mindspore.ops.dropout3d
- mindspore.ops.dropout3d(x, p=0.5)[source]
During training, randomly zeroes some channels of the input tensor with probability p 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 \(DHW\)).
For example, the \(j\_th\) channel of the \(i\_th\) sample in the batched input is a to-be-processed 3D tensor input[i,j]. Each channel will be zeroed out independently on every forward call which based on Bernoulli distribution probability p.
dropout3d can improve the independence between channel feature maps.
- Parameters
x (Tensor) – A 5D 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. The data type must be int8, int16, int32, int64, float16, float32 or float64.
p (float) – The dropping probability of a channel, between 0 and 1, e.g. p = 0.8, which means dropping out 80% of channels. Default: 0.5.
- Returns
Tensor, output, with the same shape and data type as x.
Tensor, mask, with the same shape as x and the data type is bool.
- Raises
TypeError – If x is not a Tensor.
TypeError – If dtype of x is not int8, int16, int32, int64, float16, float32 or float64.
TypeError – If the data type of p is not float.
ValueError – If p is out of the range [0.0, 1.0].
ValueError – If x shape is not 5D.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> input_x = Tensor(np.ones([2, 1, 2, 1, 2]), mindspore.float32) >>> output, mask = ops.dropout3d(input_x, 0.5) >>> print(output.shape) (2, 1, 2, 1, 2)