tf.compat.v1.losses.add

Computes the weighted loss.

tf.compat.v1.losses.compute_weighted_loss(
    losses, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES,
    reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)

Args
`losses`	`Tensor` of shape `[batch_size, d1, ... dN]`.
`weights`	Optional `Tensor` whose rank is either 0, or the same rank as `losses`, and must be broadcastable to `losses` (i.e., all dimensions must be either `1`, or the same as the corresponding `losses` dimension).
`scope`	the scope for the operations performed in computing the loss.
`loss_collection`	the loss will be added to these collections.
`reduction`	Type of reduction to apply to loss.

Returns
Weighted loss `Tensor` of the same type as `losses`. If `reduction` is `NONE`, this has the same shape as `losses`; otherwise, it is scalar.

Raises
`ValueError`	If `weights` is `None` or the shape is not compatible with `losses`, or if the number of dimensions (rank) of either `losses` or `weights` is missing.

Note:

When calculating the gradient of a weighted loss contributions from both losses and weights are considered. If your weights depend on some model parameters but you do not want this to affect the loss gradient, you need to apply tf.stop_gradient to weights before passing them to compute_weighted_loss.

Eager Compatibility

The loss_collection argument is ignored when executing eagerly. Consider holding on to the return value or collecting losses via a tf.keras.Model.

TensorFlow

tf.compat / v1 / v1.losses.add_loss

Args

Returns

Raises

Note:

Eager Compatibility

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

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tf.dtypes

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tf.estimator

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tf.graph_util

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tf.losses

tf.math

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tf.compat / v1 / v1.losses.add_loss

Args

Returns

Raises

Note:

Eager Compatibility