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tf.contrib.nn.deprecated_flipped_sparse_softmax_cross_entropy_with_logits

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Computes sparse softmax cross entropy between logits and labels.

tf.contrib.nn.deprecated_flipped_sparse_softmax_cross_entropy_with_logits(
    logits,
    labels,
    name=None
)

This function diffs from tf.nn.sparse_softmax_cross_entropy_with_logits only in the argument order.

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.

NOTE: For this operation, the probability of a given label is considered exclusive. That is, soft classes are not allowed, and the labels vector must provide a single specific index for the true class for each row of logits (each minibatch entry). For soft softmax classification with a probability distribution for each entry, see softmax_cross_entropy_with_logits.

WARNING: This op expects unscaled logits, since it performs a softmax on logits internally for efficiency. Do not call this op with the output of softmax, as it will produce incorrect results.

A common use case is to have logits of shape [batch_size, num_classes] and labels of shape [batch_size]. But higher dimensions are supported.

Args:

  • logits: Unscaled log probabilities of rank r and shape [d_0, d_1, ..., d_{r-2}, num_classes] and dtype float32 or float64.
  • labels: Tensor of shape [d_0, d_1, ..., d_{r-2}] 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 corresponding loss and gradient rows on GPU.
  • name: A name for the operation (optional).

Returns:

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

Raises:

  • 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.