vae_decoder.py

def decoder(z):
    activation = tf.nn.relu
    with tf.variable_scope("Decoder"):
        x = tf.layers.dense(z, units=FLAGS.inputs_decoder, activation=activation)
        x = tf.layers.dense(x, units=FLAGS.inputs_decoder, activation=activation)
        recovered_size = int(np.sqrt(FLAGS.inputs_decoder))
        x = tf.reshape(x, [-1, recovered_size, recovered_size, 1])

        x = tf.layers.conv2d_transpose(x, filters=64, kernel_size=4, strides=1, padding='same', activation=activation)
        x = tf.layers.conv2d_transpose(x, filters=64, kernel_size=4, strides=1, padding='same', activation=activation)
        x = tf.layers.conv2d_transpose(x, filters=64, kernel_size=4, strides=1, padding='same', activation=activation)

        x = tf.contrib.layers.flatten(x)
        x = tf.layers.dense(x, units=28 * 28, activation=None)

        x = tf.layers.dense(x, units=28 * 28, activation=tf.nn.sigmoid)
        img = tf.reshape(x, shape=[-1, 28, 28, 1])
        
        return img