15_cnn.py

# -*- coding: utf-8 -*-
"""
Created on Sat Jan 26 17:30:46 2019

@author: hp
"""

from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, BatchNormalization

classifier = Sequential()
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(BatchNormalization())
classifier.add(Dropout(0.2))
classifier.add(MaxPool2D(pool_size = (2, 2)))
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(BatchNormalization())
classifier.add(Dropout(0.2))
classifier.add(MaxPool2D(pool_size = (2, 2)))
classifier.add(Conv2D(64, (3, 3), activation = 'relu'))
classifier.add(BatchNormalization())
classifier.add(Dropout(0.2))
classifier.add(MaxPool2D(pool_size = (2, 2)))
classifier.add(Conv2D(64, (3, 3), activation = 'relu'))
classifier.add(BatchNormalization())
classifier.add(Dropout(0.2))
classifier.add(MaxPool2D(pool_size = (2, 2)))
classifier.add(Flatten())
classifier.add(Dense(units = 64, activation = 'relu'))
classifier.add(BatchNormalization())
classifier.add(Dense(units = 1, activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(
        rescale = 1./255, 
        shear_range = 0.2,
        zoom_range = 0.2,
        horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('dataset/test_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

classifier.fit_generator(training_set,
                         steps_per_epoch = 8000/32,
                         epochs = 5,
                         validation_data = test_set,
                         validation_steps = 2000/32)