tf.contrib.losses.softmax_cross_entropy

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

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

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

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.

A scalar Tensor representing the mean loss value.

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.