binary_cross_entropy¶

paddle.nn.functional. binary_cross_entropy ( input, label, weight=None, reduction='mean', name=None ) [source]

Measure the binary_cross_entropy loss between input predictions input and target labels label . The binary_cross_entropy loss can be described as:

If weight is set, the loss is:

\[Out = -1 * weight * (label * log(input) + (1 - label) * log(1 - input))\]

If weight is None, the loss is:

\[Out = -1 * (label * log(input) + (1 - label) * log(1 - input))\]

If reduction set to 'none', the interface will return the original loss Out.

If reduction set to 'mean', the reduced mean loss is:

\[Out = MEAN(Out)\]

If reduction set to 'sum', the reduced sum loss is:

\[Out = SUM(Out)\]

Note that the input predictions input always be the output of sigmoid, and the target labels label should be numbers between 0 and 1.

Parameters

input (Tensor) – The input predications tensor. 2-D tensor with shape: [N, *], N is batch_size, * means number of additional dimensions. The input should always be the output of sigmod. Available dtype is float16, float32, float64.
label (Tensor) – The target labels tensor. 2-D tensor with the same shape as input. The target labels which values should be numbers between 0 and 1. Available dtype is float16, float32, float64.
weight (Tensor, optional) – A manual rescaling weight given to the loss of each batch element. If given, has to be a Tensor of size nbatch and the data type is float32, float64. Default is 'None'.
reduction (str, optional) – Indicate how to average the loss by batch_size, the candicates are 'none' | 'mean' | 'sum'. If reduction is 'none', the unreduced loss is returned; If reduction is 'mean', the reduced mean loss is returned; If reduction is 'sum', the summed loss is returned. Default is 'mean'.
name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

Tensor. If reduction is 'none', the shape of output is: same as input , else the shape of output is scalar.

Examples

           >>> import paddle

>>> input = paddle.to_tensor([0.5, 0.6, 0.7], 'float32')
>>> label = paddle.to_tensor([1.0, 0.0, 1.0], 'float32')
>>> output = paddle.nn.functional.binary_cross_entropy(input, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
        0.65537095)

          