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

# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (128, 128, 1), activation = 'relu'))

# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Adding a second convolutional layer
classifier.add(Conv2D(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(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))

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

# Part 2 - Fitting the CNN to the images

from keras.preprocessing.image import ImageDataGenerator

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

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('./dataset/Training/',
                                                 target_size = (128, 128),
                                                 color_mode = "grayscale",
                                                 batch_size = 16,
                                                 class_mode = 'binary')

val_set = test_datagen.flow_from_directory('./dataset/Validation/',
                                            target_size = (128, 128),
                                            color_mode = "grayscale",
                                            batch_size = 16,
                                            class_mode = 'binary')

classifier.fit_generator(training_set,
                         steps_per_epoch = 300,
                         epochs = 1,
                         validation_data = val_set,
                         validation_steps = 2000)

classifier.save('my_model.h5')
print("Saved model to disk")


