classes/model/autoencoder_image.py

__author__ = 'Taneem Jan, improved the old model through pretrained Auto-encoders'

from keras.layers import Input, Dropout, Conv2D, MaxPooling2D, Conv2DTranspose, UpSampling2D
from keras.models import Model
from .Config import *
from .AModel import *


class autoencoder_image(AModel):
    def __init__(self, input_shape, output_size, output_path):
        AModel.__init__(self, input_shape, output_size, output_path)
        self.name = 'autoencoder'

        input_image = Input(shape=input_shape)
        encoder = Conv2D(32, 3, padding='same', activation='relu')(input_image)
        encoder = Conv2D(32, 3, padding='same', activation='relu')(encoder)
        encoder = MaxPooling2D()(encoder)
        encoder = Dropout(0.25)(encoder)

        encoder = Conv2D(64, 3, padding='same', activation='relu')(encoder)
        encoder = Conv2D(64, 3, padding='same', activation='relu')(encoder)
        encoder = MaxPooling2D()(encoder)
        encoder = Dropout(0.25)(encoder)

        encoder = Conv2D(128, 3, padding='same', activation='relu')(encoder)
        encoder = Conv2D(128, 3, padding='same', activation='relu')(encoder)
        encoder = MaxPooling2D()(encoder)
        encoded = Dropout(0.25, name='encoded_layer')(encoder)

        decoder = Conv2DTranspose(128, 3, padding='same', activation='relu')(encoded)
        decoder = Conv2DTranspose(128, 3, padding='same', activation='relu')(decoder)
        decoder = UpSampling2D()(decoder)
        decoder = Dropout(0.25)(decoder)

        decoder = Conv2DTranspose(64, 3, padding='same', activation='relu')(decoder)
        decoder = Conv2DTranspose(64, 3, padding='same', activation='relu')(decoder)
        decoder = UpSampling2D()(decoder)
        decoder = Dropout(0.25)(decoder)

        decoder = Conv2DTranspose(32, 3, padding='same', activation='relu')(decoder)
        decoder = Conv2DTranspose(3, 3, padding='same', activation='relu')(decoder)
        decoder = UpSampling2D()(decoder)
        decoded = Dropout(0.25)(decoder)

        # decoder = Dense(256*256*3)(decoder)
        # decoded = Reshape(target_shape=input_shape)(decoder)

        self.model = Model(input_image, decoded)
        self.model.compile(optimizer='adadelta', loss='binary_crossentropy')

    # self.model.summary()

    def fit_generator(self, generator, steps_per_epoch):
        self.model.fit_generator(generator, steps_per_epoch=steps_per_epoch, epochs=EPOCHS, verbose=1)
        self.save()

    def predict_hidden(self, images):
        hidden_layer_model = Model(inputs=self.input, outputs=self.get_layer('encoded_layer').output)
        return hidden_layer_model.predict(images)