Creating an RBM using the TensorFlow Keras layers API

Now that you have an appreciation of some of the theoretical underpinnings of the RBM, let's look at how we can implement it using the TensorFlow 2.0 library. For this purpose, we will represent the RBM as a custom layer type using the Keras layers API.

Code in this chapter was adapted to TensorFlow 2 from the original Theano (another deep learning Python framework) code from deeplearning.net.

Firstly, we extend tf.keras.layer:

from tensorflow.keras import layers
import tensorflow_probability as tfp
class RBM(layers.Layer):
    def __init__(self, number_hidden_units=10, number_visible_units=None, learning_rate=0.1, cd_steps=1):
        super().__init__()
        self.number_hidden_units = number_hidden_units
        self.number_visible_units = number_visible_units
        self.learning_rate = learning_rate
        self.cd_steps = cd_steps

We input a number of hidden units, visible units, a...