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Creates a cross-entropy loss using tf.nn.softmax_cross_entropy_with_logits_v2.
tf.compat.v1.losses.softmax_cross_entropy(
onehot_labels, logits, weights=1.0, label_smoothing=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.
If label_smoothing is nonzero, smooth the labels towards 1/num_classes:
new_onehot_labels = onehot_labels * (1 - label_smoothing)
+ label_smoothing / num_classes
Note that onehot_labels and logits must have the same shape,
e.g. [batch_size, num_classes]. The shape of weights must be
broadcastable to loss, whose shape is decided by the shape of logits.
In case the shape of logits is [batch_size, num_classes], loss is
a Tensor of shape [batch_size].
Args | |
|---|---|
onehot_labels
|
One-hot-encoded labels. |
logits
|
Logits outputs of the network. |
weights
|
Optional Tensor that is broadcastable to loss.
|
label_smoothing
|
If greater than 0 then smooth the labels. |
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 shape [batch_size]; otherwise, it is scalar.
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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. Also if
onehot_labels or logits is None.
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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.