TensorFlow 2.0 implementation of mini VGG-based decoder for an autoencoder

mini_vgg_decoder.py

class Decoder(tf.keras.layers.Layer):
    def __init__(self, **kwargs):
        super(Decoder, self).__init__()
        self.convt_1_layer_1 = tf.keras.layers.Conv2DTranspose(
                filters=64,
                kernel_size=(3, 3),
                activation=tf.nn.relu
                )
        self.convt_1_layer_2 = tf.keras.layers.Conv2DTranspose(
                filters=64,
                kernel_size=(3, 3),
                activation=tf.nn.relu
                )
        self.convt_2_layer_1 = tf.keras.layers.Conv2DTranspose(
                filters=32,
                kernel_size=(3, 3),
                activation=tf.nn.relu
                )
        self.convt_2_layer_2 = tf.keras.layers.Conv2DTranspose(
                filters=1,
                kernel_size=(3, 3),
                strides=(1, 1),
                activation=tf.nn.sigmoid
                )

    def call(self, features):
        activation = self.convt_1_layer_1(features)
        activation = self.convt_1_layer_2(activation)
        activation = self.convt_2_layer_1(activation)
        output = self.convt_2_layer_2(activation)
        return output