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__author__ = 'Taneem Jan, improved the old model through pretrained Auto-encoders' |
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from keras.layers import Input, Dropout, Conv2D, MaxPooling2D, Conv2DTranspose, UpSampling2D |
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from keras.models import Model |
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from .Config import * |
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from .AModel import * |
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class autoencoder_image(AModel): |
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def __init__(self, input_shape, output_size, output_path): |
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AModel.__init__(self, input_shape, output_size, output_path) |
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self.name = 'autoencoder' |
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input_image = Input(shape=input_shape) |
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encoder = Conv2D(32, 3, padding='same', activation='relu')(input_image) |
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encoder = Conv2D(32, 3, padding='same', activation='relu')(encoder) |
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encoder = MaxPooling2D()(encoder) |
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encoder = Dropout(0.25)(encoder) |
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encoder = Conv2D(64, 3, padding='same', activation='relu')(encoder) |
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encoder = Conv2D(64, 3, padding='same', activation='relu')(encoder) |
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encoder = MaxPooling2D()(encoder) |
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encoder = Dropout(0.25)(encoder) |
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encoder = Conv2D(128, 3, padding='same', activation='relu')(encoder) |
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encoder = Conv2D(128, 3, padding='same', activation='relu')(encoder) |
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encoder = MaxPooling2D()(encoder) |
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encoded = Dropout(0.25, name='encoded_layer')(encoder) |
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decoder = Conv2DTranspose(128, 3, padding='same', activation='relu')(encoded) |
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decoder = Conv2DTranspose(128, 3, padding='same', activation='relu')(decoder) |
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decoder = UpSampling2D()(decoder) |
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decoder = Dropout(0.25)(decoder) |
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decoder = Conv2DTranspose(64, 3, padding='same', activation='relu')(decoder) |
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decoder = Conv2DTranspose(64, 3, padding='same', activation='relu')(decoder) |
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decoder = UpSampling2D()(decoder) |
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decoder = Dropout(0.25)(decoder) |
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decoder = Conv2DTranspose(32, 3, padding='same', activation='relu')(decoder) |
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decoder = Conv2DTranspose(3, 3, padding='same', activation='relu')(decoder) |
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decoder = UpSampling2D()(decoder) |
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decoded = Dropout(0.25)(decoder) |
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self.model = Model(input_image, decoded) |
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self.model.compile(optimizer='adadelta', loss='binary_crossentropy') |
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def fit_generator(self, generator, steps_per_epoch): |
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self.model.fit_generator(generator, steps_per_epoch=steps_per_epoch, epochs=EPOCHS, verbose=1) |
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self.save() |
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def predict_hidden(self, images): |
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hidden_layer_model = Model(inputs=self.input, outputs=self.get_layer('encoded_layer').output) |
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return hidden_layer_model.predict(images) |
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