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.mint.nn.functional.dropout

mindspore.mint.nn.functional.dropout(input, p=0.5, training=True, inplace=False)

During training, randomly zeroes some of the elements of the input tensor with probability p from a Bernoulli distribution. It plays the role of reducing neuron correlation and avoid overfitting. And the return will be multiplied by $\frac{1}{1-p}$ during training. During the reasoning, this operation returns the same Tensor as the input.

Parameters

• input (Tensor) – The input Tensor of shape $(\ast ,N)$.

• p (float, optional) – The dropping rate of input neurons, between 0 and 1, e.g. p = 0.1, means dropping out 10% of input neurons. Default: 0.5 .

• training (bool, optional) – Apply dropout if it is True , if it is False , the input is returned directly, and p is invalid. Default: True .

• inplace (bool, optional) – If set to True , will do this operation in-place. Default: False .

Returns

• output (Tensor) - Zeroed tensor, with the same shape and data type as input.

Raises

• TypeError – If p is not a float.

• TypeError – If input is not a Tensor.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor, mint
>>> input = Tensor(((20, 16), (50, 50)), mindspore.float32)
>>> output = mint.nn.functional.dropout(input, p=0.5)
>>> print(output.shape)
(2, 2)