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__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) | |