016 - hide_layer_loss: 447.9067 - val_loss: 1199.5961 - val_reveal_layer_loss: 817.8455 - val_hide_layer_loss: 381.7502\n",
      "Epoch 30/100\n",
      "210/210 [==============================] - 126s 599ms/step - loss: 1670.4125 - reveal_layer_loss: 1173.8882 - hide_layer_loss: 496.5241 - val_loss: 1258.3917 - val_reveal_layer_loss: 899.2423 - val_hide_layer_loss: 359.1491\n",
      "Epoch 31/100\n",
      "210/210 [==============================] - 126s 598ms/step - loss: 1208.5391 - reveal_layer_loss: 851.7163 - hide_layer_loss: 356.8228 - val_loss: 1226.5507 - val_reveal_layer_loss: 859.7342 - val_hide_layer_loss: 366.8165\n",
      "Epoch 32/100\n",
      "210/210 [==============================] - 126s 598ms/step - loss: 1100.3881 - reveal_layer_loss: 754.4528 - hide_layer_loss: 345.9353 - val_loss: 1119.9326 - val_reveal_layer_loss: 787.8522 - val_hide_layer_loss: 332.0808\n",
      "Epoch 33/100\n",
      "210/210 [==============================] - 126s 597ms/step - loss: 1530.7655 - reveal_layer_loss: 1105.4213 - hide_layer_loss: 425.3435 - val_loss: 1120.6393 - val_reveal_layer_loss: 769.7700 - val_hide_layer_loss: 350.8694\n",
      "Epoch 34/100\n",
      "210/210 [==============================] - 126s 598ms/step - loss: 1144.1422 - reveal_layer_loss: 753.8124 - hide_layer_loss: 390.3300 - val_loss: 985.2385 - val_reveal_layer_loss: 645.5052 - val_hide_layer_loss: 339.7334\n",
      "Epoch 35/100\n",
      "210/210 [==============================] - 126s 600ms/step - loss: 2015.7703 - reveal_layer_loss: 1458.0375 - hide_layer_loss: 557.7327 - val_loss: 1599.3092 - val_reveal_layer_loss: 1100.0156 - val_hide_layer_loss: 499.2937\n",
      "Epoch 36/100\n",
      "210/210 [==============================] - 126s 600ms/step - loss: 1117.0402 - reveal_layer_loss: 772.2489 - hide_layer_loss: 344.7906 - val_loss: 1464.4373 - val_reveal_layer_loss: 1096.0487 - val_hide_layer_loss: 368.3890\n",
      "Epoch 37/100\n",
      "210/210 [==============================] - 126s 601ms/step - loss: 1136.9087 - reveal_layer_loss: 747.5968 - hide_layer_loss: 389.3120 - val_loss: 945.4858 - val_reveal_layer_loss: 613.6273 - val_hide_layer_loss: 331.8585\n",
      "Epoch 38/100\n",
      "210/210 [==============================] - 126s 599ms/step - loss: 1162.8853 - reveal_layer_loss: 799.7424 - hide_layer_loss: 363.1428 - val_loss: 924.6987 - val_reveal_layer_loss: 607.0819 - val_hide_layer_loss: 317.6171\n",
      "Epoch 39/100\n",
      "210/210 [==============================] - 126s 600ms/step - loss: 1180.2770 - reveal_layer_loss: 846.4025 - hide_layer_loss: 333.8746 - val_loss: 909.8252 - val_reveal_layer_loss: 586.9749 - val_hide_layer_loss: 322.8504\n",
      "Epoch 40/100\n",
      "210/210 [==============================] - 126s 598ms/step - loss: 949.6390 - reveal_layer_loss: 622.5533 - hide_layer_loss: 327.0856 - val_loss: 1262.3390 - val_reveal_layer_loss: 884.1454 - val_hide_layer_loss: 378.1929\n",
      "Epoch 41/100\n",
      "210/210 [==============================] - 126s 598ms/step - loss: 1061.3187 - reveal_layer_loss: 668.9381 - hide_layer_loss: 392.3809 - val_loss: 838.3143 - val_reveal_layer_loss: 507.4969 - val_hide_layer_loss: 330.8175\n",
      "Epoch 42/100\n",
      "210/210 [==============================] - 126s 600ms/step - loss: 1190.6133 - reveal_layer_loss: 833.0519 - hide_layer_loss: 357.5615 - val_loss: 818.6710 - val_reveal_layer_loss: 497.0304 - val_hide_layer_loss: 321.6404\n",
      "Epoch 43/100\n",
      "210/210 [==============================] - 126s 602ms/step - loss: 905.7214 - reveal_layer_loss: 552.7769 - hide_layer_loss: 352.9446 - val_loss: 807.9028 - val_reveal_layer_loss: 500.1981 - val_hide_layer_loss: 307.7048\n",
      "Epoch 44/100\n",
      "210/210 [==============================] - 138s 655ms/step - loss: 968.3220 - reveal_layer_loss: 664.9191 - hide_layer_loss: 303.4026 - val_loss: 1043.3582 - val_reveal_layer_loss: 712.7998 - val_hide_layer_loss: 330.5590\n",
      "Epoch 45/100\n",
      "210/210 [==============================] - 126s 597ms/step - loss: 1326.1554 - reveal_layer_loss: 945.9086 - hide_layer_loss: 380.2464 - val_loss: 1527.2535 - val_reveal_layer_loss: 1050.8906 - val_hide_layer_loss: 476.3636\n",
      "Epoch 46/100\n",
      "210/210 [==============================] - 127s 603ms/step - loss: 1087.6143 - reveal_layer_loss: 727.7413 - hide_layer_loss: 359.8731 - val_loss: 998.1278 - val_reveal_layer_loss: 631.2733 - val_hide_layer_loss: 366.8548\n",
      "Epoch 47/100\n",
      "210/210 [==============================] - 126s 602ms/step - loss: 976.9464 - reveal_layer_loss: 651.4415 - hide_layer_loss: 325.5049 - val_loss: 755.8207 - val_reveal_layer_loss: 451.6573 - val_hide_layer_loss: 304.1638\n",
      "Epoch 48/100\n",
