model.py

import os
import numpy as np
from keras.models import Sequential
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras import optimizers
from keras.preprocessing import image

img_width, img_height = 250, 250

train_data_dir = 'Data/Train'
validation_data_dir = 'Data/Validation'

train_datagen_augmented = ImageDataGenerator(
        rescale=1./255,        # normalize pixel values to [0,1]
        shear_range=0.2,       # randomly applies shearing transformation
        zoom_range=0.2,        # randomly applies shearing transformation
        horizontal_flip=True)  # randomly flip the images

# same code as before
train_generator_augmented = train_datagen_augmented.flow_from_directory(
        train_data_dir,
        target_size=(img_width, img_height),
        batch_size=32,
        class_mode='binary')

validation_generator = train_datagen_augmented.flow_from_directory(
        validation_data_dir,
        target_size=(img_width, img_height),
        batch_size=32,
        class_mode='binary')

model = Sequential()
model.add(Convolution2D(32, 3, 3, input_shape=(img_width, img_height,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

nb_epoch = 5
nb_validation_samples = 31

model.fit_generator(
        train_generator_augmented,
        nb_epoch=nb_epoch,
        steps_per_epoch=1000,
        validation_data=validation_generator,
        validation_steps=nb_validation_samples)

model.save_weights('modi-face-model.h5')

print(model.evaluate_generator(validation_generator, nb_validation_samples))