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)