tf.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 )
Defined in tensorflow/python/ops/losses/losses_impl.py
.
Creates a cross-entropy loss using tf.nn.softmax_cross_entropy_with_logits.
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
onehot_labels
: [batch_size, num_classes]
target one-hot-encoded labels.logits
: [batch_size, num_classes]
logits outputs of the network .weights
: Optional Tensor
whose rank is either 0, or rank 1 and is broadcastable to the loss which is a Tensor
of shape [batch_size]
.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.Weighted loss Tensor
of the same type as logits
. If reduction
is NONE
, this has shape [batch_size]
; otherwise, it is scalar.
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.
© 2018 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/api_docs/python/tf/losses/softmax_cross_entropy