autoencoder_convolutional.py

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