TensorFlow 2.0 implementation of mini VGG-based encoder for an autoencoder.

mini_vgg_encoder.py

class Encoder(tf.keras.layers.Layer):
    def __init__(self, **kwargs):
        super(Encoder, self).__init__()
        self.input_layer = tf.keras.layers.InputLayer(
                input_shape=kwargs['input_shape']
                )
        self.conv_1_layer_1 = tf.keras.layers.Conv2D(
                filters=32,
                kernel_size=(3, 3),
                activation=tf.nn.relu
                )
        self.conv_1_layer_2 = tf.keras.layers.Conv2D(
                filters=32,
                kernel_size=(3, 3),
                activation=tf.nn.relu
                )
        self.conv_2_layer_1 = tf.keras.layers.Conv2D(
                filters=64,
                kernel_size=(3, 3),
                activation=tf.nn.relu
                )
        self.conv_2_layer_2 = tf.keras.layers.Conv2D(
                filters=64,
                kernel_size=(3, 3),
                activation=tf.nn.sigmoid
                )

    def call(self, features):
        features = self.input_layer(features)
        activation = self.conv_1_layer_1(features)
        activation = self.conv_1_layer_2(activation)
        activation = self.conv_2_layer_1(activation)
        code = self.conv_2_layer_2(activation)
        return code