024CNN.py

# Part 1

# Building the CNN

from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator

# Initialising the CNN

classifier = Sequential()

# Step 1 - Convolution

classifier.add(Convolution2D(32, 3, 3, input_shape=(64, 64, 3), activation='relu'))

# Step 2 - Pooling

classifier.add(MaxPooling2D(pool_size=(2,2)))

# Another Covolutional Layer

classifier.add(Convolution2D(32, 3, 3, activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2,2)))

# Step 3 - Flattening

classifier.add(Flatten())

# Step 4 - Full Connection

classifier.add(Dense(output_dim=128, activation='relu'))
classifier.add(Dense(output_dim=1, activation='sigmoid'))

# Compiling the CNN

classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Part 2

# Fitting the CNN to the images

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, samples_per_epoch=8000, nb_epoch=25, validation_data=test_set, nb_val_samples=2000)