def load_model(self, shape, gibbs_sampling_steps, model_path):
    ' Load a trained model from disk. The shape of the model\n        (num_visible, num_hidden) and the number of gibbs sampling steps\n        must be known in order to restore the model.\n        :param shape: tuple(num_visible, num_hidden)\n        :param gibbs_sampling_steps:\n        :param model_path:\n        :return: self\n        '
    (self.num_visible, self.num_hidden) = (shape[0], shape[1])
    self.gibbs_sampling_steps = gibbs_sampling_steps
    tf.reset_default_graph()
    self._build_model()
    init_op = tf.global_variables_initializer()
    self.tf_saver = tf.train.Saver()
    with tf.Session() as self.tf_session:
        self.tf_session.run(init_op)
        self.tf_saver.restore(self.tf_session, model_path)
Load a trained model from disk. The shape of the model
(num_visible, num_hidden) and the number of gibbs sampling steps
must be known in order to restore the model.
:param shape: tuple(num_visible, num_hidden)
:param gibbs_sampling_steps:
:param model_path:
:return: self
def load_model(self, shape, gibbs_sampling_steps, model_path):
    ' Load a trained model from disk. The shape of the model\n        (num_visible, num_hidden) and the number of gibbs sampling steps\n        must be known in order to restore the model.\n        :param shape: tuple(num_visible, num_hidden)\n        :param gibbs_sampling_steps:\n        :param model_path:\n        :return: self\n        '
    (self.num_visible, self.num_hidden) = (shape[0], shape[1])
    self.gibbs_sampling_steps = gibbs_sampling_steps
    tf.reset_default_graph()
    self._build_model()
    init_op = tf.global_variables_initializer()
    self.tf_saver = tf.train.Saver()
    with tf.Session() as self.tf_session:
        self.tf_session.run(init_op)
        self.tf_saver.restore(self.tf_session, model_path)