Commit 54014db

from keras.preprocessing.image import ImageDataGenerator

from keras.models import Sequential

from keras.layers import Conv2D, MaxPooling2D

from keras.layers import Activation, Dropout, Flatten, Dense

from keras import backend as K

img_width, img_height = 224, 224

train_data_dir = 'v_data/train'

validation_data_dir = 'v_data/test'

nb_train_samples = 400

nb_validation_samples = 100

epochs = 10

batch_size = 16

if K.image_data_format() == 'channels_first':

input_shape = (3, img_width, img_height)

else:

input_shape = (img_width, img_height, 3)

model = Sequential()

model.add(Conv2D(32, (2, 2), input_shape = input_shape))

model.add(Activation('relu'))

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

model.add(Conv2D(32, (2, 2)))

model.add(Activation('relu'))

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

model.add(Conv2D(64, (2, 2)))

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'])

train_datagen = ImageDataGenerator(

rescale = 1. / 255,

shear_range = 0.2,

zoom_range = 0.2,

horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1. / 255)

train_generator = train_datagen.flow_from_directory(train_data_dir,

target_size =(img_width, img_height),

batch_size = batch_size, class_mode ='binary')

validation_generator = test_datagen.flow_from_directory(

validation_data_dir,

target_size =(img_width, img_height),

batch_size = batch_size, class_mode ='binary')

model.fit_generator(train_generator,

steps_per_epoch = nb_train_samples // batch_size,

epochs = epochs, validation_data = validation_generator,

validation_steps = nb_validation_samples // batch_size)

model.save_weights('model_saved.h5')