# tf.losses.sparse_softmax_cross_entropy(labels, logits, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES)

### tf.losses.sparse_softmax_cross_entropy(labels, logits, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES)

Cross-entropy loss using tf.nn.sparse_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.

#### Args:

• labels: Tensor of shape [d_0, d_1, ..., d_{r-1}] (where r is rank of labels and result) and dtype int32 or int64. Each entry in labels must be an index in [0, num_classes). Other values will raise an exception when this op is run on CPU, and return NaN for corresponding loss and gradient rows on GPU.
• logits: Unscaled log probabilities of shape [d_0, d_1, ..., d_{r-1}, num_classes] and dtype float32 or float64.
• weights: Coefficients for the loss. This must be scalar or of same rank as 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 shapes of logits, labels, and weight are incompatible, or if weights is None.