tf.contrib.losses.softmax_cross_entropy(
logits,
onehot_labels,
weights=1.0,
label_smoothing=0,
scope=None
)
Defined in tensorflow/contrib/losses/python/losses/loss_ops.py
.
Creates a cross-entropy loss using tf.nn.softmax_cross_entropy_with_logits. (deprecated)
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.
If label_smoothing
is nonzero, smooth the labels towards 1/num_classes:
new_onehot_labels = onehot_labels * (1 - label_smoothing)
+ label_smoothing / num_classes
Args:
logits
: [batch_size, num_classes] logits outputs of the network .onehot_labels
: [batch_size, num_classes] one-hot-encoded labels.weights
: Coefficients for the loss. The tensor must be a scalar or 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.
Returns:
A scalar Tensor
representing the mean loss value.
Raises:
ValueError
: If the shape oflogits
doesn't match that ofonehot_labels
or if the shape ofweights
is invalid or ifweights
is None.