Dog-Cat-CNN-Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "304dc328",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "import pandas,numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe07ffa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model initialization\n",
    "model= Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8d567f06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'sequential', 'layers': []}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Initially we have no layers\n",
    "model.get_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4cbd171c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Convolution2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f59e1e5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(\n",
    "    Convolution2D(\n",
    "        filters=32, # 32 different filters are taken\n",
    "        kernel_size=(3,3), #filter size/dimension smaller ==> more computing\n",
    "        input_shape=(64,64,3), # 64 X 64 RGB Image\n",
    "        activation=\"relu\"    #Mostly Activation fn is \"relu\" for inner layers\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4644ee23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'sequential',\n",
       " 'layers': [{'class_name': 'InputLayer',\n",
       "   'config': {'batch_input_shape': (None, 64, 64, 3),\n",
       "    'dtype': 'float32',\n",
       "    'sparse': False,\n",
       "    'ragged': False,\n",
       "    'name': 'conv2d_input'}},\n",
       "  {'class_name': 'Conv2D',\n",
       "   'config': {'name': 'conv2d',\n",
       "    'trainable': True,\n",
       "    'batch_input_shape': (None, 64, 64, 3),\n",
       "    'dtype': 'float32',\n",
       "    'filters': 32,\n",
       "    'kernel_size': (3, 3),\n",
       "    'strides': (1, 1),\n",
       "    'padding': 'valid',\n",
       "    'data_format': 'channels_last',\n",
       "    'dilation_rate': (1, 1),\n",
       "    'groups': 1,\n",
       "    'activation': 'relu',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "     'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}}]}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5dbcab96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 62, 62, 32)        896       \n",
      "=================================================================\n",
      "Total params: 896\n",
      "Trainable params: 896\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d16257bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import MaxPooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2348bc77",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(\n",
    "    MaxPooling2D(\n",
    "    pool_size=(2,2)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b43ad7d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'sequential',\n",
       " 'layers': [{'class_name': 'InputLayer',\n",
       "   'config': {'batch_input_shape': (None, 64, 64, 3),\n",
       "    'dtype': 'float32',\n",
       "    'sparse': False,\n",
       "    'ragged': False,\n",
       "    'name': 'conv2d_input'}},\n",
       "  {'class_name': 'Conv2D',\n",
       "   'config': {'name': 'conv2d',\n",
       "    'trainable': True,\n",
       "    'batch_input_shape': (None, 64, 64, 3),\n",
       "    'dtype': 'float32',\n",
       "    'filters': 32,\n",
       "    'kernel_size': (3, 3),\n",
       "    'strides': (1, 1),\n",
       "    'padding': 'valid',\n",
       "    'data_format': 'channels_last',\n",
       "    'dilation_rate': (1, 1),\n",
       "    'groups': 1,\n",
       "    'activation': 'relu',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "     'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}},\n",
       "  {'class_name': 'MaxPooling2D',\n",
       "   'config': {'name': 'max_pooling2d',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'pool_size': (2, 2),\n",
       "    'padding': 'valid',\n",
       "    'strides': (2, 2),\n",
       "    'data_format': 'channels_last'}}]}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0ddf82d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 62, 62, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 31, 31, 32)        0         \n",
      "=================================================================\n",
      "Total params: 896\n",
      "Trainable params: 896\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c4b825e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "933246e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8b5bae98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'sequential',\n",
       " 'layers': [{'class_name': 'InputLayer',\n",
       "   'config': {'batch_input_shape': (None, 64, 64, 3),\n",
       "    'dtype': 'float32',\n",
       "    'sparse': False,\n",
       "    'ragged': False,\n",
       "    'name': 'conv2d_input'}},\n",
       "  {'class_name': 'Conv2D',\n",
       "   'config': {'name': 'conv2d',\n",
       "    'trainable': True,\n",
       "    'batch_input_shape': (None, 64, 64, 3),\n",
       "    'dtype': 'float32',\n",
       "    'filters': 32,\n",
       "    'kernel_size': (3, 3),\n",
       "    'strides': (1, 1),\n",
       "    'padding': 'valid',\n",
       "    'data_format': 'channels_last',\n",
       "    'dilation_rate': (1, 1),\n",
       "    'groups': 1,\n",
       "    'activation': 'relu',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "     'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}},\n",
       "  {'class_name': 'MaxPooling2D',\n",
       "   'config': {'name': 'max_pooling2d',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'pool_size': (2, 2),\n",
       "    'padding': 'valid',\n",
       "    'strides': (2, 2),\n",
       "    'data_format': 'channels_last'}},\n",
       "  {'class_name': 'Flatten',\n",
       "   'config': {'name': 'flatten',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'data_format': 'channels_last'}}]}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fec4c492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 62, 62, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 30752)             0         \n",
