def sample_visible_from_hidden(self, hidden):
    ' Sample the visible units from the hidden units.\n        This is the Negative phase of the Contrastive Divergence algorithm.\n        :param hidden: activations of the hidden units\n        :return: visible probabilities\n        '
    visible_activation = (tf.matmul(hidden, tf.transpose(self.W)) + self.bv_)
    if (self.visible_unit_type == 'bin'):
        vprobs = tf.nn.sigmoid(visible_activation)
    elif (self.visible_unit_type == 'gauss'):
        vprobs = tf.truncated_normal((1, self.num_visible), mean=visible_activation, stddev=self.stddev)
    else:
        vprobs = None
    return vprobs
Sample the visible units from the hidden units.
This is the Negative phase of the Contrastive Divergence algorithm.
:param hidden: activations of the hidden units
:return: visible probabilities
def sample_visible_from_hidden(self, hidden):
    ' Sample the visible units from the hidden units.\n        This is the Negative phase of the Contrastive Divergence algorithm.\n        :param hidden: activations of the hidden units\n        :return: visible probabilities\n        '
    visible_activation = (tf.matmul(hidden, tf.transpose(self.W)) + self.bv_)
    if (self.visible_unit_type == 'bin'):
        vprobs = tf.nn.sigmoid(visible_activation)
    elif (self.visible_unit_type == 'gauss'):
        vprobs = tf.truncated_normal((1, self.num_visible), mean=visible_activation, stddev=self.stddev)
    else:
        vprobs = None
    return vprobs