maleria2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e508596a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Test', 'Train']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "import os\n",
    "print(os.listdir(\"C:\\\\Users\\\\Anushka\\\\Desktop\\\\DMWminiproject\\\\Dataset\\\\Dataset\"))\n",
    "from glob import glob\n",
    "from PIL import Image\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import fnmatch\n",
    "import keras\n",
    "from time import sleep\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Conv2D,MaxPool2D,Dropout,Flatten,BatchNormalization,MaxPooling2D,Activation\n",
    "from tensorflow.keras.optimizers import RMSprop,Adam\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from keras import backend as k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c878ab3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196\n",
      "220\n"
     ]
    }
   ],
   "source": [
    "imagePatches_0 = glob('C:\\\\Users\\\\Anushka\\\\Desktop\\\\DMWminiproject\\\\Dataset\\\\Dataset\\\\Train\\\\Uninfected\\\\*.png', recursive=True)\n",
    "imagePatches_1 = glob('C:\\\\Users\\\\Anushka\\\\Desktop\\\\DMWminiproject\\\\Dataset\\\\Dataset\\\\Train\\\\Parasite\\\\*.png', recursive=True)\n",
    "print(len(imagePatches_0))\n",
    "print(len(imagePatches_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9d9b2fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=[]\n",
    "y=[]\n",
    "for img in imagePatches_0:\n",
    "    full_size_image = cv2.imread(img)\n",
    "    im = cv2.resize(full_size_image, (224, 224), interpolation=cv2.INTER_CUBIC)\n",
    "    x.append(im)\n",
    "    y.append(0)\n",
    "for img in imagePatches_1:\n",
    "    full_size_image = cv2.imread(img)\n",
    "    im = cv2.resize(full_size_image, (224, 224), interpolation=cv2.INTER_CUBIC)\n",
    "    x.append(im)\n",
    "    y.append(1)\n",
    "x = np.array(x)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a40b9ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(x, y, test_size = 0.2, random_state = 101)\n",
    "y_train = to_categorical(y_train, num_classes = 2)\n",
    "y_valid = to_categorical(y_valid, num_classes = 2)\n",
    "del x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8c5574b2",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'BatchNormalization' from 'keras.layers.normalization' (C:\\Users\\Anushka\\anaconda3\\lib\\site-packages\\keras\\layers\\normalization\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-c9e5060150ab>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mInput\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mModel\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mConv2D\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMaxPooling2D\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMaxPooling1D\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mGlobalAveragePooling2D\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDense\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDropout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFlatten\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mInput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mLSTM\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTimeDistributed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalization\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mBatchNormalization\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madvanced_activations\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLeakyReLU\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSequential\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;31mImportError\u001b[0m: cannot import name 'BatchNormalization' from 'keras.layers.normalization' (C:\\Users\\Anushka\\anaconda3\\lib\\site-packages\\keras\\layers\\normalization\\__init__.py)"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential,Input,Model\n",
    "from keras.layers import Conv2D, MaxPooling2D, MaxPooling1D, GlobalAveragePooling2D, Dense, Dropout, Flatten, Input, LSTM, TimeDistributed\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.advanced_activations import LeakyReLU \n",
    "model = Sequential()\n",
    "model.add(Conv2D(32,(7,7),activation='relu'))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.15))\n",
    "model.add(Conv2D(64,(5,5),activation='relu'))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.15))\n",
    "model.add(Conv2D(128,(3,3),activation='relu'))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.15))\n",
    "model.add(Conv2D(128,(3,3),activation='relu'))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.15))\n",
    "model.add(GlobalAveragePooling2D())\n",
    "model.add(Dense(1000, activation='relu'))\n",
    "model.add(Dense(2,activation='softmax'))\n",
    "model.compile(optimizer='adam', loss = 'categorical_crossentropy', metrics=['accuracy'])\n",
    "#model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cc82629d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-9784a512750c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcallbacks\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mModelCheckpoint\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmcp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mModelCheckpoint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'model_check_path.hdf5'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmonitor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"val_acc\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msave_best_only\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msave_weights_only\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mhist\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[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;36m50\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[0mvalidation_split\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmcp\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;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "mcp = ModelCheckpoint(filepath='model_check_path.hdf5',monitor=\"val_acc\", save_best_only=True, save_weights_only=False)\n",
    "hist = model.fit(x_train,y_train,batch_size = 32, epochs = 50, verbose=1,  validation_split=0.2, callbacks=[mcp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f650d1c9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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