AV_Fashion_Apparel_Detection_using_Keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np #algebra\n",
    "import pandas as pd #data processing, csv I/O\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "import keras\n",
    "from keras.utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train=pd.read_csv(\"C:\\\\Users\\\\ASUS\\\\Documents\\\\Python Scripts\\\\Keras\\\\AV\\\\Keras_Projects-master\\\\train_converted.csv\")\n",
    "label_train=pd.read_csv(\"C:\\\\Users\\\\ASUS\\\\Documents\\\\Python Scripts\\\\Keras\\\\AV\\\\Keras_Projects-master\\\\train.csv\")\n",
    "data_test=pd.read_csv(\"C:\\\\Users\\\\ASUS\\\\Documents\\\\Python Scripts\\\\Keras\\\\AV\\\\Keras_Projects-master\\\\test_converted.csv\")\n",
    "test_ID=pd.read_csv(\"C:\\\\Users\\\\ASUS\\\\Documents\\\\Python Scripts\\\\Keras\\\\AV\\\\Keras_Projects-master\\\\test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.head()\n",
    "label_train.head()\n",
    "data_test.head()\n",
    "test_ID.head()\n",
    "label_train.drop(\"id\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "im_rows=28\n",
    "im_cols=28\n",
    "input_shape=(im_rows, im_cols, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=np.array(data_train)\n",
    "y=to_categorical(np.array(label_train))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the training data into train and validation data\n",
    "X_train, y_train,= X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Test data\n",
    "X_test=np.array(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshape train, test and validation data\n",
    "X_train=X_train.reshape(X_train.shape[0], im_rows, im_cols, 1)\n",
    "X_test=X_test.reshape(X_test.shape[0], im_rows, im_cols, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=X_train.astype('float32')\n",
    "X_test=X_test.astype('float32')\n",
    "\n",
    "X_train/=255\n",
    "X_test/=255\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Building CNN model\n",
    "img_rows,img_cols=28,28\n",
    "\n",
    "batch_size=1000\n",
    "num_classes=10\n",
    "epochs=100\n",
    "\n",
    "\n",
    "model=Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3,3), activation='relu', kernel_initializer='he_normal', input_shape=input_shape))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Conv2D(64, (3,3), activation='relu'))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "model.add(Dropout(2,2))\n",
    "model.add(Conv2D(128,(3,3), activation='relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(), metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_4 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 3, 3, 128)         73856     \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 3, 3, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 1152)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               147584    \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 241,546\n",
      "Trainable params: 241,546\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Check the model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    }
   ],
   "source": [
    "#Training the model\n",
    "history=model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 2, 1, ..., 8, 1, 5], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get Predictions for the test data\n",
    "predict_classes=model.predict_classes(X_test)\n",
    "predict_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write predicted class to csv\n",
    "np.savetxt(\"C:\\\\Users\\\\ASUS\\\\Documents\\\\Python Scripts\\\\Keras\\\\AV\\\\Keras_Projects-master\\\\op2_noval.csv\", predict_classes, delimiter=\",\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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