tf.compat.v1.losses.softmax_cross

Cross-entropy loss using tf.nn.sparse_softmax_cross_entropy_with_logits.

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

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 shape [batch_size], then the loss weights apply to each corresponding sample.

Args
`labels`	`Tensor` of shape `[d_0, d_1, ..., d_{r-1}]` (where `r` is rank of `labels` and result) and dtype `int32` or `int64`. Each entry in `labels` must be an index in `[0, num_classes)`. Other values will raise an exception when this op is run on CPU, and return `NaN` for corresponding loss and gradient rows on GPU.
`logits`	Unscaled log probabilities of shape `[d_0, d_1, ..., d_{r-1}, num_classes]` and dtype `float16`, `float32` or `float64`.
`weights`	Coefficients for the loss. This must be scalar or broadcastable to `labels` (i.e. same rank and each dimension is either 1 or the same).
`scope`	the scope for the operations performed in computing the loss.
`loss_collection`	collection to which the loss will be added.
`reduction`	Type of reduction to apply to loss.

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

Raises
`ValueError`	If the shapes of `logits`, `labels`, and `weights` are incompatible, or if any of them are None.

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.softmax_cross_entropy

Args

Returns

Raises

Eager Compatibility

TensorFlow

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tf.compat / v1 / v1.losses.softmax_cross_entropy

Args

Returns

Raises

Eager Compatibility