Cat and dog Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
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    }
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd  \n",
    "import os \n",
    "from pathlib import Path \n",
    "import glob\n",
    "import zipfile \n",
    "import random \n",
    "\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import tensorflow as tf  \n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import Model  \n",
    "import keras_preprocessing\n",
    "from keras_preprocessing import image   \n",
    "from tensorflow.keras.optimizers import RMSprop\n",
    "from keras_preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T04:41:27.348204Z",
     "iopub.status.busy": "2021-08-01T04:41:27.347861Z",
     "iopub.status.idle": "2021-08-01T04:41:28.039766Z",
     "shell.execute_reply": "2021-08-01T04:41:28.038437Z",
     "shell.execute_reply.started": "2021-08-01T04:41:27.348169Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>../input/cat-and-dog/training_set/training_set...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>../input/cat-and-dog/training_set/training_set...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>../input/cat-and-dog/training_set/training_set...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>../input/cat-and-dog/training_set/training_set...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>../input/cat-and-dog/training_set/training_set...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                          image_path  label\n",
       "0  ../input/cat-and-dog/training_set/training_set...      0\n",
       "1  ../input/cat-and-dog/training_set/training_set...      1\n",
       "2  ../input/cat-and-dog/training_set/training_set...      1\n",
       "3  ../input/cat-and-dog/training_set/training_set...      0\n",
       "4  ../input/cat-and-dog/training_set/training_set...      1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = Path('../input/cat-and-dog') \n",
    "\n",
    "train_dir = data_dir / \"training_set/training_set\" \n",
    "test_dir = data_dir / \"test_set/test_set\"\n",
    "\n",
    "cat_samples_dir_train = train_dir / \"cats\" \n",
    "dog_samples_dir_train = train_dir / \"dogs\" \n",
    "\n",
    "def make_csv_with_image_labels(CATS_PATH, DOGS_PATH):  \n",
    "    '''\n",
    "    Function for making a dataframe that contains images path as well as their labels.\n",
    "    Parameters:- \n",
    "     - CATS_PATH - Path for Cats  Images  \n",
    "     - DOGS_PATH - Path for Dogs Images \n",
    "    Output:- \n",
    "    It simply returns dataframe  \n",
    "    '''\n",
    "    cat_images = CATS_PATH.glob('*.jpg')\n",
    "    dog_images = DOGS_PATH.glob('*.jpg')\n",
    "    \n",
    "    df = [] \n",
    "    for i in cat_images: \n",
    "        df.append((i, 0)) \n",
    "    for j in dog_images: \n",
    "        df.append((i, 1))\n",
    "    \n",
    "    df = pd.DataFrame(df, columns=[\"image_path\", \"label\"], index = None) \n",
    "    df = df.sample(frac = 1).reset_index(drop=True) \n",
    "    \n",
    "    return df  \n",
    "\n",
    "train_csv = make_csv_with_image_labels(cat_samples_dir_train,dog_samples_dir_train) \n",
    "train_csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T04:41:28.047018Z",
     "iopub.status.busy": "2021-08-01T04:41:28.044526Z",
     "iopub.status.idle": "2021-08-01T04:41:28.250111Z",
     "shell.execute_reply": "2021-08-01T04:41:28.249147Z",
     "shell.execute_reply.started": "2021-08-01T04:41:28.046959Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total No. Of CAT Samples :-  4000\n",
      "Total No. Of DOG Samples :-  4005\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len_cat = len(train_csv[\"label\"][train_csv.label == 0])\n",
    "len_dog = len(train_csv[\"label\"][train_csv.label == 1])\n",
    "\n",
    "arr = np.array([len_cat , len_dog]) \n",
    "labels = ['CAT', 'DOG'] \n",
    "print(\"Total No. Of CAT Samples :- \", len_cat)\n",
    "print(\"Total No. Of DOG Samples :- \", len_dog)\n",
    "\n",
    "plt.pie(arr, labels=labels, explode = [0.2,0.0] , shadow=True) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T04:41:28.256423Z",
     "iopub.status.busy": "2021-08-01T04:41:28.254254Z",
     "iopub.status.idle": "2021-08-01T04:41:35.418083Z",
     "shell.execute_reply": "2021-08-01T04:41:35.416690Z",
     "shell.execute_reply.started": "2021-08-01T04:41:28.256382Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 8005 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "def get_train_generator(train_dir, batch_size=64, target_size=(224, 224)):  \n",
    "    ''' \n",
    "    Function for preparing training data\n",
    "    '''\n",
    "    train_datagen = ImageDataGenerator(rescale = 1./255., # normalizing the image \n",