      "210/210 [==============================] - 138s 656ms/step - loss: 866.1079 - reveal_layer_loss: 571.0367 - hide_layer_loss: 295.0713 - val_loss: 749.2005 - val_reveal_layer_loss: 443.4874 - val_hide_layer_loss: 305.7130\n",
      "Epoch 49/100\n",
      "210/210 [==============================] - 126s 601ms/step - loss: 951.9025 - reveal_layer_loss: 622.5393 - hide_layer_loss: 329.3633 - val_loss: 735.3559 - val_reveal_layer_loss: 422.8902 - val_hide_layer_loss: 312.4658\n",
      "Epoch 50/100\n",
      "210/210 [==============================] - 138s 656ms/step - loss: 994.3183 - reveal_layer_loss: 687.9046 - hide_layer_loss: 306.4137 - val_loss: 1125.8936 - val_reveal_layer_loss: 776.1237 - val_hide_layer_loss: 349.7698\n",
      "Epoch 51/100\n",
      "210/210 [==============================] - 126s 597ms/step - loss: 1130.5918 - reveal_layer_loss: 762.8084 - hide_layer_loss: 367.7837 - val_loss: 771.8934 - val_reveal_layer_loss: 471.0867 - val_hide_layer_loss: 300.8069\n",
      "Epoch 52/100\n",
      "210/210 [==============================] - 125s 597ms/step - loss: 792.7928 - reveal_layer_loss: 519.9908 - hide_layer_loss: 272.8018 - val_loss: 1297.2618 - val_reveal_layer_loss: 977.3478 - val_hide_layer_loss: 319.9138\n",
      "Epoch 53/100\n",
      "210/210 [==============================] - 125s 595ms/step - loss: 972.8359 - reveal_layer_loss: 632.1773 - hide_layer_loss: 340.6586 - val_loss: 775.2087 - val_reveal_layer_loss: 456.8925 - val_hide_layer_loss: 318.3164\n",
      "Epoch 54/100\n",
      "210/210 [==============================] - 126s 600ms/step - loss: 802.5449 - reveal_layer_loss: 516.2147 - hide_layer_loss: 286.3304 - val_loss: 831.4017 - val_reveal_layer_loss: 530.6929 - val_hide_layer_loss: 300.7088\n",
      "Epoch 55/100\n",
      "210/210 [==============================] - 126s 601ms/step - loss: 1196.7098 - reveal_layer_loss: 814.4639 - hide_layer_loss: 382.2465 - val_loss: 807.8776 - val_reveal_layer_loss: 485.2192 - val_hide_layer_loss: 322.6585\n",
      "Epoch 56/100\n",
      "210/210 [==============================] - 138s 655ms/step - loss: 736.3351 - reveal_layer_loss: 436.8442 - hide_layer_loss: 299.4910 - val_loss: 650.4580 - val_reveal_layer_loss: 371.1911 - val_hide_layer_loss: 279.2668\n",
      "Epoch 57/100\n",
      "210/210 [==============================] - 126s 600ms/step - loss: 888.0355 - reveal_layer_loss: 550.8927 - hide_layer_loss: 337.1424 - val_loss: 889.9518 - val_reveal_layer_loss: 571.2581 - val_hide_layer_loss: 318.6938\n",
      "Epoch 58/100\n",
      "210/210 [==============================] - 126s 598ms/step - loss: 860.8588 - reveal_layer_loss: 587.5588 - hide_layer_loss: 273.2998 - val_loss: 1297.6543 - val_reveal_layer_loss: 895.1751 - val_hide_layer_loss: 402.4792\n",
      "Epoch 59/100\n",
      "210/210 [==============================] - 126s 601ms/step - loss: 1010.7046 - reveal_layer_loss: 660.9481 - hide_layer_loss: 349.7569 - val_loss: 799.6699 - val_reveal_layer_loss: 504.2818 - val_hide_layer_loss: 295.3880\n",
      "Epoch 60/100\n",
      "210/210 [==============================] - 138s 655ms/step - loss: 763.4725 - reveal_layer_loss: 505.4258 - hide_layer_loss: 258.0467 - val_loss: 1034.5790 - val_reveal_layer_loss: 748.6717 - val_hide_layer_loss: 285.9076\n",
      "Epoch 61/100\n",
      "210/210 [==============================] - 125s 597ms/step - loss: 924.8834 - reveal_layer_loss: 612.6750 - hide_layer_loss: 312.2082 - val_loss: 756.5637 - val_reveal_layer_loss: 455.9811 - val_hide_layer_loss: 300.5827\n",
      "Epoch 62/100\n",
      "210/210 [==============================] - 125s 595ms/step - loss: 988.1774 - reveal_layer_loss: 698.5276 - hide_layer_loss: 289.6495 - val_loss: 882.5992 - val_reveal_layer_loss: 551.9495 - val_hide_layer_loss: 330.6497\n",
      "Epoch 63/100\n",
      "210/210 [==============================] - 125s 597ms/step - loss: 812.4040 - reveal_layer_loss: 524.4537 - hide_layer_loss: 287.9501 - val_loss: 637.0800 - val_reveal_layer_loss: 359.1969 - val_hide_layer_loss: 277.8832\n",
      "Epoch 64/100\n",
      "210/210 [==============================] - 126s 601ms/step - loss: 705.1205 - reveal_layer_loss: 455.0811 - hide_layer_loss: 250.0394 - val_loss: 676.5771 - val_reveal_layer_loss: 408.4032 - val_hide_layer_loss: 268.1735\n",
      "Epoch 65/100\n",
      "210/210 [==============================] - 126s 601ms/step - loss: 915.8925 - reveal_layer_loss: 619.7272 - hide_layer_loss: 296.1651 - val_loss: 858.1519 - val_reveal_layer_loss: 450.3844 - val_hide_layer_loss: 407.7676\n",
      "Epoch 66/100\n",
      "210/210 [==============================] - 138s 655ms/step - loss: 835.4161 - reveal_layer_loss: 489.4008 - hide_layer_loss: 346.0153 - val_loss: 660.7982 - val_reveal_layer_loss: 374.1298 - val_hide_layer_loss: 286.6682\n",
