autoencoder.py 文件源码

def build_conv_autoencoder(input_dim=(28, 28, 1)):
    input_img = Input(shape=input_dim)  # adapt this if using `channels_first` image data format

    x = Conv2D(64, (3, 3), activation='relu', padding='same')(input_img)
    x = MaxPooling2D((2, 2), padding='same')(x)
    x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
    x = MaxPooling2D((2, 2), padding='same')(x)
    x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
    encoded = MaxPooling2D((2, 2), padding='same')(x)

    # at this point the representation is (4, 4, 8) i.e. 128-dimensional

    x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)
    x = UpSampling2D((2, 2))(x)
    x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
    x = UpSampling2D((2, 2))(x)
    if input_dim[0] == 28:
        x = Conv2D(64, (3, 3), activation='relu')(x)
    else:
        x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)

    x = UpSampling2D((2, 2))(x)
    decoded = Conv2D(input_dim[2], (3, 3), activation='sigmoid', padding='same')(x)

    autoencoder = Model(input_img, decoded)
    autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
    return autoencoder


# def build_lstm_autoencoder(timesteps, input_dim)
#   inputs = Input(shape=(timesteps, input_dim))
#   encoded = LSTM(latent_dim)(inputs)

#   decoded = RepeatVector(timesteps)(encoded)
#   decoded = LSTM(input_dim, return_sequences=True)(decoded)

#   sequence_autoencoder = Model(inputs, decoded)
#   encoder = Model(inputs, encoded)
#   return encoder, sequence_autoencoder