tf.nn.softmax_cross_entropy_with_logits(_sentinel=None, labels=None, logits=None, dim=-1, name=None)
Docstring:
Computes softmax cross entropy between logits and labels.
Type: function
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.
与softmax搭配使用的交叉熵损失函数，输入不需要额外加一层softmax，softmax_cross_entropy_with_logits中会集成有softmax并进行了计算优化；它适用于分类的类别之间是相互排斥的场景，即只有一个标签（不是狗就是猫）。
NOTE: While the classes are mutually exclusive, their probabilities need not be. All that is required is that each row of labels is a valid probability distribution. If they are not, the computation of the gradient will be incorrect.
If using exclusive labels (wherein one and only one class is true at a time), see sparse_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.
logits and labels must have the same shape [batch_size, num_classes] and the same dtype (either float16, float32, or float64).
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 row labels[i] must be a valid probability distribution.
logits: Unscaled log probabilities.
dim: The class dimension. Defaulted to -1 which is the last dimension.
name: A name for the operation (optional).
Returns:
A 1-D Tensor of length batch_size of the same type as logits with the
softmax cross entropy loss.
