model = Sequential()

model.add(Convolution2D(4, 3, 3, input_shape=(img_width, img_height, 1)))

model.add(Activation('relu'))

model.add(Convolution2D(4, 3, 3))

model.add(Activation('relu'))

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

# layer

model.add(Convolution2D(8, 3, 3))

model.add(Activation('relu'))

model.add(Convolution2D(8, 3, 3))

model.add(Activation('relu'))

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

# model.add(Convolution2D(16, 3, 3))

# model.add(Activation('relu'))

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

# layer

model.add(Flatten())

model.add(Dense(64))

model.add(Activation('relu'))

# model.add(Dropout(0.5))

model.add(Dense(16))

model.add(Activation('relu'))

model.add(Dropout(0.6))

model.add(Dense(classNum))

model.add(Activation('softmax'))

# test

model.compile(loss='categorical_crossentropy',

optimizer='adam',

metrics=['accuracy'])

# this is the augmentation configuration we will use for training

train_datagen = ImageDataGenerator(

rescale=1./255,

shear_range=0.2,

zca_whitening=True,

zoom_range=0.2,

horizontal_flip=False)

# this is the augmentation configuration we will use for testing:

# only rescaling

test_datagen = ImageDataGenerator(rescale=1./255, zca_whitening=True)

train_generator = train_datagen.flow_from_directory(

trainDir,

target_size=(img_width, img_height),

batch_size=32,

color_mode='grayscale',

class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(

validationDir,

target_size=(img_width, img_height),

batch_size=32,

color_mode='grayscale',

class_mode='categorical')

model.fit_generator(

train_generator,

samples_per_epoch=nb_train_samples,

nb_epoch=nb_epoch,

validation_data=validation_generator,

nb_val_samples=nb_validation_samples)