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mindspore.ops.Dropout3D

class mindspore.ops.Dropout3D(keep_prob=0.5)

During training, randomly zeroes some channels of the input tensor with probability $1-keep\mathrm{\_}prob$ from a Bernoulli distribution(For a 5-dimensional tensor with a shape of NCDHW, the channel feature map refers to a 3-dimensional feature map with a shape of DHW).

Note

The keep probability $keep\mathrm{\_}prob$ is equal to $1-p$ in mindspore.ops.dropout3d().

Dropout3D can improve the independence between channel feature maps.

Parameters

keep_prob (float) – The keep probability of a channel, between 0 and 1, e.g. keep_prob = 0.8, means dropping out 20% of channels. Default: 0.5 .

Inputs:

• x (Tensor) - A 5-D tensor with shape $(N,C,D,H,W)$, where N is the batch size, C is the number of channels, D is the feature depth, H is the feature height, and W is the feature width.

Outputs:

• output (Tensor) - With the same shape and data type as x.

• mask (Tensor) - With the same shape as x and the data type is bool.

Raises

• TypeError – If the data type of keep_prob is not float.

• ValueError – If keep_prob is out of the range [0.0, 1.0]; or if the dim of input is not 5-D.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> dropout = ops.Dropout3D(keep_prob=0.5)
>>> x = Tensor(np.ones([2, 1, 2, 1, 2]), mindspore.float32)
>>> output, mask = dropout(x)
>>> print(output.shape)
(2, 1, 2, 1, 2)