# tf.losses.softmax_cross_entropy(onehot_labels, logits, weights=1.0, label_smoothing=0, scope=None, loss_collection=tf.GraphKeys.LOSSES)

### tf.losses.softmax_cross_entropy(onehot_labels, logits, weights=1.0, label_smoothing=0, scope=None, loss_collection=tf.GraphKeys.LOSSES)

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

#### Args:

• 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 the same rank as onehot_labels, and must be broadcastable to onehot_labels (i.e., all dimensions must be either 1, or the same as the corresponding losses dimension).
• 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.

#### 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.