tf.compat.v1.nn.sigmoid_cross_entropy_with

Computes softmax cross entropy between logits and labels. (deprecated)

tf.compat.v1.nn.softmax_cross_entropy_with_logits(
    _sentinel=None, labels=None, logits=None, dim=-1, name=None, axis=None
)

Future major versions of TensorFlow will allow gradients to flow into the labels input on backprop by default.

See tf.nn.softmax_cross_entropy_with_logits_v2.

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.

If using exclusive labels (wherein one and only one class is true at a time), see sparse_softmax_cross_entropy_with_logits.

A common use case is to have logits and labels of shape [batch_size, num_classes], but higher dimensions are supported, with the dim argument specifying the class dimension.

Backpropagation will happen only into logits. To calculate a cross entropy loss that allows backpropagation into both logits and labels, see tf.nn.softmax_cross_entropy_with_logits_v2.

Note that to avoid confusion, it is required to pass only named arguments to this function.

Args
`_sentinel`	Used to prevent positional parameters. Internal, do not use.
`labels`	Each vector along the class dimension should hold a valid probability distribution e.g. for the case in which labels are of shape `[batch_size, num_classes]`, each row of `labels[i]` must be a valid probability distribution.
`logits`	Per-label activations, typically a linear output. These activation energies are interpreted as unnormalized log probabilities.
`dim`	The class dimension. Defaulted to -1 which is the last dimension.
`name`	A name for the operation (optional).
`axis`	Alias for dim.

Returns
A `Tensor` that contains the softmax cross entropy loss. Its type is the same as `logits` and its shape is the same as `labels` except that it does not have the last dimension of `labels`.

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tf.compat / v1 / v1.nn.sigmoid_cross_entropy_with_logits

Args

Returns