Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bcd74382",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "import PIL.Image as img\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4cf629a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.cifar10\n",
    "(training_images,_), (val_images,_) = mnist.load_data()\n",
    "training_images=training_images/ 255.0\n",
    "val_images=val_images/ 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ae20e91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32, 3)\n",
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1dce6826910>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(np.shape(training_images[1]))\n",
    "print(type(training_images[1]))\n",
    "plt.figure(figsize=(1,1))\n",
    "plt.imshow(training_images[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7e3601a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_images=training_images.reshape(len(training_images),32,32,3)\n",
    "val_images=val_images.reshape(len(val_images),32,32,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "5b94d986",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=None\n",
    "model=keras.models.Sequential([\n",
    "    keras.layers.Conv2D(32,(3,3),padding='same',activation='relu',input_shape=(32,32,3),name='first'),\n",
    "    keras.layers.MaxPooling2D(2,padding='same'),\n",
    "\n",
    "    keras.layers.Conv2D(16,(3,3),padding='same',activation='relu',name='second'),\n",
    "    keras.layers.MaxPooling2D(2,padding='same'),\n",
    "    \n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.Reshape((1,-1),name='rs1'),\n",
    "    \n",
    "    keras.layers.LSTM(1024,name='lstm1'),\n",
    "    keras.layers.Reshape((1,-1),name='rs2'),\n",
    "    keras.layers.LSTM(1024,name='lstm2'),\n",
    "    keras.layers.Reshape((1,-1),name='rs3'),\n",
    "    keras.layers.LSTM(1024,name='lstm3'),\n",
    "    \n",
    "    keras.layers.Reshape((8,8,16),name='rs4'),\n",
    "    \n",
    "    #decoder network\n",
    "    keras.layers.Conv2D(16,(3,3),activation='relu',padding='same',name='third'),\n",
    "    keras.layers.UpSampling2D(2),\n",
    "    \n",
    "    keras.layers.Conv2D(32,(3,3),activation='relu',padding='same',name='fourth'),\n",
    "    keras.layers.UpSampling2D(2),\n",
    "    \n",
    "    keras.layers.Conv2D(3,(3,3),activation='sigmoid',padding='same',name='last')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "3eb55f51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_42\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " first (Conv2D)              (None, 32, 32, 32)        896       \n",
      "                                                                 \n",
      " max_pooling2d_84 (MaxPoolin  (None, 16, 16, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " second (Conv2D)             (None, 16, 16, 16)        4624      \n",
      "                                                                 \n",
      " max_pooling2d_85 (MaxPoolin  (None, 8, 8, 16)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_34 (Flatten)        (None, 1024)              0         \n",
      "                                                                 \n",
      " rs1 (Reshape)               (None, 1, 1024)           0         \n",
      "                                                                 \n",
      " lstm1 (LSTM)                (None, 1024)              8392704   \n",
      "                                                                 \n",
      " rs2 (Reshape)               (None, 1, 1024)           0         \n",
      "                                                                 \n",
      " lstm2 (LSTM)                (None, 1024)              8392704   \n",
      "                                                                 \n",
      " rs3 (Reshape)               (None, 1, 1024)           0         \n",
      "                                                                 \n",
      " lstm3 (LSTM)                (None, 1024)              8392704   \n",
      "                                                                 \n",
      " rs4 (Reshape)               (None, 8, 8, 16)          0         \n",
      "                                                                 \n",
      " third (Conv2D)              (None, 8, 8, 16)          2320      \n",
      "                                                                 \n",
      " up_sampling2d_84 (UpSamplin  (None, 16, 16, 16)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " fourth (Conv2D)             (None, 16, 16, 32)        4640      \n",
      "                                                                 \n",
      " up_sampling2d_85 (UpSamplin  (None, 32, 32, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " last (Conv2D)               (None, 32, 32, 3)         867       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 25,191,459\n",
      "Trainable params: 25,191,459\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ca4b93d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "196/196 [==============================] - 235s 1s/step - loss: 0.6210 - val_loss: 0.5948\n",
      "Epoch 2/10\n",
      "196/196 [==============================] - 211s 1s/step - loss: 0.5850 - val_loss: 0.5802\n",
      "Epoch 3/10\n",
      "196/196 [==============================] - 202s 1s/step - loss: 0.5765 - val_loss: 0.5752\n",
      "Epoch 4/10\n",
      "196/196 [==============================] - 198s 1s/step - loss: 0.5729 - val_loss: 0.5729\n",
      "Epoch 5/10\n",
      "196/196 [==============================] - 203s 1s/step - loss: 0.5703 - val_loss: 0.5821\n",
      "Epoch 6/10\n",
      "196/196 [==============================] - 208s 1s/step - loss: 0.5700 - val_loss: 0.5687\n",
      "Epoch 7/10\n",
      "196/196 [==============================] - 190s 969ms/step - loss: 0.5670 - val_loss: 0.5680\n",
      "Epoch 8/10\n",
      " 14/196 [=>............................] - ETA: 2:58 - loss: 0.5687"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_12296\\1694653630.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhistory\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_images\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtraining_images\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m256\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval_images\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mval_images\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     66\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1648\u001b[0m                         ):\n\u001b[0;32m   1649\u001b[0m                             \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1650\u001b[1;33m                             \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1651\u001b[0m                             \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1652\u001b[0m                                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 150\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    151\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    878\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    910\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    911\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 912\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_no_variable_creation_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    913\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_variable_creation_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    914\u001b[0m       \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compiler.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    132\u001b[0m       (concrete_function,\n\u001b[0;32m    133\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m--> 134\u001b[1;33m     return concrete_function._call_flat(\n\u001b[0m\u001b[0;32m    135\u001b[0m         filtered_flat_args, captured_inputs=concrete_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0;32m    136\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\monomorphic_function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1743\u001b[0m         and executing_eagerly):\n\u001b[0;32m   1744\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1745\u001b[1;33m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[0;32m   1746\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\monomorphic_function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    376\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    377\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 378\u001b[1;33m           outputs = execute.execute(\n\u001b[0m\u001b[0;32m    379\u001b[0m               \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    380\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     50\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 52\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     53\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     54\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "history=model.fit(training_images,training_images,epochs=10,batch_size=256,validation_data=(val_images,val_images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0fc86c09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 4s 12ms/step\n"
     ]
    }
   ],
   "source": [
    "pred=model.predict(val_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "8cfa45e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'reconstructed')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.imshow(val_images[1])\n",
    "plt.title(\"original\")\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(pred[1])\n",
    "plt.title(\"reconstructed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f084bee2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'reconstructed')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.imshow(val_images[1])\n",
    "plt.title(\"original\")\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(pred[1])\n",
    "plt.title(\"reconstructed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "dc2100ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "original=(val_images[1]*255)\n",
    "recons=(pred[1]*255)\n",
    "\n",
    "original=np.squeeze(original).astype(np.uint8)\n",
    "recons=np.squeeze(recons).astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f6e08340",
   "metadata": {},
   "outputs": [],
   "source": [
    "original=img.fromarray(original)\n",
    "recons=img.fromarray(recons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d4874cef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.mkdir(os.path.join(os.getcwd(),'results_vae'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "75b1ddc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(os.path.join(os.getcwd(),'results_vae'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "b3c47604",
   "metadata": {},
   "outputs": [],
   "source": [
    "original.save('original.jpg')\n",
    "recons.save('recons.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2740d512",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}