      "=================================================================\n",
      "Total params: 896\n",
      "Trainable params: 896\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ca532bbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "36b194bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(\n",
    "    Dense(\n",
    "        units=128,\n",
    "        activation=\"relu\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "285cd4d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'sequential',\n",
       " 'layers': [{'class_name': 'InputLayer',\n",
       "   'config': {'batch_input_shape': (None, 64, 64, 3),\n",
       "    'dtype': 'float32',\n",
       "    'sparse': False,\n",
       "    'ragged': False,\n",
       "    'name': 'conv2d_input'}},\n",
       "  {'class_name': 'Conv2D',\n",
       "   'config': {'name': 'conv2d',\n",
       "    'trainable': True,\n",
       "    'batch_input_shape': (None, 64, 64, 3),\n",
       "    'dtype': 'float32',\n",
       "    'filters': 32,\n",
       "    'kernel_size': (3, 3),\n",
       "    'strides': (1, 1),\n",
       "    'padding': 'valid',\n",
       "    'data_format': 'channels_last',\n",
       "    'dilation_rate': (1, 1),\n",
       "    'groups': 1,\n",
       "    'activation': 'relu',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "     'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}},\n",
       "  {'class_name': 'MaxPooling2D',\n",
       "   'config': {'name': 'max_pooling2d',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'pool_size': (2, 2),\n",
       "    'padding': 'valid',\n",
       "    'strides': (2, 2),\n",
       "    'data_format': 'channels_last'}},\n",
       "  {'class_name': 'Flatten',\n",
       "   'config': {'name': 'flatten',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'data_format': 'channels_last'}},\n",
       "  {'class_name': 'Dense',\n",
       "   'config': {'name': 'dense',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'units': 128,\n",
       "    'activation': 'relu',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "     'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}}]}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f86c9619",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 62, 62, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 30752)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               3936384   \n",
      "=================================================================\n",
      "Total params: 3,937,280\n",
      "Trainable params: 3,937,280\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()\n",
    "\n",
    "#Here, Parameter Calculation:\n",
    "# maxpooling layer: 31*31*32=30752 ==> flatten ==> Dense layer\n",
    "# Dense layer = (30752 * 20) + 20 = 615060"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f18e63e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(\n",
    "    Dense(\n",
    "        units=1,\n",
    "        activation=\"sigmoid\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1bcd0b7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'sequential',\n",
       " 'layers': [{'class_name': 'InputLayer',\n",
       "   'config': {'batch_input_shape': (None, 64, 64, 3),\n",
       "    'dtype': 'float32',\n",
       "    'sparse': False,\n",
       "    'ragged': False,\n",
       "    'name': 'conv2d_input'}},\n",
       "  {'class_name': 'Conv2D',\n",
       "   'config': {'name': 'conv2d',\n",
       "    'trainable': True,\n",
       "    'batch_input_shape': (None, 64, 64, 3),\n",
       "    'dtype': 'float32',\n",
       "    'filters': 32,\n",
       "    'kernel_size': (3, 3),\n",
       "    'strides': (1, 1),\n",
       "    'padding': 'valid',\n",
       "    'data_format': 'channels_last',\n",
       "    'dilation_rate': (1, 1),\n",
       "    'groups': 1,\n",
       "    'activation': 'relu',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "     'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}},\n",
       "  {'class_name': 'MaxPooling2D',\n",
       "   'config': {'name': 'max_pooling2d',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'pool_size': (2, 2),\n",
       "    'padding': 'valid',\n",
       "    'strides': (2, 2),\n",
       "    'data_format': 'channels_last'}},\n",
       "  {'class_name': 'Flatten',\n",
       "   'config': {'name': 'flatten',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'data_format': 'channels_last'}},\n",
       "  {'class_name': 'Dense',\n",
       "   'config': {'name': 'dense',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'units': 128,\n",
       "    'activation': 'relu',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "     'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}},\n",
       "  {'class_name': 'Dense',\n",
       "   'config': {'name': 'dense_1',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'units': 1,\n",
       "    'activation': 'sigmoid',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "     'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}}]}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e5db3d37",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3e4d3b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(learning_rate=0.0001),loss='binary_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d9432b8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras_preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6d6ae167",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 8000 images belonging to 2 classes.\n",
      "Found 2000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen=ImageDataGenerator(rescale=1./255,\n",
    "                                 shear_range=0.2,\n",
    "                                 zoom_range=0.2,\n",
    "                                 horizontal_flip=True,\n",
    "                                 vertical_flip=True,\n",
    "                                rotation_range=20)\n",
    "test_datagen=ImageDataGenerator(rescale=1./255)\n",
    "train_generator=train_datagen.flow_from_directory(\n",
    "'/media/usb/Dog-Cat-Dataset/training_set/',\n",
    "    target_size=(64,64),\n",
    "    batch_size=32,\n",
