tf.raw_ops.SparseSoftmaxCrossEntropyWithLogits
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Computes softmax cross entropy cost and gradients to backpropagate.
tf.raw_ops.SparseSoftmaxCrossEntropyWithLogits(
features, labels, name=None
)
Unlike SoftmaxCrossEntropyWithLogits, this operation does not accept a matrix of label probabilities, but rather a single label per row of features. This label is considered to have probability 1.0 for the given row.
Inputs are the logits, not probabilities.
Args |
|---|
features | A Tensor. Must be one of the following types: half, bfloat16, float32, float64. batch_size x num_classes matrix |
labels | A Tensor. Must be one of the following types: int32, int64. batch_size vector with values in [0, num_classes). This is the label for the given minibatch entry. |
name | A name for the operation (optional). |
Returns |
|---|
A tuple of Tensor objects (loss, backprop). |
loss | A Tensor. Has the same type as features. |
backprop | A Tensor. Has the same type as features. |
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Last updated 2024-04-26 UTC.
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