    "                                   rotation_range = 40,\n",
    "                                   width_shift_range = 0.2,\n",
    "                                   height_shift_range = 0.2,\n",
    "                                   shear_range = 0.2,\n",
    "                                   zoom_range = 0.2,\n",
    "                                   horizontal_flip = True) \n",
    "    \n",
    "    train_generator = train_datagen.flow_from_directory(train_dir, \n",
    "                                                    batch_size = batch_size, \n",
    "                                                    color_mode='rgb',\n",
    "                                                    class_mode = 'binary', \n",
    "                                                    target_size = target_size)   \n",
    "    return train_generator  \n",
    "\n",
    "train_generator = get_train_generator(train_dir) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T04:41:35.419906Z",
     "iopub.status.busy": "2021-08-01T04:41:35.419561Z",
     "iopub.status.idle": "2021-08-01T04:41:35.735604Z",
     "shell.execute_reply": "2021-08-01T04:41:35.734742Z",
     "shell.execute_reply.started": "2021-08-01T04:41:35.419851Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2023 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "def get_testgenerator(test_dir,batch_size=64, target_size=(224,224)): \n",
    "    ''' \n",
    "    Function for preparing testing data\n",
    "    '''\n",
    "    test_datagen = ImageDataGenerator( rescale = 1.0/255. )\n",
    "    test_generator  =  test_datagen.flow_from_directory(test_dir, \n",
    "                                                          batch_size  = batch_size, \n",
    "                                                          color_mode='rgb',\n",
    "                                                          class_mode  = 'binary', \n",
    "                                                          target_size = target_size) \n",
    "    return test_generator\n",
    "\n",
    "test_generator = get_testgenerator(test_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T04:41:35.737284Z",
     "iopub.status.busy": "2021-08-01T04:41:35.736831Z",
     "iopub.status.idle": "2021-08-01T04:41:37.762872Z",
     "shell.execute_reply": "2021-08-01T04:41:37.762043Z",
     "shell.execute_reply.started": "2021-08-01T04:41:35.737248Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 112, 112, 64)      1792      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 56, 56, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 28, 28, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 7, 7, 256)         295168    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 3, 3, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 2304)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 158)               364190    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               40704     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 808,735\n",
      "Trainable params: 808,735\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# building the model \n",
    "model = tf.keras.Sequential([ \n",
    "    layers.Conv2D(64, (3,3), strides=(2,2),padding='same',input_shape= (224,224,3),activation = 'relu'),\n",
    "    layers.MaxPool2D(2,2), \n",
    "    layers.Conv2D(128, (3,3), strides=(2,2),padding='same',activation = 'relu'),\n",
    "    layers.MaxPool2D(2,2), \n",
    "    layers.Conv2D(256, (3,3), strides=(2,2),padding='same',activation = 'relu'), \n",
    "    layers.MaxPool2D(2,2),  \n",
    "    layers.Flatten(), \n",
    "    layers.Dense(158, activation ='relu'), \n",
    "    layers.Dense(256, activation = 'relu'), \n",
    "    layers.Dense(128, activation = 'relu'), \n",
    "    layers.Dense(1, activation = 'sigmoid'), \n",
    "]) \n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T04:42:28.540689Z",
     "iopub.status.busy": "2021-08-01T04:42:28.540346Z",
     "iopub.status.idle": "2021-08-01T05:13:01.340706Z",
     "shell.execute_reply": "2021-08-01T05:13:01.339654Z",
     "shell.execute_reply.started": "2021-08-01T04:42:28.540657Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "warnings ignored\n",
      "Epoch 1/15\n",
      "126/126 [==============================] - 117s 927ms/step - loss: 0.6851 - acc: 0.5718 - val_loss: 0.6760 - val_acc: 0.5378\n",
      "Epoch 3/15\n",
      "126/126 [==============================] - 117s 929ms/step - loss: 0.6533 - acc: 0.6313 - val_loss: 0.6210 - val_acc: 0.6624\n",
      "Epoch 4/15\n",
      "126/126 [==============================] - 118s 940ms/step - loss: 0.6212 - acc: 0.6506 - val_loss: 0.5557 - val_acc: 0.7123\n",
      "Epoch 5/15\n",
