losses_ops.py 文件源码

def softmax_classifier(tensor_in,
                       labels,
                       weights,
                       biases,
                       class_weight=None,
                       name=None):
  """Returns prediction and loss for softmax classifier.

  This function returns "probabilities" and a cross entropy loss. To obtain
  predictions, use `tf.argmax` on the returned probabilities.

  This function requires labels to be passed in one-hot encoding.

  Args:
    tensor_in: Input tensor, [batch_size, feature_size], features.
    labels: Tensor, [batch_size, n_classes], one-hot labels of the output
      classes.
    weights: Tensor, [batch_size, feature_size], linear transformation
      matrix.
    biases: Tensor, [batch_size], biases.
    class_weight: Tensor, optional, [n_classes], weight for each class.
      If not given, all classes are supposed to have weight one.
    name: Operation name.

  Returns:
    `tuple` of softmax predictions and loss `Tensor`s.
  """
  with ops.name_scope(name, 'softmax_classifier', [tensor_in, labels]):
    logits = nn.xw_plus_b(tensor_in, weights, biases)
    if class_weight is not None:
      logits = math_ops.multiply(logits, class_weight)
    return nn.softmax(logits), loss_ops.softmax_cross_entropy(logits, labels)