      "210/210 [==============================] - 126s 600ms/step - loss: 813.1669 - reveal_layer_loss: 521.2858 - hide_layer_loss: 291.8811 - val_loss: 645.3067 - val_reveal_layer_loss: 366.7581 - val_hide_layer_loss: 278.5486\n",
      "Epoch 68/100\n",
      "210/210 [==============================] - 126s 598ms/step - loss: 753.6780 - reveal_layer_loss: 510.0239 - hide_layer_loss: 243.6541 - val_loss: 776.9995 - val_reveal_layer_loss: 477.0534 - val_hide_layer_loss: 299.9460\n",
      "Epoch 69/100\n",
      "210/210 [==============================] - 126s 599ms/step - loss: 697.1790 - reveal_layer_loss: 426.1296 - hide_layer_loss: 271.0491 - val_loss: 674.3327 - val_reveal_layer_loss: 387.3721 - val_hide_layer_loss: 286.9606\n",
      "Epoch 70/100\n",
      "210/210 [==============================] - 124s 591ms/step - loss: 694.3226 - reveal_layer_loss: 430.3265 - hide_layer_loss: 263.9960 - val_loss: 663.8238 - val_reveal_layer_loss: 378.2699 - val_hide_layer_loss: 285.5541\n",
      "Epoch 71/100\n",
      "210/210 [==============================] - 124s 592ms/step - loss: 693.6407 - reveal_layer_loss: 426.6837 - hide_layer_loss: 266.9572 - val_loss: 665.2563 - val_reveal_layer_loss: 395.1892 - val_hide_layer_loss: 270.0673\n",
      "Epoch 72/100\n",
      "210/210 [==============================] - 125s 595ms/step - loss: 1344.4744 - reveal_layer_loss: 965.2856 - hide_layer_loss: 379.1892 - val_loss: 779.6772 - val_reveal_layer_loss: 465.2732 - val_hide_layer_loss: 314.4044\n",
      "Epoch 73/100\n",
      "210/210 [==============================] - 125s 596ms/step - loss: 805.4976 - reveal_layer_loss: 525.2676 - hide_layer_loss: 280.2300 - val_loss: 759.6793 - val_reveal_layer_loss: 441.3364 - val_hide_layer_loss: 318.3430\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_gen, \n",
    "    epochs=EPOCHS, \n",
    "    validation_data=val_gen, \n",
    "    verbose=1, \n",
    "    shuffle=True,\n",
    "    callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eKNesE7qBYUA",
    "outputId": "14ac6ee3-c61c-4ac7-8dc7-2666412b0430"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23/23 [==============================] - 3s 127ms/step\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(test_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "id": "8ZzIL8vNdm7g",
    "outputId": "3300938e-fc19-4e29-ae14-45b91f8a3e25"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.9/dist-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image(X_test_secret[0])  # Secret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "id": "dLgZmtDiQyuB",
    "outputId": "4165dde7-99ca-4ddb-a51d-3137c7b1b523"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.9/dist-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image(pred[0][0])  # Revealed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "id": "BglWOmkAU54F",
    "outputId": "6b591f6d-7ffe-4fda-9514-91b71ab2ba0c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.9/dist-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD7CAYAAACscuKmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAABCs0lEQVR4nO29aZBk2XUe9p3cl1qyqrqqep/qbVYsM8QIAAUIqwYCKZkwbASCS1iQhND4B62gQlIIgB1hU/QGRACC4AgGwxMmJQSDFkhChAaGKYrwCCBlkBhMD2brWbvR0z29VlVXVdaWe+b1j8p69zunKl8npruzxsr7RXT0fXXvu++++97Nd84953xHnHMICAj4Tx+JvR5AQEDAYBAWe0DAkCAs9oCAIUFY7AEBQ4Kw2AMChgRhsQcEDAluabGLyMdF5FUROScin79dgwoICLj9kDdrZxeRJIDXADwC4DKApwD8knPupds3vICAgNuF1C2c+24A55xz5wFARL4B4BMAei72Qibjxgs5AMCOnxi3a3ELIr6OyqsbG7oLqusIdB3MH3pA6OL2DHXMg3S6ZSLpBaZcPq/qShMT1IW+01TKP44E33NHtxOqSyW1cNZqtnbtL5lMqnZt1+lZ16G6RrUWla9fn9fXajWj8qFDh1Xd9evX/LU6vr9ms6napdLpqBz34Yn7JNl57Oe8Hc9W4mq5Q90jH/FzsT3waaIvpp5THIR67dCcAoAktuoa9TpareauN3Ari/0QgEt0fBnAe+JOGC/k8Pc++F4AQKttJo0Om+YJdZKZqNygifmTH/xAtaunE9ROv8BNmuC20AIxCylNc5g2izhFdSmq69T0giuWxqLyPQ/cr+o+8an/Iiq3zKs4MTUZlfNZf89NWnAAkEv5BTI5NqrqlucXo/K+ffui8mhpXLVbq270rKs1G1H54guvROWvfOUrqt3169ej8v/66/+LqvvSl74UlTeqlah8+eoV1W724IGo3DA/BDz9bZorO2/8w9WBfa8ctfPlTMI8W/rBSyT08+Q+7CJz9FXhH82U6D74vHQ6o+r4OemT9L0kEv7dr1Qqqi6T2erztVee370vDGCDTkQeFZHTInK60mje/ISAgIA7glv5sl8BcISOD3f/puCcewzAYwBwoDTmbqcvfsJ0lehTVL/dSGay+ph+4WdmZlRdq92OypsN/cXGCkkLoyNROUuiLgDU63XfX0urCXMnjkflcrkclSu1qmp3YP9BX9fQdYVCISpPzk5H5V/5O39btfvyl78clf+33/pNVXd53ovx/FU7dPSIare2thaVU+Y+eyFpX6HOrs12VKUQJ2a7Xcs3q+OeVLs41SKmfwarUwAgMWvHqga74Va+7E8BOCUix0QkA+AXAXz7FvoLCAi4g3jTX3bnXEtE/hsA/x5AEsDvOOdevG0jCwgIuK24FTEezrk/BvDHt2ksAQEBdxC3tNjfDLb1N+diDVsKvXQaq6fwLqr0Vq1uO+zu8MLySlSumU3Jl19+OSq7lNaiRka8nn5wv9+l3md2y9O00/vCiy+ruqlxbwk4deqU79vs2l+54rdX1irahMn7DGs1X3ffOx/Q4yj6vYqfvHFe1xX8jnOadPHy2opqx882JdqCwo8sEaM3C82/xOzGM4wxKHbHPQ699Pkd+rbsrtvH9bGjnZoQ/UJLZILt/aIHd9mAgCFBWOwBAUOCgYrxzgGdSDQxYgiL5HHmuU7vuqTqM8ZMEeet9yZQHNcicn15OSqXJid146Sf8qmJkqrKZr1YzKKvlSqbznvJzc7OqrocOWzMLy5E5ZXVsh7zqB9zaUyrCUtLS1H5T//8e1F5grz/AGDmkFc1ri8tqrpCypsEC/mcH/vqqmrHfTbIpGjBT9aqaMo7baf7JbUjBxvzmWPRvR8z1s36t8NQKqbEvPsxf+c+rOPPtrk3bujhyx4QMCQIiz0gYEgQFntAwJBg4Ka3bVh9hCPWrN7R0xwRo79bV1qly0nvdm8GtZY2r2WL3t306Iljqo5dUW0ARIMCRlh/d0ZpL42XonI+l1N1tYp3fXUt75rbarVUOw6kWFnR5rA/+7M/i8p3P3Sfv26ppNodO+bv7Xd/93dV3ZkzZ6LylWtXozKbFAHg6lVfVxodU3U99XSry76JPZh63J5OzHu1Q9/mUapXUz8zFdFo9O1e+rw1valAGxMp53X2YHoLCBh6hMUeEDAkGKwYL17M2CEOKe83Y2sisYojf3ZEAXGdkWZ6iXpvVopncoyl8rKqY0+4WqPes86Kizzk6QlvsrNzNTriVYFz586purtPeq+5H1C8/9NPPanaFYvFqHz+vPZ+m9nnI92+9YffjMof+MAHVLtlMjE+8pGPqrozz/m4ao5GzBsyD3vMULNDU2DNa/ws4iIf+bSkrYvz0FMqZm8xPpZ8I86cTAQkah2YZpKgUduY++644t7n8GUPCBgShMUeEDAkGKgYL/Bi0A7qnxhRicGeTjt4uEgsTthAgTsYCJMq6B3xVM7vpM/Pa9620ZwXwTu0Mw8AafrtzRL11Oqq3i3fXPNeaLPTmhzjk5/8ZFQu5CgYJWl2b0lI/K///qOq7jd+4zei8sm3nYjK44ZO6dLijag8eeKkqjs07VUB3vlfJFKLrXGxfN47AIXJIHbEUBHaMTvpjB3WoD5JVWJ342P6axNpiaR632esytBnME0vhC97QMCQICz2gIAhQVjsAQFDgsFGvQFwXV3aWZ2ayQCt9xvrYaLclHRDPk70VuzerPreSytKpPVvZgdeP5s3Ourxw55fPZ3QBqA68eBffO21qPz000+rdt/5Y08OtLpR1mOhvZA2mXTWl7XePz7io97++PHHVd19xzxp5TtPeA+6I2NTql3y+D1R+Te/+GVVlyfCzGbLz1whpfX+ett79sXxSNo8AD2R7E8XT8SQp3TMOYnbYHprMU//mySO5CHb4Ud02jF9hy97QMCQICz2gIAhwcADYbZFHSvyNCkLSTqtedg5G0ib5JdMSvOMC/G319s6OEWldWKuMCs8suwYY/pQqXhIbAe0c9Nzz/xY1b3xyqtR+calq6qOBdxGzXve2TGqwBvzc12a9sE15SXv4Wa99fI0yHUiqwCAw7M+WCW/6cXsH/27/6DacZDPe+59u6pb3lyPyitpX54+pANhnvzx6ahcNc8sSVlxkhn/rFsdPd/Mxb+T8MGrSvzOSV2bvzgtVVw6Jn4uAJCid3U7Kwuguf1tnzYFFgc9Ndu7p+8CgEbdn5czAVCRGToEwgQEBITFHhAwJAiLPSBgSDBwnb0fMr+dvN27mzdspsx6w+v9YkwwHPXWpqrkDuLLmw5vaxzUzursFcqQWqtqk1cj4fW61fkFVTeR2z2/WyqjzVWTE57kYXVTc77fdcjncJsa85FtR2b2q3bVstejR8weySi51j73H3/o+5vWZBvJutcvG8trqi7taE4qXn+9+OpZ1a5U9CbAvJnHDcqFVyVSjo41r1G5FcPXnkz7+xo1rr/8ftgU1mwmtu8mm+XiXLnjvqr9Ek6q4x3u4Df/bt+0hYj8jogsiMgZ+tukiHxXRM52/5+I6yMgIGDv0Y8Y/68AfNz87fMAnnDOnQLwRPc4ICDgLYybivHOuT8XkTnz508A+FC3/HUA3wfwuZteTRCJH67TO2rHikBJ8SJtgmxjuawWPzdqm75dwohpJAIllNmsf/qKXrzg1oyYoN/QfFqLhPtIbJ0wbmGz456wgkXJpPHQk6yfj3bTkGNkycTjJXVMjWpu+1d+ciEqd0S/BssUpfbBhx6Oyqsb66rduRd8Hs9aR3PcTR89FJX3kUfej158TrU7fJf3KLxyQ6s1NZqeLHkDclQhAEjKz1W1rtNgswmsTWoHjNk2/v3z/dtozX6ho9lMJYnkqn8rxtMcWFXDm956j+HNbtDNOue2/UCvA5iNaxwQELD3uOXdeLf1k9jz8ygij4rIaRE5Xa03ezULCAi4w3izu/HzInLAOXdNRA4AWOjV0Dn3GIDHAGD/xFhfMrMVi1MkpiVJwhof12mLlmte/LQxMvyrppJhWrmHgw12ECHsbhVo1CuqVcIRCUVHi1sZ+l1MGA+psYw/1vE+ehzrG568ItFqqLoN4oVrVbxa067qMc5QVteZop5HRz/KZylLbG60qNqNZLwX18F9Os1VjW5gX8nv386UdLt75jw5RsaoZTfWylF5YcV7+a1vbqp2bRK7U0a8TSe9Oscicqeld/6bZMmxIjKL4HHU4+q9ihH343bZ1bV2pHjiA6PadVWBXmQawJv/sn8bwGe65c8AeDymbUBAwFsA/Zje/jWAvwRwj4hcFpHPAvgigEdE5CyAv949DggIeAujn934X+pR9dEefw8ICHgLYrAedI7NGkbniHFdU2lvyHVtZkp7dC1QWuL1tjYF9XutOAICjr7jKLJcTptxEuxZVtXmqk3S55Nr2kxUIy8o9qCDMd/NHfSRY0ePHlF16zXvUZel8V69eEG1KyW8ftypaH1eKCrrwAF/Lcno12WS91LyWt9++kXPG99J+fuyUWOvnPHmu3RRc8hPkpmSSS9WiHATAKpV711no/u4bn29HJXz+7UBqVbTz4IRRwLZ623pNy0zYNI/JXqb19qkj+/w8tt+b0PUW0BAQFjsAQFDgj0jr7DSBosl7bb1SNtdjJqc1Gac/DUvBq4bby/La9cLWtyK4eZmb7q2Nn/xpBYzWrydKPpgl2RHT/8EpUJK03y0TODH5dd9uqbZ44dV3eriYlQeJ6+5efo7ABw+OufHaIIoCll/XiflzWuLy5rkokFec4mMVmWqJu3VNh649z51/Op5n76quqqfWWHEm/pKWT83Y5N6TtmM5oxJbWPd97mW9PdSMxljOcutzXjL5jyrHHZIbejE8N4rzndr7e1hbtuR7TUuWKfHulLn964KCAj4TwlhsQcEDAnCYg8IGBLsmc5uc7ElmRjQ6ExKV257vWhkROtdWSJ5iOOgYMKKtmnYb84vblcx+wNJctHMJDQxYIF022RG65dZzsdG5A9i9jBmJsn9dEJTCayRi+k+cidewiXVrkHmto2mNr2lKB/dfMOTUqSMXj4+UYrKTWh9dazk9f7zpJefuPdu1W5x/npUftdfeVjVLd/wewRM9JE1eetyRESRMXUzUz4XXvGoJ8j8warOwce58DgvG9Af4YptF+sS2+d5NrcCu2Fbfd67VAfTW0DA0CMs9oCAIcHAxfht7DArkPknLt0tl3MmauzNEgv0C8U7Tn8/dOiQapehdEe5Ta2StNnEs6H545ppL/LnyGSXzmvxebXmvcLWVrU32ea679NNeNNkLm3IGpo8j1rVGCezVJvGsUQRdQDw+hsXo3LCeNfNnfSEFTdW/HkPPPCAalcmb7hOQ4dAV2h+UqTKHDTeb8UsmQevXVd1i4uem3+duNZdSc8Hv1fW9Kai5QyxhSM7GhOkWNMYo9+ot53tqLzDLBdSNgcEBHQRFntAwJBgsFlcBehkt35faoZ0IdHxHldJIxI6UF3SiytL82+odgen/e7zlYUrug/axc8W/W6zZHTwxUbVB0TYXdkU7bI78paauK6DKPYf8WL95F3Tqu7apctR+cg77ld1yTVKgVX1105VtTg3kfP3+epZPQfVCS+q3pjw3nrVgzOqXaVBtMqaCwIr529E5VdyXmV418ycanf3bCkql41YnDjog5QuXvLi/gv/t04hlSW3sPUx/SwaRT/Gkyl/z5VXL6p27rC/t8qYGYfzfR4RbyG4UtDvWHnz9aicH9MkHZ2cf+7zhievMOL7L+Z9n62qVkkcceFNFDRZSAGkRhEpRauhRfN03j/PtU1tQcl3vQ0T0lt9CF/2gIAhQVjsAQFDgrDYAwKGBIM1vYk3J4j1DqIIs6T5DWLvOva8a0HrJyOp3voKe82x+aRVr6p2bdbtTdol1tnrRHZg0+cWi8WedaMUiVbMaR01Saq/UNRYs6FNQQs3fEqplui6yoZXwJt1vwdw/eo11W5y3KeDSmdHVN2xaU+IkZ32OvDGeZ1iemnNX2uzoSPRXjn7gu+Dtj72jWrCkRaRhK4uadNelggxNomEYmRce05yxF3K6b2gkTGvH7sN/2zTxtNujCLsEoZTvkZkHmnz3nLacI64a5u0zGnFPa+q1H5SHDe8jojTfWy/03EGuPBlDwgYEoTFHhAwJBioGC/OB+Bbhx9FLmGJLVRwgBdtMgk9/DZxtI+PaPOJI++sKonxbSP4sJdSynjosTrRL4/duvFwW1/1gSXllu6jUCUxk36HOfsoABwoeV64XFp7dGUTXow9ftdcVG6aLKtHRr25qnZee51duu7F9Wd+4uvG2oYL77AX9/cd0V5tVyv+vsdKXk1IJbS4f9chr04k63qM07N+jJuXvclrbET3sVjx51Ub2iSVy3oz67WlclQ++o73qHY/OftaVJ6/roNksuO+j8kxbTbj92eTnq1VEwp530fBqHb8nqWIa69tPehIdrfvZj8IX/aAgCFBWOwBAUOCsNgDAoYEg3WXBbCtLjsTPcTJ2RJOu6kyh6CQe6WN/GE9ev/+/aquMu9NT5vkEptKa/Mam/2su6waI12aTW2ANq9ZPvI8RZElDdFjivjVE2R2SRgu9DJFgy22tK/r5WrZD5eIKtcXNFnkTNvfd9FsPzChx6kjc74/k5jzRsWTdjTOaQKPuQPeZZjn443XL6h2I2lvirvxut47SLT8O1KjaL6kIS1Zp5x2+0z+v3Ei+ETWP6c/P31atWsRmYdNb92hh90wz7PtKIU4vUuHZvT7t7Hi56eQ0Tp7jvn3qY+Gef/qFI1nIzxdHwQb/aR/OiIi3xORl0TkRRH5te7fJ0XkuyJytvv/xM36CggI2Dv0I8a3APxj59z9AN4L4FdF5H4AnwfwhHPuFIAnuscBAQFvUfST6+0agGvd8rqIvAzgEIBPAPhQt9nXAXwfwOfi+hIQB7wVO5hM24itio+bRPyOaedItJk03GyvX/diPItihZw244BE6+qmEdmo/xwRTViyAza7VAyPeZ7MLgmjJTQobfBaw1fWK9rLb8N5cbqV0fM4RYQV1TUv+mZMNNTKivfCK+X1XBXS3ky0vlGOyjXDiz467a+1adIoz46UovINSsu1QCmYAKBO1HjTI1oEl5q/zxLx7jWMCrhBas1ETqtU15Z99CMTbBST2kvurllvOmyaT+C1Jc+5nzTXHi95laJcLkflc6+8qtrtG5+KymmjvnFehBx5bbYb2huwQ155WZPeGl1PvjgCl59qg05E5gA8BOBJALPdHwIAuA5gttd5AQEBe4++F7uIjAD4NwD+oXNOeT+4LS+SXd1yReRRETktIqcrZoMnICBgcOhrsYtIGlsL/fecc3/U/fO8iBzo1h8AsLDbuc65x5xzDzvnHi5k07s1CQgIGABuqrPLlj3rtwG87Jz751T1bQCfAfDF7v+P3/xy4l1frd6ioomMCyupSR2qc7YdHVt3Rd4H6JCObXODgcxVDrouwS6sNHyb14zNbYvG9TJF91LvaH17RPyP4RjtCaQL2lQzO+bNVY2c1sVTk95stEHElBMFHdnWWfKmIGnrZ7FIOnaGdMCFijavTcx581q+qfctXj/ziu+v4fX542/XvPFslrtxVjPQbC77fYWRyZIfe0I/9zEyt42TeyygI8qaI14f3mcsv8+/eCYq152WQDkddTal56pD+yklimI8Mq1Nb+VFfy+WKJV1+BxF8HWM/t2gfa7sqHme3T2vRIwJrh87+/sA/FcAXhCRZ7t/+2+xtcj/QEQ+C+AigE/30VdAQMAeoZ/d+P8XvdNMfPT2DicgIOBOYcC88S4KsrcplIVEmYT9aekh4e9I+0wNkzYd0YgX71arXqzsGJIBIdNKPmXJKyitNBFmZrPa3DNCEU7WzFIkU1/W6ToWQSeYu914Ul1f9oSQm1UtPq8teBNjs+3rOrNarJwmwoqOCUHsZPx9jqa8aJpw2gR4hcgX757Q/c9S/6MNbxpbNkSjrzzz46j8yP3vUnULRLghJPpWjc2SzU0LlzTRaIIIIeebXg3JZ/Qz+5m3efLPT/3yL6q6h97t01L94KknVd0ffuuPovJzz3nCjta6jr4rFPzztGZQJhnpkDok5hvLKmfTvLeblS3VkclXLIJvfEDAkCAs9oCAIcFgA2Gci8SPpJjUTSR2S8d61+3ObJGwln1qlzY7niUiHVgnDu8VCrAANHfYWFHvgidJXFwlUoqKSQXLYuXYiA6qmCl5r7NkQ4ujjkS4lXXvyrBs0i4tlP0xc9QDwEzR9z8y5q+dcnqMxbwXYzdXNMFGOu/Vl2rZi6PTBzT3/A9fft6fU9Yi/uFCKSq3knRfa1q85YCllcUbqm6VgncWN33ZlfSOe5GeWX1VuYDgvrtPROVGxz/rjhlHdc2L+M89+SNVx8QWSxu6/3c98LaofPyQJ/N46aVXVLuF694Lr1PXnpkbZb9Tz/x3TWNRWlv3Y3RVPd8LXctFw3jdMcKXPSBgSBAWe0DAkCAs9oCAIcGATW8C6UYbOROx1nK9dXbpcWBJH1lXbta0V9v993rPrdPPPhOV/7NPflK1+8Qv+OPr1zWZAntjtSnl8f/+1a+pdteuesLGxoaOBqulKBWz1aPJA6tAHlL3vF2nOX76+Wej8vHjx1Xd2bNno3Kn4udg2eiyKzR342OaDGKVOdrLfvyJSW2KPHz4cFSeKUypunzD958ic+aqIeAsHfD3ubyo9yYKBa+bH5z1Y5xva317fNLvP+zL6Qi+ixe9V17+Xq9Tp4zjZHXT68OVNbOHkfN6dNHsBeXT/nmuEKlpwbTrkJfl8vyiqpslXb9A/S1sao/FfVN+jm+s67oTJ48BAK6e+zF6IXzZAwKGBGGxBwQMCQYsxhNHhXWTa1OgSoxnXBwSpArkDSnF2nI5Kk+Nl6LyB/7qX1XtvvP4v43Kh/YfUHXtpu//h3/xF1H5wx/+sGp37YJPoyyG4662TmK9STNUIdVm/qpP7bxmUlRliQCDiRsAYIL42e475VUXa5K5QmQenD4JABrk3ljveJG2YNQm5tBb29TjaFa8ua1FqYwPG262++6+Jyqvtl5XdWwuPL9OgSSjhnedAnlskIyj94w5+6eNF1uaIpsySf0NzKiUYyaNMh1n6bysSd10YJ83W+bz2hybIyKKtVV/n4eOHFXtaqSmaiUVmOyK+HF88uHLHhAwJAiLPSBgSBAWe0DAkGCw7rLiOcltcmXW03dEvalIN6GyIa8gHa9jiR6zXnduEanDj/9SRzHNHfB6+g//QtfVyCT1wL33ReVrxkR3/vz5qHxiVruzdojEsjiqCRYnZ6ejcpUYO+odHdm2THrdpTfeUHXcf4VSO9v0v4sbXhefOnJQ1c2XvWvqyXFyuU0YPZfm25JuCrESpajcXNdmrdfOvOQPVrSZknnYOYfb+LgmbmBTbdUQTzTpHWkS0UTT7FNkmLPf6cgx6fh3qWWi9qqblGeATG+WJDRH+foSRu9vUNuVijc/Hr9Pm1wTFP1YMWQh26nBRXp/v8OXPSBgSBAWe0DAkGDgprdtGb1tPOg4lZNN58xCFQuSOzguSMRv17W4NTFRisrHDnuTxs997BHV7qtf/mpU/huPfEzVXXzdm4Zef9Xzgo8f0GLw3SdPReW3zZ3UfbzkI6iaZozsXVZr+rqfnD+r2j344IP+YFx7jE0S53uazJk2QDBb8uL5yEEdzXZhxZNSrLe8kWe0qqO1siQ+J9NaxC+U/LiYQ6+6pEXktTVvDisZXjVWDY5M+Tleq2oPuvWan6uNslYT6i0vWo/kvejeaGrjVZrE7HRGL4sUmdQ6Na0eNmp+LG3qs7qhPdwOHPDehmsbJh8Bif/MnVgzqkCe1JdCR0f+bb9Lzi4eQviyBwQMCcJiDwgYEgxcjI92482WO0mcsBlskrzLzjvzpm8+zhmO+hYRHNxzwhMa/I//w6+rdp/+Lz8VlV8+c0bVHTnoAxbG7/Vi1FNEbgAABedF2nniUQM0jXXK7JAz19zxe7wqkBjNq3a5gj/utLTYNpL1dcyDZumu6y0vIjaIOhoAyhXvDVcV75E30tA7wHka/7IJcFknr78ijSlnvAZTeS+asmoBAFcooGj/qBfjL52/oNpliF+QOecAYLroabcPTfukRZcvanIJJvrI5bTXY448FjcbWgRX7WiH3z7b/TNeVapWLqk6DgqbmvDBLhxQBQDHJz1Png1e2uxamOJ8TcOXPSBgSBAWe0DAkCAs9oCAIcGAPegE7R7paTirjjUekAMTUnx+TKqbnIk2W1r0XmFjU94s9P73vle1++D7/1pUXp3XBIgdSut08qg33527ockINub9tS5euKDqDpW8DpnJ6DFeXPCpom6QTr3Z0V5heSIsXFvQKfbarhyVx4goo9HRJqNK0+vz9VU94+P7PGnleKoUlVMNE/FF+wV1w2OeKHj9NUvXznb0PTfIbJYykYpJ0r9536ZoyEr5nWqaN5pTYL36/Iv+WgU9H2MULSYprW/rPSTLy+4rmWyjkNX3uUxkmjcW9PuSyPj7TBER6PwNTeYxM+ffudy49r60HpK74aZfdhHJiciPROQ5EXlRRP5Z9+/HRORJETknIr8vIpmb9RUQELB36EeMrwP4iHPunQAeBPBxEXkvgC8B+Kpz7iSAFQCfvWOjDAgIuGX0k+vNAdi2xaS7/xyAjwD45e7fvw7g1wH81k2v2DW5dYwHXW+/HzMeKsd50FmMk1mnsulFWHvO177ylah8ZL8OYmlsepHwfNV7tWWzWvxcIaKItvE6S5D0Zb2dmHjgGon0B0/MqXY3lryakDABKG1KKTU67s04zhAyOHiR8+WyDuTZAHn2TVHG2BXNY5dNemFuZER7v40d9aayEgl9+50mnuBUVv/uz76v6vZRgM7ZZW8OnCxqs9My8dKPzWqPwsOTPrho5flzUbmS1PdSJJUhZTK1cmSWTcXFb2SBRHD7TiwuetE9ZWzLE8QtlyTVbtJw/rXI47Je16bU7ff4lk1vIpLsZnBdAPBdAD8BUHbObc/yZQCHepweEBDwFkBfi90513bOPQjgMIB3A7i33wuIyKMiclpETteML3hAQMDg8FOZ3pxzZQDfA/CzAEoi0bboYQBXepzzmHPuYefcw7ls2MMLCNgr3FRnF5FpAE3nXFlE8gAewdbm3PcAfArANwB8BsDjP82F43T0HSnceqRs3gnidTeqVYVMMPvIbXJlZUW1m6CccI2a1rfHydzBpJUbVUO2uOIjnpIpHZ1Uqfj9gsa61rfH9/k+l53Xye69VwtS60SemTNRWBuvexfL1RXfrm2IPgpHvPtmelO7sIrzfVaJrKFoCEFSpMtubmriiRtveL729CbtYYzoCLtq25vs3vHQg6ruXe/zZtFXnvAEn0dHNUf9mWf/MirXG9ptd5zMWks3/P7A7L16HKUJr+sXizqdc4pSWO/cFyLCSdLTrc6epxTWI3MlVXfslH++i5RnIDsxqdqt0f5MpWMINhI3N731Y2c/AODrIpLEliTwB86574jISwC+ISL/E4BnAPx2H30FBATsEfrZjX8ewEO7/P08tvT3gICA/x9goB50iQ5QrNzcyGZFJRZYWiyOmvRJHDrnjNdZIelNPrWyF7OzTm9b8L7CkvFgGit6812ZyAn2EzEGABSOz0Xl61d05FKH9Iuly3qbo9Tw1z5a8qLq6msXVLsfU/qq0qQ2NTGv+diMrztPYjUATK76+Tk0d1jV4Zofc77ix5ver691jTz5ylVtCmqsUKrn/V5tulHSprcEKILPpMo68x89B+DZi57Xb+2ofraj5PF3dGafqhsredXr7IxXqR6Y1KK6I7VpfFxH3y2u+nGlClq0rhCBx3ef82PMlzTn+wqZbWemjqg65P28Jif8XK21tZrXpHfHmm23j13goAsICAiLPSBgSDBg8gqHjthAgu0q/7sT5wUU4yRnrqTRAe8kk0eUoQ1u0S5n01A4t2iXWogNr7qud+MzxCM2NanFvjPPPR+V77tH77K/QHWjtCM8YYIe3nG/pxhumvFn815MrpMYeGhWp126UfZWiBUTNHTlDU+usJ/E0ZShX06SJ1ghr0kjsvScMjG8ajXyZrTBS6msn+Njc3NReXJK78bXia9v4fq8qisQKcU9xA04M6PViTYRjjijHrLXXLut59vRe1AqlaLy7GEtxt+V9ypEtqDVhCTNXYOeWce+xTTfHbMbvz3GOC/S8GUPCBgShMUeEDAkCIs9IGBIMFjyCnjCyYRVqlmX36F3UIod5q7YQSTg0dnxM+YvmHSsF2k0SS/nMgC0iIQhSWemjbmDddS84XUvZLyuOG08pCbJtMemlcuGYLFGEU9NQ0px8m6vl3ZIlz1x5C7VLkH7Fof2a957ju7LEhtELqFfl3SS0j8VdNRbh+qKOa+vOqNrNmkcm6s6Ei3Z8m1HCn4PY2VRk4rkkn4voZ3XHotpiiScmzsWlQsdfa0Kp24yEWVNItqs1XR8R6XjdXb2mts3M63aJWgOGuYb26B9ANbTEyb9cpLWRcs89yjrVcyeVviyBwQMCcJiDwgYEgycN37bqtGOETesFC9kXlKmhThvIdMHm1Pa6u9arGTToBWR2XsvS+mOWsZ7zBGZQtKM8ehBH/Z/2mSQHR8h7nIyQ127prnnTx0/7sdouN9K7OVH/Hc3DIlGZakcldcy2gzV3PDmsBqJ0vaRNUiVWTPpjlT2VAptLuaMiY5UniNH51Qdqxpsarp4UXsDnrj37qh8zHjQdehF4NwBHft+0H02a3pOVTujfnJgk0t6Md7yC26QKtCyORPoHUmk/HmplG7Hql2ncXNPVIvwZQ8IGBKExR4QMCQIiz0gYEgwWJ1dACd9RL0Z7VB5L6oqE8DPLreG+5ujzRIc6WaIGFnZd8a1V6hLJjRwVUvq4Os6hhDyCOnsrzyvc8lNjXq32FEyIR1854Oq3eK854rPGq71tUUfqbdGUXs2R9kR4r2fmdR67sF9ntjh+rOeWNPuP6TpUXYMsQWnRK6RCWnE6OxsUms0tFlLyCTFvOjOuKyOjvp9iiljzlze8Ca2ND2XlOj3I5325jvripok014ioZ9nh17ODN1bxpgA89Rl25gw2UzpaFzWvFZv0jM0en+6G60Z3GUDAgLCYg8IGBYMVox3QB9S/C6NdhdNdvbFJjp9a+yxJ3SiGNGUxSDbP4uxWTKNddLaVLOx4aPgKhs6Iq540BNFfPCDH1R1Vy68EZXZ3FY0ImGLVIOxjOZQ7zDX3qSPDmvOa274UeJ5X1nQHmmcxmhklFIajelorTZ70KX0ZK1sEkFI3s9VJqcj53JFb/ZbmteprHIZr6JMjHpPxJP3nFDtikUvPq+ul1XdOqWfnjnkiSFSdS2OZ0i122walYTSXLVbvd/N4oh/FjkTBcjf1YZ5n5vUJUdhOiPGJzjKsKDfiW1TXyrVe0mHL3tAwJAgLPaAgCHBwD3o+hHj4/gpkn06DiVNhItKNxWTCJbPs8E6KRLjOdjlwrL2cJu/6o9bDS3i53JebJ2k3XcAcJR2iId/Y3lJtcuTiMjEDYAW8ZlMoWUmnumSV8plVcciYo0IPBItfa22Ek0NmQLNKxNs1Fp6PlI0/rvvv0+PMWdF4S3ssLTQznTVcA926p4/bpW8/DJOi/GOduqbbdMH3YwzlM1pCn7Jj/g5TRoiDqH3r9O2AVYkutPfEyZNVArkXZc23Ind98qqpYzwZQ8IGBKExR4QMCQIiz0gYEgwUJ1d0J/ObT3oVB1V7SDA4N8umx6nzdFbXu9K2E7oWExaaW7K+vtm03Cmk35cmtLkFamC19mf/PFpVXeCor4+TKmQiiYqjaOfLlBkGwBcveo53ys0LqvnbhLnO3twAUBp2pvsNte8ntsyeiLr6W3TB8jEliTTWLqo9XCejw3j5cdppufnPZFkvqjNTmM0x4eJsx8AUmVvYiyve2+6Ua2yI5f3unfSbPhkyJxVyOg5aFG0XI5MpG1LaEL5CBomUjEl/lhIT7fc8Oxh2Dac8tXqlsnVkoMw+v6yd9M2PyMi3+keHxORJ0XknIj8voiErI0BAW9h/DRi/K8BeJmOvwTgq865kwBWAHz2dg4sICDg9qIvMV5EDgP4mwD+ZwD/SLbczD4C4Je7Tb4O4NcB/FZcPwlJIN8VSW3QA4ssqaQWOZnEgMUmizSdZ5yPkE17cZHNUwkrqtO1pic1j1i94sXiRtWPf72uudAlS4ETeR2oUiNeu7vuPaXqkqResIg8XdJecktL3hQ3MavHmBvzYmudzFwtw0HOwUVThzSnPD+L/XM+cGdxRafDWln1GVOnjxxQdSUyh7HpsGk87VzWf29GZ7TK8/rZc34c+/0YcwWtCoxP+eCXRE6/0mtVr4Y0hdInmVd/ZdW3c0ZtWljzHPtS0M/iKgUb3f+e9/l2ae0p6Og4l9bXloav43XhjHkwT2bbpFGbts10CbN2VJueNRr/AsA/hTf/TgEoOz+aywAO7XJeQEDAWwQ3Xewi8rcALDjnnn4zFxCRR0XktIicrtQbNz8hICDgjqAfMf59AH5BRH4eQA7AGICvASiJSKr7dT8M4MpuJzvnHgPwGAAcmCj99MRZAQEBtwX95Gf/AoAvAICIfAjAP3HO/YqI/CGATwH4BoDPAHi8j77Q7EYbtY3uzXpiYkeOK3JXJKbAhPF1ZVLJTlv3kSRdPOFiXAqJPCBlzHJpIjFIEQGBjQarVb0OnzI6e47MRsmiISAgcw3r/RXjvtkmt9p2W99Lk0gKOZqqZVP8guuMqyvtFzTh9cZqQrebPeYJMGYPa+557nO9Q3qocTet07UuzWu3Y94LGSGzXCdlXEXJDLVh9k+q5I7LefBWbqyqdswbX5rRewJjxP1fbmg9+ud/4RNRed8hn4p50xBsLK97Ysp0Wr8TSUUy6d8rS9KRVJZl88y2zayWEZNwK041n8PWZt05bOnwv30LfQUEBNxh/FRONc657wP4frd8HsC7b/+QAgIC7gQGHPUmSHRF4aQRKpg0wkb7CInWiRR5whmJRUhEbDu9GcgSeYdFesMHhiQfazFbqC2Xq3Xt+cXHlj+cpW5LSsFc8RkSOXeksiIx1rX0GNkbjqO1LOdCm8TAhklNzQQKNVIhrKfgKRLjx/dp7jcWn+WqTwHdMXPa7pEmCgAO5DzRxzSlabYEDaPj3hxm8xGwepgh09XYpEmtVPP3lsxq09vqcjkqv3ZJb0393Kd/OSovUFTdpjH9gkxi6ax+J1ROg6Z/FlbbZJOd9ZSrd73y4iJGg298QMCQICz2gIAhwUDF+A4cKm0r32xBM0SbnWNHu9Ht3h50inrYiHptEvEdi+5GZejQjnjLZHGtk0hYYzHYeAOyKGZ3Te0xQ6kvpNbYNFScusiKrUzkwCpDy1gnWmrHXY+JKYwrbX9vVozPUFBLo2N52/wzqxAvHlNHA5rC2dIgM8EG03PvUN/oeIMCdwBgbaUclZmOurKwqdpliXiiZfxBri4s+jGNl1Qdz7GQGlYsaFG9QQ8qbTz0+P1pMv+dIfrgdzqV1B5629dOBPKKgICAsNgDAoYEYbEHBAwJBpz+SdBOdU1vNrKN0/tYb68eeq643ua7hkm7hOTu6X2sntikw4bZO2hSiqMc6eWcahkAmmS6sXV58payxINt8uJqJomoINH7NznO1BKHNvnQta03FpneKjWv22byWg9V6a0b2nOtxumfKG3RWELr7CkyIzaqFVWXp7vjPRIm7QSAFI03aZ47j9jV/Ziu31hU7Q4SyWbHmFKTRCr5Nz/5n6s6pPx7NV7yEYcbhnu+Te/Ojnefnm+aTb9mHXDegrTRzbfTRgXTW0BAQFjsAQHDgoGK8a1OBzcqW2JhNquDAdgEY8V2FSRDaZ3EZFllL7mGMVukScBhUceaKpj/rm4CclhczJGpKWHMWjkiocgZDz0VkLODiGP3PnfSEfg+Umb8yR6CnFWN2ITZtjzmxJFWJ9H3MGV+tX1awhH2BlSegYbUgclDCoYnPs/nkRflmPG0y9M7UTHZWVVbmtORkubsT2T8uJbXdMou5ri7/x3vVHVvzPvUWelRP/7Kuu4jk/V1zqiOnEosPU7jslx19P6JeXe2xfqQxTUgICAs9oCAYUFY7AEBQ4KB6uz1ZhMXF7YICvJ5bYLJkgmjZcwnfKyi48xvFeuQI5SSGAAyGa+Xsutl2uiQHFHVNMqyI+71DrVrV7UbKY/RGbIDsAnG6PMpuh8mvuy0tE7Npierb7PuzH1YDnye06Zx92Vec3ZFPXHsmGnn+7DuyTzHGXq2WXPP7FoshoixU/fjcOzhbPZINlc8EcV6eUXVdWr+3thtt1ozxCdlzylfN+/fw+9+e1RuGr72LJFfMolJO6XNlHViErHvN78vvJdlySP1e6Z19u301pbQhRG+7AEBQ4Kw2AMChgQDFeMTiQSyxS3xOpvXZhb2KurUtVjsSGRhq5zlTuuQOcxVtRdUlsRYVhksJ5oQh5utYxIwl/Z1hWzv9ExpI95mFMed7j9FnlRtEqVtVN0YqShNo2uwKU6Z4axXIpnerFjJYj0/l+lpzVF//o2LUdmml2KvPGXqhAGNI2Fyg3Uo6pB52/ImHXKN01Ybz7VCxp/HHopLLf1+5Gn8R49oE+Mjf+NjUXnBmOVGRr3ozl6bloClUPDjWF/XkXkqjwHNUMc8lzYdZ4yJdZv4I0S9BQQEhMUeEDAsGKgYP1IcwV97+D0Adu4iN0lstWIlt+1VBnSAixV9uY7P63S0l1KNdjytN1K75XdzL73hd2+n8/o3s1LxAR2S1X3UEn5c9bZWV9rKK8//PW8CPzYbXpRswRJb8A45BbtsarEVLX/toqFmrpZ98MvUA3NR+eWrF1S78XGvTiRNUFKaXq0ihaO01o1nI5OFGKpqkJqzSsE0yaYmnsjlfP8r9bKqW1z0AS/7pj1PXtt4p11b91lij79Le8ldXFyIypOzc6puvUJU1eQl19xcU+3aDW8xyNa0KjBKVoj1Td8ubdTDDeJVbBsSkM3u3LViImHClz0gYEgQFntAwJAgLPaAgCHBwE1vhS5JgI3CYp16B3lFTEqbXu2s3s91vco3A+vwXN64ckm1Y7OLNcGoOqNfcZ31eGOwOaxp7pPnMY6kg+vYswwANun4aKkUlZkAEjCc+IaYnr33VPquHWmo/HHDRHIl0zQfMZ5hXFco6Ii4sTHPKZ8nc29iU9/z+XPno/IHPqrNiHzfdi+InwXvBdmoTo6qq4reZ0nS/GSafk6zZq+mTnOcNv23+4h66zc/+wUA69iKwWw55x4WkUkAvw9gDsAFAJ92zq306iMgIGBv8dOI8R92zj3onHu4e/x5AE84504BeKJ7HBAQ8BbFrYjxnwDwoW7569jKAfe5uBMSiUQkEllxIy79Ex+z2LQzTZTvY3R0tGddr/KbxQ+uX1XHjrnbnSWo6A0WcTsxJsZkHF8f9xEjxvO4Ngz3G5/H85gxnmvqzlxvMV4FBkGDx2vvE5TJNkkcgq5pOPOSJD6ntHjLYj0Tn1iVZGPDm8NKpLoAwMSEJ69YNwE0yaS/Xo2Cnqw60SEvv0bNeG3SrPR61wEgQSY667GI9s3V0X6/7A7An4rI0yLyaPdvs8657Ry71wHM9tlXQEDAHqDfL/v7nXNXRGQGwHdF5BWudM45EZunYwvdH4dHAWCCKXcCAgIGir6+7M65K93/FwB8C1upmudF5AAAdP9f6HHuY865h51zD1vRKSAgYHC46ZddRIoAEs659W75YwB+A8C3AXwGwBe7/z9+s77Kq2V8+zv/F4CbmKRMHesuvcr2mHWw7n30dS1Gv/rw/QcPqHZct4OIg/TSlIlc4rYdJoaIGaPNHcf3o8Yfszdh73Ni0ruVcn87XJxbu7v3AkAiSfq9izOhxeyZ9DjPzmmnyvsb+l6EEuPVyGU4Y9ImHyNiDtbRAW1iZHJLAKg2dt+Tse8V58Lb8czYSsk6uyHzsKmqGdt9xmnu/YjxswC+1Z3sFID/0zn3JyLyFIA/EJHPArgI4NN99BUQELBHuOlid86dB/DOXf6+BOCjd2JQAQEBtx8D9aDLZnM4fuokgHgxPpb7OsY00a9ZjtvZa/US1e0xlzPGA425w9qIUQUS/V3bitlxkX/KLJfobWLkupTh4Tsyd1dU5nm0XHhtTp9tpFlODcUpoHfkW6ZXUIxIr++N1AnTrkk8c1bE5/Pqdf+cKk738dBDD0XlvDGbVav+vMzIPlW3WvVEFLGqRszzZJNaukN8fTa3QoNUAUu60vU+DOmfAgICwmIPCBgWhMUeEDAkGKjOnkwlMFbacr+MS1sbFxHHsKaIOF28l+ntdujsBcP0kqQIpx0mr8Tu49iqojmI2ZtgXdbqhon07o9UkmYPg+fAuF7O7PfOkG3S5ztWI6TpSdn9B2rbIK71VttE35Fun8xoHbVG5scCzXc+pclK66zbG12cufmFyD5rNe0ifPDQoajMkXKAJjbNm3eO8w4Iersxtzb9vdh9HN4xiXMf5mftjLdsZHqLieIMX/aAgCFBWOwBAUOCgYrxAolE1zgvOSuK9DLFWfHWpnLq1Udc1JuK0IrxoONy1qokqd6mNxYJLUmj6/HTG2eKtGJ8klQK5m634j2L7so0BiBFakiHSDTsOFiNSprE0o7SHXE6KUty4cjTLp/V4nmViCKa7Axoou9A7Uz3SvVoK4dCfS9MEjpJHoQA0E57EglLXsGpuBrMWd/WKmCc5yevhVYMaSqnzxYzBZH2EggnAwICwmIPCBgSDFSM7zgX7cymjLjhLCEbgYkW1K56TB87VQFfTvCJVoxXHHF9EluYXVPeELbkFYqz3vzWtokD3sV4XPEc7LBUUP98XiqlVRzena83TRZXzvDKRBEdOw4a/46Net+2Sec544WXkN5eYY2OD1xp8hwYMbhFF6+Z/nl+6lSVTOs+rl71BCScmRUAWiQzL65oPvjcSMlfu+bH64wYXxjxEZ8Jw0HH72OKRPUdRB9UtmnFkhEHXUj/FBAw9AiLPSBgSBAWe0DAkGCwHnTJBEZGtiKKOJIIAGoUuWRNaGzi0TnbdL6uNnln2T44l5wjG5c1AbIKnDO83dyWyTFG8rpdk/RmS3xZr5J+nNR6XZNsQ9Mlb/6xOjvfS96kvm5QmuMmea5lja0mXyxQudizrl73edVGKFU0ACwve+bwTFL3zwSRvC8ixgTIJrVyVROOZJK+bXnDj6PRspF+tIdhnmeb2rKH3ur6kmp38K6TUXltTevlLu3nR5J6/OXVVTry1y6O6Dmtb9K9mc2mVeqDn23W6OXs2Vdp6Hff9xk86AIChh5hsQcEDAkGKsanUinMTG0F/1uOOBbrrQjOojCL1tZ7jD2M9u/fr+rOn/fpfeo1327fPk1G0EtUB7TJayTvRd219XXVbmzKc5h1msY7jbjPxPClZchzjUXJwxSkAcSnt2653YNkOg2dHrpcLvvrFrUqUOG2pNckzbchT2JxKqXFeEf2x/VNL4JvrOsAlAY9s6pJK6243/haSWMuJdOereP3pZCj+9T8FLHmzDbPY8d6fvol1CBzG5vhAJ0HoBOTapxNtTsCsdgb0Jp7u3Vx2czClz0gYEgQFntAwJAgLPaAgCHBQHX2zY0N/PCHPwQAHDigudaPHDkSla2Jh3VULlvdns+zpr2VFW8mYn3K5uRiU1YcJ7sizjCmtwKZsuYvXVF1YwVfl0/r88bHS1F5df5GzzHyvdgxNp3W5bZhI9vKtCdw4r57dGN233S9oxHjCEd4q2J5teyva9xNKxX/LGwSEZUOmcq1utaHN2lPwHX0HsZY0b8TIyP+3SmZCDuGvReOPmuZ76OQGzKfZ98/0HORth6jcqGm59k0z4xdhu3z3K4K5BUBAQFhsQcEDAsGKsa3222UuyLo/PXrqu4n585F5Xvu0WLl8ePHo/IkpeaJI8BgUxugzR0patdqak+kFonn+RgPOjbHFEa12rGw4NPeJQy/G4tp65VNVZemqC/msduRdonGXG/p8TdJjGWijLa5zwpx3TPnHKCjBzNCYqqJ5OoQU0SjpU17HNG3TibMmvF6bNEjbFtSh5w3vSVYVTKfqBaZCutVPY7apjeLlkn8z4/3TjpsaOyUCbPmdIRgKuHHGJf2q0P3nXRGjO9hbrPPvc1ivOXH7+Oz3deXXURKIvJNEXlFRF4WkZ8VkUkR+a6InO3+P3HzngICAvYK/YrxXwPwJ865e7GVCuplAJ8H8IRz7hSAJ7rHAQEBb1H0k8V1HMAHAPwdAHDONQA0ROQTAD7UbfZ1AN8H8Lm4vrKZDI7fdRQAcPbsWVVX3fDiliW2GCUygSaJbNcXF1U73mW3mThbE6WozLv4lm+MPanYIw8wu6YkelVM+qeLFy9G5W2PwW0k6Pd1g3apAaC56fs5fsSnYLKefGp32HoRNuu7tqtBi4RVmkdLyb1BaZIyae+5Zj2/4vj6EkQOUefAFRNIkqCsqBt1LSILeTq2ScVpWvGW+QtNyiROUbVBqswOmmbXmyykQW3rTV3XBvP17d4fADTpXUqJUYd6UJTvGGOMmiDJbQ+6W9uNPwZgEcC/FJFnROT/6KZunnXOXeu2uY6tbK8BAQFvUfSz2FMAfgbAbznnHgKwCSOyu62fk11/UkTkURE5LSKnq7X6bk0CAgIGgH4W+2UAl51zT3aPv4mtxT8vIgcAoPv/wm4nO+cec8497Jx7OJ/L7tYkICBgAOgnP/t1EbkkIvc4517FVk72l7r/PgPgi93/H79ZXx3n0Oh+3RvmK8+6RssG5lNUU46in/ZNTumbId3Tcm4rs1HS9zda1GYzZRpb1d5erEOxt9fVJf0798JLL0blY4eOqLqDU9NRecQQT0wWfXQfR+Mtze/6Owpgp77tGkR6SH+3Oh57CjaMRxfPAUeUdYzpLR2TGipNUYHMX2953Rv03HN57UHnMuQ1Rx5j9bZ+tuw1mMgYQnVGy5+XMe06MXztLRq0UaPRJl28xVGMRnfm/R+buomfDXvGOfPM2CuxaUyp6MODrl87+z8A8HsikgFwHsDfxZZU8Aci8lkAFwF8us++AgIC9gB9LXbn3LMAHt6l6qO3dTQBAQF3DAP1oBsfG8PHPvYxADsDOJ566qmo/Mwzz6g6Fpnn5uaishVlFskUZ4Mq2CzHItXy8rJqZ8ViRq8UVUsUmAJoTrGX1rTZrHXUm7VmS1oNGc950ZfHZUVwVidsdlYeV1yaqwkyOdr+02y+arMIq2XYNBFW2P4TNI8smnYMrzkLnTs49qmyRZ6Bm8Ykys8zGZPOi+9zfHwcvWCDWFodfif0+8FzUiXvPWfe7xaP2diW2YtTedMZ1YjFeOs5mQiBMAEBAdsIiz0gYEgQFntAwJBgoDr7ysoKvvnNbwIA5ufnVV2W9ETLtc51rItbXnfW4a0OyZzbrOOtGH2br2X1Oo56Y718aUlzkE9MeX14/Ybuf5HavnFWR+YtENHF7KQ3vT34trerdutEcJkyvgu8N9GkSCtLhFAcHaN2Wmcvkntygsxadp+lpXLCaR0ySbqs2mPIacIR5oavGZNXlp51ikxlGUvYSOWEIZxMkt6baPk9F/uOxblJtzg1YFr378g0qdyYrWmMdGxL3Mnzwzq7M/sbnFfA7rMkuqF6LvDGBwQEhMUeEDAkkLit+tt+MZFFbDng7ANw4ybN7zTeCmMAwjgswjg0ftpx3OWcm96tYqCLPbqoyGnn3G5OOkM1hjCOMI5BjiOI8QEBQ4Kw2AMChgR7tdgf26PrMt4KYwDCOCzCODRu2zj2RGcPCAgYPIIYHxAwJBjoYheRj4vIqyJyTkQGxkYrIr8jIgsicob+NnAqbBE5IiLfE5GXRORFEfm1vRiLiORE5Eci8lx3HP+s+/djIvJk9/n8fpe/4I5DRJJdfsPv7NU4ROSCiLwgIs+KyOnu3/biHbljtO0DW+wikgTwmwB+DsD9AH5JRO4f0OX/FYCPm7/tBRV2C8A/ds7dD+C9AH61OweDHksdwEecc+8E8CCAj4vIewF8CcBXnXMnAawA+OwdHsc2fg1b9OTb2KtxfNg59yCZuvbiHblztO3OuYH8A/CzAP49HX8BwBcGeP05AGfo+FUAB7rlAwBeHdRYaAyPA3hkL8cCoADgxwDegy3njdRuz+sOXv9w9wX+CIDvAJA9GscFAPvM3wb6XACMA3gd3b202z2OQYrxhwBcouPL3b/tFfaUCltE5gA8BODJvRhLV3R+FltEod8F8BMAZeei3ESDej7/AsA/hY9nmdqjcTgAfyoiT4vIo92/Dfq53FHa9rBBh3gq7DsBERkB8G8A/EPnnGK1HNRYnHNt59yD2PqyvhvAvXf6mhYi8rcALDjnnh70tXfB+51zP4MtNfNXReQDXDmg53JLtO03wyAX+xUATLV6uPu3vUJfVNi3GyKSxtZC/z3n3B/t5VgAwDlXBvA9bInLJZEoRcsgns/7APyCiFwA8A1sifJf24NxwDl3pfv/AoBvYesHcNDP5ZZo22+GQS72pwCc6u60ZgD8IoBvD/D6Ft/GFgU20CcV9q1CtoKmfxvAy865f75XYxGRaREpdct5bO0bvIytRf+pQY3DOfcF59xh59wctt6H/+Cc+5VBj0NEiiIyul0G8DEAZzDg5+Kcuw7gkohspzHepm2/PeO40xsfZqPh5wG8hi398L8b4HX/NYBrAJrY+vX8LLZ0wycAnAXw/wCYHMA43o8tEex5AM92//38oMcC4B0AnumO4wyA/7779+MAfgTgHIA/BJAd4DP6EIDv7MU4utd7rvvvxe13c4/ekQcBnO4+m38LYOJ2jSN40AUEDAnCBl1AwJAgLPaAgCFBWOwBAUOCsNgDAoYEYbEHBAwJwmIPCBgShMUeEDAkCIs9IGBI8P8BGkCqFNZrKp0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image(X_test_cover[0])  # Cover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "id": "9jwfYLmoRKSx",
    "outputId": "13b770b2-c25a-4ad3-9855-b304b86a468d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.9/dist-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image(pred[1][0])  # Cover with secret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving complete model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "UACJL5D0Vfi-"
   },
   "outputs": [],
   "source": [
    "model.save(\"./models/complete_model.h5\", include_optimizer=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving only the reveal model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "reveal_model = tf.keras.models.Model(\n",
    "    model.get_layer('hide_layer').output,\n",
    "    model.get_layer('reveal_layer').output\n",
    ")\n",
    "reveal_model.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "reveal_model.save('./models/reveal_model.h5', include_optimizer=False)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "tensorflow:Python",
   "language": "python",
   "name": "conda-env-tensorflow-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
   }
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "08247855086944968fa02c8366d0d1b9": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "10833ce3156a497bbcd7a857cfbf7f96": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "1cece1ea8af947ffb1d094df6ba5bb3c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_a9864e2189894df4aa9a205d80708931",
       "IPY_MODEL_3af3feb5b0e848c2a6ec65dddb10bd06",
       "IPY_MODEL_9f8d7884768e4b8c821194c918b7a387"
      ],
      "layout": "IPY_MODEL_08247855086944968fa02c8366d0d1b9"
     }
    },
    "32763fc1ffbb42d28b363a94aa2e9f25": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "3af3feb5b0e848c2a6ec65dddb10bd06": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_cab5f845c0f7442e91ed81324fd6240b",
      "max": 2,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_10833ce3156a497bbcd7a857cfbf7f96",
      "value": 2
     }
    },
    "746208597d004f54ba8ebea90052d58a": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "8aca562f7bd14cbe8faef455f2eba703": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "9f8d7884768e4b8c821194c918b7a387": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_746208597d004f54ba8ebea90052d58a",
      "placeholder": "​",
      "style": "IPY_MODEL_32763fc1ffbb42d28b363a94aa2e9f25",
      "value": " 2/2 [00:00&lt;00:00, 55.35it/s]"
     }
    },
    "a77e545e4c8a477fa23fc24532373e7e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "a9864e2189894df4aa9a205d80708931": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_8aca562f7bd14cbe8faef455f2eba703",
      "placeholder": "​",
      "style": "IPY_MODEL_a77e545e4c8a477fa23fc24532373e7e",
      "value": "100%"
     }
    },
    "cab5f845c0f7442e91ed81324fd6240b": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}