tf.compat.v1.losses.softmax_cross_entropy
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. |
| 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. |
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.
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/compat/v1/losses/softmax_cross_entropy