tf.compat.v1.losses.sparse_softmax_cross_entropy
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.
© 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/sparse_softmax_cross_entropy