mindspore.mint.nn.Dropout2d

class mindspore.mint.nn.Dropout2d(p=0.5)[source]

During training, randomly zeroes some channels of the input tensor with probability p from a Bernoulli distribution (For a 4-dimensional tensor with a shape of \(NCHW\), the channel feature map refers to a 2-dimensional feature map with the shape of \(HW\)).

For example, the \(j\_th\) channel of the \(i\_th\) sample in the batched input is a to-be-processed 2D tensor input[i,j]. Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.

Dropout2d can improve the independence between channel feature maps.

Warning

This is an experimental API that is subject to change or deletion.

Refer to mindspore.mint.nn.functional.dropout2d() for more details.

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor, mint
>>> import numpy as np
>>> dropout = mint.nn.Dropout2d(p=0.5)
>>> x = Tensor(np.ones([2, 1, 2, 3]), mindspore.float32)
>>> output = dropout(x)
>>> print(output.shape)
(2, 1, 2, 3)