tf.contrib.losses.sparse_softmax_cross_entropy
Cross-entropy loss using tf.nn.sparse_softmax_cross_entropy_with_logits. (deprecated)
tf.contrib.losses.sparse_softmax_cross_entropy(
logits, labels, weights=1.0, scope=None
)
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 size [batch_size], then the loss weights apply to each corresponding sample.
| Args | |
|---|---|
logits |
[batch_size, num_classes] logits outputs of the network . |
labels |
[batch_size, 1] or [batch_size] labels of dtype int32 or int64 in the range [0, num_classes). |
weights |
Coefficients for the loss. The tensor must be a scalar or a tensor of shape [batch_size] or [batch_size, 1]. |
scope |
the scope for the operations performed in computing the loss. |
| Returns | |
|---|---|
A scalar Tensor representing the mean loss value. |
| Raises | |
|---|---|
ValueError |
If the shapes of logits, labels, and weights are incompatible, or if weights is None. |
© 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/r1.15/api_docs/python/tf/contrib/losses/sparse_softmax_cross_entropy