tf.losses.softmax_cross_entropy

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Creates a cross-entropy loss using tf.nn.softmax_cross_entropy_with_logits_v2.

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
)

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

Note that onehot_labels and logits must have the same shape, e.g. [batch_size, num_classes]. The shape of weights must be broadcastable to loss, whose shape is decided by the shape of logits. In case the shape of logits is [batch_size, num_classes], loss is a Tensor of shape [batch_size].

onehot_labels One-hot-encoded labels.
logits Logits outputs of the network.
weights Optional Tensor that is broadcastable to loss.
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.

Eager Compatibility

The loss_collection argument is ignored when executing eagerly. Consider holding on to the return value or collecting losses via a tf.keras.Model.