Document feedback

Question document fragment

When a question document fragment contains a formula, it is displayed as a space.

Submission type

issue

It's a little complicated...

I'd like to ask someone.

Please select the submission type

Problem type

Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

- Usability:

- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

- Correctness:

- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

- Content Compliance:

- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

- Copyright infringement.

Please select the type of question

Problem description

Describe the bug so that we can quickly locate the problem.

Submit

Document feedback

mindspore.ops.BinaryCrossEntropy

class mindspore.ops.BinaryCrossEntropy(reduction='mean')

Computes the binary cross entropy between the logits and the labels.

Sets logits as $x$, labels as $y$, output as $\ell (x,y)$. Let,

$$L=\{l_1,\dots ,l_N{\}}^{\mathrm{\top }},l_n=-w_n[y_n\cdot \log x_n+(1-y_n)\cdot \log (1-x_n)]$$

In which, $L$ indicates the loss of all batch_sizes, $l$ indicates the loss of one batch_size, and n indicates one batch_size in the 1-N range, $w_n$ indicates the weight of $n$-th batch of binary cross entropy. Then,

$$\begin{array}{r}\ell (x,y)=\{\begin{array}{ll}L,& \text{if reduction}=\text{'none';}\\ \mathrm{mean}(L),& \text{if reduction}=\text{'mean';}\\ \mathrm{sum}(L),& \text{if reduction}=\text{'sum'.}\end{array}\end{array}$$

Warning

• The value of $x$ must range from 0 to 1.

Parameters

reduction (str) – Specifies the reduction to be applied to the output. Its value must be one of ‘none’, ‘mean’ or ‘sum’. Default: ‘mean’.

Inputs:

• logits (Tensor) - The predictive value whose data type must be float16 or float32, The shape is $(N,\ast )$ where $\ast$ means, any number of additional dimensions.

• labels (Tensor) - The target value which has the same shape and data type as logits.

• weight (Tensor, optional) - A rescaling weight applied to the loss of each batch element. And it must have the same shape and data type as logits. Default: None.

Outputs:

Tensor or Scalar. Returns Tensor that has the same dtype and shape as logits if reduction is ‘none’. Otherwise, returns a scalar Tensor.

Raises

• TypeError – If dtype of logits, labels or weight (if given) is neither float16 nor float32.

• ValueError – If reduction is not one of ‘none’, ‘mean’ or ‘sum’.

• ValueError – If shape of labels is not the same as logits or weight (if given).

• TypeError – If logits, labels or weight is not a Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.binary_cross_entropy = ops.BinaryCrossEntropy()
...     def construct(self, logits, labels, weight):
...         result = self.binary_cross_entropy(logits, labels, weight)
...         return result
...
>>> net = Net()
>>> logits = Tensor(np.array([0.2, 0.7, 0.1]), mindspore.float32)
>>> labels = Tensor(np.array([0., 1., 0.]), mindspore.float32)
>>> weight = Tensor(np.array([1, 2, 2]), mindspore.float32)
>>> output = net(logits, labels, weight)
>>> print(output)
0.38240486