binaryclassification.py

from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator

classifier = Sequential()

classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))

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

classifier.add(Flatten())

classifier.add(Dense(units = 128, activation = 'relu'))

classifier.add(Dense(units = 1, activation = 'sigmoid'))

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

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

classifier.fit_generator(training_set, steps_per_epoch = 100, epochs = 1, validation_data = test_set, validation_steps = 20)
with open('model\\model_architecture.json', 'w') as f:
    f.write(classifier.to_json())
classifier.save_weights("model\\model.h5")