| from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D | |
| from tensorflow.keras.models import Model | |
| class Autoencoder(object): | |
| def __init__(self): | |
| # Encoding | |
| input_layer = Input(shape=(28, 28, 1)) | |
| encoding_conv_layer_1 = Conv2D(16, (3, 3), activation='relu', padding='same')(input_layer) | |
| encoding_pooling_layer_1 = MaxPooling2D((2, 2), padding='same')(encoding_conv_layer_1) | |
| encoding_conv_layer_2 = Conv2D(8, (3, 3), activation='relu', padding='same')(encoding_pooling_layer_1) | |
| encoding_pooling_layer_2 = MaxPooling2D((2, 2), padding='same')(encoding_conv_layer_2) | |
| encoding_conv_layer_3 = Conv2D(8, (3, 3), activation='relu', padding='same')(encoding_pooling_layer_2) | |
| code_layer = MaxPooling2D((2, 2), padding='same')(encoding_conv_layer_3) | |
| # Decoding | |
| decodging_conv_layer_1 = Conv2D(8, (3, 3), activation='relu', padding='same')(code_layer) | |
| decodging_upsampling_layer_1 = UpSampling2D((2, 2))(decodging_conv_layer_1) | |
| decodging_conv_layer_2 = Conv2D(8, (3, 3), activation='relu', padding='same')(decodging_upsampling_layer_1) | |
| decodging_upsampling_layer_2 = UpSampling2D((2, 2))(decodging_conv_layer_2) | |
| decodging_conv_layer_3 = Conv2D(16, (3, 3), activation='relu')(decodging_upsampling_layer_2) | |
| decodging_upsampling_layer_3 = UpSampling2D((2, 2))(decodging_conv_layer_3) | |
| output_layer = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(decodging_upsampling_layer_3) | |
| self._model = Model(input_layer, output_layer) | |
| self._model.compile(optimizer='adadelta', loss='binary_crossentropy') | |
| def train(self, input_train, input_test, batch_size, epochs): | |
| self._model.fit(input_train, | |
| input_train, | |
| epochs = epochs, | |
| batch_size=batch_size, | |
| shuffle=True, | |
| validation_data=( | |
| input_test, | |
| input_test)) | |
| def getDecodedImage(self, encoded_imgs): | |
| decoded_image = self._model.predict(encoded_imgs) | |
| return decoded_image |
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