gmm_ops.py 文件源码

def _define_diag_covariance_probs(self, shard_id, shard):
    """Defines the diagonal covariance probabilities per example in a class.

    Args:
      shard_id: id of the current shard.
      shard: current data shard, 1 X num_examples X dimensions.

    Returns a matrix num_examples * num_classes.
    """
    # num_classes X 1
    # TODO(xavigonzalvo): look into alternatives to log for
    # reparametrization of variance parameters.
    det_expanded = tf.reduce_sum(tf.log(self._covs + 1e-3),
                                 1, keep_dims=True)
    diff = shard - self._means
    x2 = tf.square(diff)
    cov_expanded = tf.expand_dims(1.0 / (self._covs + 1e-3), 2)
    # num_classes X num_examples
    x2_cov = tf.batch_matmul(x2, cov_expanded)
    x2_cov = tf.transpose(tf.squeeze(x2_cov, [2]))
    self._probs[shard_id] = -0.5 * (
        tf.to_float(self._dimensions) * tf.log(2.0 * np.pi) +
        tf.transpose(det_expanded) + x2_cov)