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from __future__ import print_function
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img

nb_classes = 7

def cnn():
    model = Sequential()

    model.add(Convolution2D(32, 3, 3, border_mode='same',
                            input_shape=(32, 32, 3)))
    model.add(Activation('relu'))
    model.add(Convolution2D(32, 3, 3))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Convolution2D(64, 3, 3, border_mode='same'))
    model.add(Activation('relu'))
    model.add(Convolution2D(64, 3, 3))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))

    return model

batch_size = 16
nb_classes = 7
nb_epoch = 50
data_augmentation = True

# input image dimensions
img_x, img_y = 32, 32
# images are RGB.
img_channels = 3

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

# input dataset
train_datagen = ImageDataGenerator(
        rescale=1./255,
        rotation_range=30,
        zoom_range=0.1,
        horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
        'train',
        target_size=(img_x, img_y),
        batch_size=batch_size,
        class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
        'validation',
        target_size=(img_x, img_y),
        batch_size=batch_size,
        class_mode='categorical')

model.fit_generator(
        train_generator,
        samples_per_epoch=800,
        nb_epoch=nb_epoch,
        validation_data=validation_generator,
        nb_val_samples=90,
        verbose=1)

model.save_weights('sanoba_cnn.hdf5')