Add losses for VAE

    import tensorflow as tf

    P, Q = _make_tf_shapes_consistent(P, Q)

    P, Q = _ensure_float(P, Q)

    #extended 1-dimensional inputs two binary distribution

    #extended one of probabilites to to binary distribution

    if P.shape[-1] == 1:

      P = tf.concat([P,1-P], axis = -1)

      Q = tf.concat([Q,1-Q], axis = -1)

    def loss(P, Q):

      P, Q = _make_pytorch_shapes_consistent(P, Q)

      #extended 1-dimensional inputs two binary distribution

      #extended one of probabilites to binary distribution

      if P.shape[-1] == 1:

        P = torch.cat((P,1-P), dim = -1)

        Q = torch.cat((Q,1-Q), dim = -1)

class ShannonEntropy(Loss):

  """The ShannonEntropy of discrete-distribution.

  The inputs last dimension should be num of variable.

  The inputs should have shape (batch size, num of variable) and represents

  probabilites distribution.

  """

  def _compute_tf_loss(self, inputs):

    import tensorflow as tf

    #extended 1-dimensional inputs to binary distribution

    #extended one of probabilites to binary distribution

    if inputs.shape[-1] == 1:

      inputs = tf.concat([inputs,1-inputs], axis = -1)

    return tf.reduce_mean(-inputs*tf.math.log(1e-20+inputs), -1)

    import torch

    def loss(inputs):

      #extended 1-dimensional inputs to binary distribution

      #extended one of probabilites to binary distribution

      if inputs.shape[-1] == 1:

        inputs = torch.cat((inputs,1-inputs), dim = -1)

      return torch.mean(-inputs*torch.log(1e-20+inputs), -1)