loss_ops.py 文件源码

def log_loss(predictions, targets, weight=1.0, epsilon=1e-7, scope=None):
  """Adds a Log Loss term to the training procedure.

  `weight` acts as a coefficient for the loss. If a scalar is provided, then the
  loss is simply scaled by the given value. If `weight` is a tensor of size
  [batch_size], then the total loss for each sample of the batch is rescaled
  by the corresponding element in the `weight` vector. If the shape of
  `weight` matches the shape of `predictions`, then the loss of each
  measurable element of `predictions` is scaled by the corresponding value of
  `weight`.

  Args:
    predictions: The predicted outputs.
    targets: The ground truth output tensor, same dimensions as 'predictions'.
    weight: Coefficients for the loss a scalar, a tensor of shape
      [batch_size] or a tensor whose shape matches `predictions`.
    epsilon: A small increment to add to avoid taking a log of zero.
    scope: The scope for the operations performed in computing the loss.

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

  Raises:
    ValueError: If the shape of `predictions` doesn't match that of `targets` or
      if the shape of `weight` is invalid.
  """
  with ops.name_scope(scope, "log_loss",
                      [predictions, targets]) as scope:
    predictions.get_shape().assert_is_compatible_with(targets.get_shape())
    if weight is None:
      raise ValueError("`weight` cannot be None")
    predictions = math_ops.to_float(predictions)
    targets = math_ops.to_float(targets)
    losses = -math_ops.mul(
        targets,
        math_ops.log(predictions + epsilon)) - math_ops.mul(
            (1 - targets), math_ops.log(1 - predictions + epsilon))
    return compute_weighted_loss(losses, weight)