      "126/126 [==============================] - 118s 940ms/step - loss: 0.6128 - acc: 0.6592 - val_loss: 0.5382 - val_acc: 0.7341\n",
      "Epoch 6/15\n",
      "126/126 [==============================] - 124s 983ms/step - loss: 0.5853 - acc: 0.6998 - val_loss: 0.4984 - val_acc: 0.7647\n",
      "Epoch 7/15\n",
      "126/126 [==============================] - 124s 984ms/step - loss: 0.5730 - acc: 0.7027 - val_loss: 0.5394 - val_acc: 0.7202\n",
      "Epoch 8/15\n",
      "126/126 [==============================] - 117s 931ms/step - loss: 0.5627 - acc: 0.7048 - val_loss: 0.5158 - val_acc: 0.7444\n",
      "Epoch 9/15\n",
      "126/126 [==============================] - 116s 922ms/step - loss: 0.5628 - acc: 0.7179 - val_loss: 0.4652 - val_acc: 0.7721\n",
      "Epoch 10/15\n",
      "126/126 [==============================] - 119s 947ms/step - loss: 0.5381 - acc: 0.7495 - val_loss: 0.4548 - val_acc: 0.7904\n",
      "Epoch 11/15\n",
      "126/126 [==============================] - 118s 933ms/step - loss: 0.5238 - acc: 0.7433 - val_loss: 0.4686 - val_acc: 0.7889\n",
      "Epoch 12/15\n",
      "126/126 [==============================] - 120s 949ms/step - loss: 0.5149 - acc: 0.7508 - val_loss: 0.4618 - val_acc: 0.7598\n",
      "Epoch 13/15\n",
      "126/126 [==============================] - 116s 919ms/step - loss: 0.5215 - acc: 0.7429 - val_loss: 0.4499 - val_acc: 0.7968\n",
      "Epoch 14/15\n",
      "126/126 [==============================] - 118s 936ms/step - loss: 0.4905 - acc: 0.7663 - val_loss: 0.4299 - val_acc: 0.8082\n",
      "Epoch 15/15\n",
      "126/126 [==============================] - 121s 959ms/step - loss: 0.5031 - acc: 0.7558 - val_loss: 0.3939 - val_acc: 0.8309\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=RMSprop(lr=0.001), loss='binary_crossentropy', metrics=['acc'])\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "print('warnings ignored')\n",
    "\n",
    "history = model.fit_generator(train_generator,\n",
    "                              epochs=15,\n",
    "                              verbose=1,\n",
    "                              validation_data=test_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T05:24:12.721699Z",
     "iopub.status.busy": "2021-08-01T05:24:12.721276Z",
     "iopub.status.idle": "2021-08-01T05:24:12.975405Z",
     "shell.execute_reply": "2021-08-01T05:24:12.974427Z",
     "shell.execute_reply.started": "2021-08-01T05:24:12.721666Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training and validation loss')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "import matplotlib.image  as mpimg\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc=history.history['acc']\n",
    "val_acc=history.history['val_acc']\n",
    "loss=history.history['loss']\n",
    "val_loss=history.history['val_loss']\n",
    "\n",
    "epochs=range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'r', \"Training Accuracy\")\n",
    "plt.plot(epochs, val_acc, 'b', \"Validation Accuracy\")\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'r', \"Training Loss\")\n",
    "plt.plot(epochs, val_loss, 'b', \"Validation Loss\")\n",
    "\n",
    "plt.title('Training and validation loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T05:30:05.800079Z",
     "iopub.status.busy": "2021-08-01T05:30:05.799705Z",
     "iopub.status.idle": "2021-08-01T05:30:05.850045Z",
     "shell.execute_reply": "2021-08-01T05:30:05.849186Z",
     "shell.execute_reply.started": "2021-08-01T05:30:05.800046Z"
    }
   },
   "outputs": [],
   "source": [
    "model.save('my_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T05:30:40.629658Z",
     "iopub.status.busy": "2021-08-01T05:30:40.629299Z",
     "iopub.status.idle": "2021-08-01T05:30:40.755524Z",
     "shell.execute_reply": "2021-08-01T05:30:40.754772Z",
     "shell.execute_reply.started": "2021-08-01T05:30:40.629627Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 112, 112, 64)      1792      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 56, 56, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 28, 28, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 7, 7, 256)         295168    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 3, 3, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 2304)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 158)               364190    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               40704     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 808,735\n",
      "Trainable params: 808,735\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.save('my_model.h5')\n",
    "new_model = tf.keras.models.load_model('./my_model.h5')\n",
    "\n",
    "# Show the model architecture\n",
    "new_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}