    "    class_mode='binary',\n",
    "    shuffle=True)\n",
    "validation_generator=test_datagen.flow_from_directory(\n",
    "'/media/usb/Dog-Cat-Dataset/test_set/',\n",
    "    target_size=(64,64),\n",
    "    batch_size=32,\n",
    "    class_mode='binary',\n",
    "    shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27c14e23",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "250/250 [==============================] - 76s 297ms/step - loss: 0.6984 - accuracy: 0.5260 - val_loss: 0.6559 - val_accuracy: 0.6135\n",
      "Epoch 2/25\n",
      "250/250 [==============================] - 73s 292ms/step - loss: 0.6636 - accuracy: 0.5921 - val_loss: 0.6436 - val_accuracy: 0.6195\n",
      "Epoch 3/25\n",
      "250/250 [==============================] - 76s 304ms/step - loss: 0.6481 - accuracy: 0.6177 - val_loss: 0.6362 - val_accuracy: 0.6210\n",
      "Epoch 4/25\n",
      "250/250 [==============================] - 81s 321ms/step - loss: 0.6338 - accuracy: 0.6407 - val_loss: 0.6161 - val_accuracy: 0.6620\n",
      "Epoch 5/25\n",
      "250/250 [==============================] - 87s 348ms/step - loss: 0.6219 - accuracy: 0.6507 - val_loss: 0.6490 - val_accuracy: 0.6195\n",
      "Epoch 6/25\n",
      "250/250 [==============================] - 77s 308ms/step - loss: 0.6158 - accuracy: 0.6635 - val_loss: 0.7078 - val_accuracy: 0.5765\n",
      "Epoch 7/25\n",
      "250/250 [==============================] - 70s 281ms/step - loss: 0.6094 - accuracy: 0.6657 - val_loss: 0.6091 - val_accuracy: 0.6685\n",
      "Epoch 8/25\n",
      "250/250 [==============================] - 74s 296ms/step - loss: 0.6071 - accuracy: 0.6730 - val_loss: 0.5899 - val_accuracy: 0.6845\n",
      "Epoch 9/25\n",
      "250/250 [==============================] - 72s 287ms/step - loss: 0.6087 - accuracy: 0.6606 - val_loss: 0.5984 - val_accuracy: 0.6775\n",
      "Epoch 10/25\n",
      "250/250 [==============================] - 101s 405ms/step - loss: 0.6042 - accuracy: 0.6724 - val_loss: 0.6163 - val_accuracy: 0.6660\n",
      "Epoch 11/25\n",
      "250/250 [==============================] - 74s 294ms/step - loss: 0.5842 - accuracy: 0.6912 - val_loss: 0.5983 - val_accuracy: 0.6750\n",
      "Epoch 12/25\n",
      "250/250 [==============================] - 76s 304ms/step - loss: 0.5928 - accuracy: 0.6818 - val_loss: 0.6245 - val_accuracy: 0.6450\n",
      "Epoch 13/25\n",
      " 20/250 [=>............................] - ETA: 1:29 - loss: 0.5730 - accuracy: 0.7284"
     ]
    }
   ],
   "source": [
    "model.fit(train_generator,epochs=25,validation_data=validation_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fb14c37",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"dog-cat-cnn-model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c58e2272",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(pd.DataFrame(model.history.history['accuracy']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89c8b17c",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(pd.DataFrame(model.history.history['loss']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcae5567",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94abc471",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "024662c4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "910ac8a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1592c81f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b86a0a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image=image.load_img(\"/media/usb/Dog-Cat-Dataset/Prediction_Images/index.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59022ce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "type(test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "886731ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "936eb2e0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "m=load_model(\"dog-cat-cnn-model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "561aaf2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.predict(test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5efb279",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image_array = image.img_to_array(test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2cf6c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96cc4d68",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.predict(test_image_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63be4817",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "import cv2\n",
    "test_image_array_resize= cv2.resize(test_image_array,(64,64))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0f7ae6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image_array_resize.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2be5c91e",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.predict(test_image_array_resize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35df7433",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image_array_resize_4d = numpy.expand_dims(test_image_array_resize,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80251df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image_array_resize_4d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfcc0cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.predict(test_image_array_resize_4d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffe42a37",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "cv2.imshow(\"hello\",test_image_array_4d_resize)\n",
    "cv2.waitkey()\n",
    "cv2.dest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9de95f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.predict(test_image_array_4d_resize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00f051aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image_array_resize_4d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b69d573",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_generator.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76cca5cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f24fcf1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "245ee74b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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