tf.nn.sparse_softmax_cross_entropy_with_logits

Computes sparse softmax cross entropy between logits and labels.

tf.nn.sparse_softmax_cross_entropy_with_logits(
    labels, logits, name=None
)

Measures the probability error in discrete classification tasks in which the classes are mutually exclusive (each entry is in exactly one class). For example, each CIFAR-10 image is labeled with one and only one label: an image can be a dog or a truck, but not both.

A common use case is to have logits of shape [batch_size, num_classes] and have labels of shape [batch_size], but higher dimensions are supported, in which case the dim-th dimension is assumed to be of size num_classes. logits must have the dtype of float16, float32, or float64, and labels must have the dtype of int32 or int64.

logits = tf.constant([[2., -5., .5, -.1],
                      [0., 0., 1.9, 1.4],
                      [-100., 100., -100., -100.]])
labels = tf.constant([0, 3, 1])
tf.nn.sparse_softmax_cross_entropy_with_logits(
    labels=labels, logits=logits).numpy()
array([0.29750752, 1.1448325 , 0.        ], dtype=float32)

To avoid confusion, passing only named arguments to this function is recommended.

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 float16, float32, or float64.
name A name for the operation (optional).

A Tensor of the same shape as labels and of the same type as logits with the softmax cross entropy loss.

ValueError If logits are scalars (need to have rank >= 1) or if the rank of the labels is not equal to the rank of the logits minus one.