def mean_squared_error(
predictions, labels=None, weights=_WEIGHT_SENTINEL, scope=None,
targets=None, weight=_WEIGHT_SENTINEL):
"""Adds a Sum-of-Squares loss 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.
labels: The ground truth output tensor, same dimensions as 'predictions'.
weights: Coefficients for the loss a scalar, a tensor of shape
[batch_size] or a tensor whose shape matches `predictions`.
scope: The scope for the operations performed in computing the loss.
targets: Deprecated alias for `labels`.
weight: Deprecated alias for `weights`.
Returns:
A scalar `Tensor` representing the loss value.
Raises:
ValueError: If the shape of `predictions` doesn't match that of `labels` or
if the shape of `weight` is invalid.
"""
labels = _labels(labels, targets)
weights = _weights(weights, weight)
with ops.name_scope(scope, "mean_squared_error",
[predictions, labels, weights]) as scope:
predictions.get_shape().assert_is_compatible_with(labels.get_shape())
predictions = math_ops.to_float(predictions)
labels = math_ops.to_float(labels)
losses = math_ops.square(math_ops.sub(predictions, labels))
return compute_weighted_loss(losses, weights)
评论列表
文章目录
