mindspore.nn.Dropout2d

class mindspore.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.

Refer to mindspore.ops.dropout2d() for more details.

Supported Platforms:: Ascend GPU CPU

Examples

>>> dropout = 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)