{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "127a506a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "%matplotlib inline\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "229926ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "categories= ['Cat', 'Dog']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "71581e1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "what= {0:'Cat', 1:'Dog'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "95bea914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for category in categories:\n",
    "    for img in os.listdir(category):\n",
    "        img_array = cv2.imread(os.path.join(category,img), cv2.IMREAD_GRAYSCALE)\n",
    "        plt.imshow(img_array, cmap= 'gray')\n",
    "        plt.show()\n",
    "        break\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f5f0703",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(375, 500)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e3da961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "IMG_SIZE = 50\n",
    "\n",
    "new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
    "plt.imshow(new_array, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b82e6dc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 12501/12501 [04:35<00:00, 45.43it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████| 12501/12501 [04:46<00:00, 43.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24946\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "training_data = []\n",
    "\n",
    "DATADIR=''\n",
    "def create_training_data():\n",
    "    for category in categories:  # do dogs and cats\n",
    "\n",
    "        path = os.path.join(DATADIR,category)  # create path to dogs and cats\n",
    "        class_num = categories.index(category)  # get the classification  (0 or a 1). 0=dog 1=cat\n",
    "\n",
    "        for img in tqdm(os.listdir(path)):  # iterate over each image per dogs and cats\n",
    "            try:\n",
    "                img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE)  # convert to array\n",
    "                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))  # resize to normalize data size\n",
    "                training_data.append([new_array, class_num])  # add this to our training_data\n",
    "            except Exception as e:  # in the interest in keeping the output clean...\n",
    "                pass\n",
    "            #except OSError as e:\n",
    "            #    print(\"OSErrroBad img most likely\", e, os.path.join(path,img))\n",
    "            #except Exception as e:\n",
    "            #    print(\"general exception\", e, os.path.join(path,img))\n",
    "\n",
    "create_training_data()\n",
    "\n",
    "print(len(training_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "09e15d29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "random.shuffle(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a36f8d6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test=training_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4844d61f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[248, 248, 248, ...,  45,  38,  28],\n",
       "       [248, 248, 248, ...,  45,  39,  25],\n",
       "       [249, 249, 247, ...,  39,  41,  40],\n",
       "       ...,\n",
       "       [167,  66, 156, ..., 113, 109, 127],\n",
       "       [132, 167, 145, ...,  95, 104, 125],\n",
       "       [134, 138,  84, ...,  92,  82, 111]], dtype=uint8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce87942f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bfecdb0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = []\n",
    "label = []\n",
    "\n",
    "for x,y in training_data:\n",
    "    features.append(x)\n",
    "    label.append(y)\n",
    "\n",
    "#print(features[0].reshape(-1, IMG_SIZE, IMG_SIZE, 1))\n",
    "\n",
    "features = np.array(features).reshape(-1, IMG_SIZE, IMG_SIZE, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "23cb13e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#testing if we labele images right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35b2f443",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7a155c5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dog\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_number = 12547   #less than 24946, i.e total number of images\n",
    "new_array = cv2.resize(features[random_number], (IMG_SIZE, IMG_SIZE))\n",
    "plt.imshow(new_array, cmap='gray')\n",
    "print(what[label[random_number]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e2ea028",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fe01ad6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#building training and testing data set\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test= train_test_split(features, label, train_size= 0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07f5ee0f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6a66958e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "clfr= SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e513920d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0].ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "097c9e61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07eb0bae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0bd8b272",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.datasets import cifar10\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "64d4aed6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(256, (3, 3), input_shape=features.shape[1:]))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(256, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors\n",
    "\n",
    "model.add(Dense(64))\n",
    "\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "90aae9ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train= np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f73677fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      " 25/355 [=>............................] - ETA: 45:04 - loss: 0.7123 - accuracy: 0.5038"
     ]
    },
    {
     "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<ipython-input-37-98985c1d4ae8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m  \u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.3\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\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    106\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    107\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 108\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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    109\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m     \u001b[1;31m# Running inside `run_distribute_coordinator` already.\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\\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   1096\u001b[0m                 batch_size=batch_size):\n\u001b[0;32m   1097\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-> 1098\u001b[1;33m               \u001b[0mtmp_logs\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   1099\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   1100\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\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    778\u001b[0m       \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m         \u001b[0mcompiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"nonXla\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 780\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    781\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    782\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_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\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    805\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    806\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[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 807\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_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    808\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_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    809\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[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2827\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2828\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\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[1;32m-> 2829\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2830\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2831\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mproperty\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\\function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[1;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1841\u001b[0m       \u001b[0;31m`\u001b[0m\u001b[0margs\u001b[0m\u001b[0;31m`\u001b[0m \u001b[1;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \"\"\"\n\u001b[1;32m-> 1843\u001b[1;33m     return self._call_flat(\n\u001b[0m\u001b[0;32m   1844\u001b[0m         [t for t in nest.flatten((args, kwargs), expand_composites=True)\n\u001b[0;32m   1845\u001b[0m          if isinstance(t, (ops.Tensor,\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\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   1921\u001b[0m         and executing_eagerly):\n\u001b[0;32m   1922\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[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1923\u001b[1;33m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[0;32m   1924\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0;32m   1925\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    543\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    544\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--> 545\u001b[1;33m           outputs = execute.execute(\n\u001b[0m\u001b[0;32m    546\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    547\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     57\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     58\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---> 59\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     61\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": [
    "model.fit(x_train, y_train  , batch_size=32, epochs=3, validation_split=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7fa5229",
   "metadata": {},
   "outputs": [],
   "source": []
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