TensorFlow 2.0 implementation of a decoder layer for a variational autoencoder.

decoder.py

class Decoder(tf.keras.layers.Layer):
  def __init__(self, original_dim):
    super(Decoder, self).__init__()
    self.hidden_layer_1 = tf.keras.layers.Dense(units=32, activation=tf.nn.relu)
    self.hidden_layer_2 = tf.keras.layers.Dense(units=64, activation=tf.nn.relu)
    self.hidden_layer_3 = tf.keras.layers.Dense(units=128, activation=tf.nn.relu)
    self.output_layer = tf.keras.layers.Dense(units=original_dim, activation=tf.nn.sigmoid)

  def call(self, input_features):
    activation_1 = self.hidden_layer_1(input_features)
    activation_2 = self.hidden_layer_2(activation_1)
    activation_3 = self.hidden_layer_3(activation_2)
    output = self.output_layer(activation_3)
    return output