loss_ops.py 文件源码

def softmax_cross_entropy(
    logits, onehot_labels, weights=1.0, label_smoothing=0, scope=None):
  """Creates a cross-entropy loss using tf.nn.softmax_cross_entropy_with_logits.

  `weights` acts as a coefficient for the loss. If a scalar is provided,
  then the loss is simply scaled by the given value. If `weights` is a
  tensor of size [`batch_size`], then the loss weights apply to each
  corresponding sample.

  If `label_smoothing` is nonzero, smooth the labels towards 1/num_classes:
      new_onehot_labels = onehot_labels * (1 - label_smoothing)
                          + label_smoothing / num_classes

  Args:
    logits: [batch_size, num_classes] logits outputs of the network .
    onehot_labels: [batch_size, num_classes] one-hot-encoded labels.
    weights: Coefficients for the loss. The tensor must be a scalar or a tensor
      of shape [batch_size].
    label_smoothing: If greater than 0 then smooth the labels.
    scope: the scope for the operations performed in computing the loss.

  Returns:
    A scalar `Tensor` representing the mean loss value.

  Raises:
    ValueError: If the shape of `logits` doesn't match that of `onehot_labels`
      or if the shape of `weights` is invalid or if `weights` is None.
  """
  with ops.name_scope(scope, "softmax_cross_entropy_loss",
                      [logits, onehot_labels, weights]) as scope:
    logits.get_shape().assert_is_compatible_with(onehot_labels.get_shape())

    onehot_labels = math_ops.cast(onehot_labels, logits.dtype)

    if label_smoothing > 0:
      num_classes = math_ops.cast(
          array_ops.shape(onehot_labels)[1], logits.dtype)
      smooth_positives = 1.0 - label_smoothing
      smooth_negatives = label_smoothing / num_classes
      onehot_labels = onehot_labels * smooth_positives + smooth_negatives

    losses = nn.softmax_cross_entropy_with_logits(labels=onehot_labels,
                                                  logits=logits,
                                                  name="xentropy")
    return compute_weighted_loss(losses, weights, scope=scope)