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# tf.contrib.losses.softmax_cross_entropy

```tf.contrib.losses.softmax_cross_entropy(
logits,
onehot_labels,
weights=1.0,
label_smoothing=0,
scope=None
)
```

Creates a cross-entropy loss using tf.nn.softmax_cross_entropy_with_logits. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed after 2016-12-30. Instructions for updating: Use tf.losses.softmax_cross_entropy instead. Note that the order of the logits and labels arguments has been changed.

`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 of `logits` doesn't match that of `onehot_labels` or if the shape of `weights` is invalid or if `weights` 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/contrib/losses/softmax_cross_entropy