gmm_ops.py 文件源码

def _define_maximization_operation(self, num_batches):
    """Maximization operations."""
    # TODO(xavigonzalvo): some of these operations could be moved to C++.
    # Compute the effective number of data points assigned to component k.
    with ops.control_dependencies(self._w):
      points_in_k = array_ops.squeeze(
          math_ops.add_n(self._points_in_k), squeeze_dims=[0])
      # Update alpha.
      if 'w' in self._params:
        final_points_in_k = points_in_k / num_batches
        num_examples = math_ops.to_float(math_ops.reduce_sum(final_points_in_k))
        self._alpha_op = self._alpha.assign(final_points_in_k /
                                            (num_examples + MEPS))
      else:
        self._alpha_op = control_flow_ops.no_op()
      self._train_ops = [self._alpha_op]

      # Update means.
      points_in_k_expanded = array_ops.reshape(points_in_k,
                                               [self._num_classes, 1, 1])
      if 'm' in self._params:
        self._means_op = self._means.assign(
            math_ops.div(
                math_ops.add_n(self._w_mul_x), points_in_k_expanded + MEPS))
      else:
        self._means_op = control_flow_ops.no_op()
      # means are (num_classes x 1 x dims)

      # Update covariances.
      with ops.control_dependencies([self._means_op]):
        b = math_ops.add_n(self._w_mul_x2) / (points_in_k_expanded + MEPS)
        new_covs = []
        for k in range(self._num_classes):
          mean = self._means.value()[k, :, :]
          square_mean = math_ops.matmul(mean, mean, transpose_a=True)
          new_cov = b[k, :, :] - square_mean + self._min_var
          if self._covariance_type == FULL_COVARIANCE:
            new_covs.append(array_ops.expand_dims(new_cov, 0))
          elif self._covariance_type == DIAG_COVARIANCE:
            new_covs.append(
                array_ops.expand_dims(array_ops.diag_part(new_cov), 0))
        new_covs = array_ops.concat(new_covs, 0)
        if 'c' in self._params:
          # Train operations don't need to take care of the means
          # because covariances already depend on it.
          with ops.control_dependencies([self._means_op, new_covs]):
            self._train_ops.append(
                state_ops.assign(
                    self._covs, new_covs, validate_shape=False))