mindspore.ops.Dropout

class mindspore.ops.Dropout(keep_prob=0.5, Seed0=0, Seed1=0)[source]

During training, randomly zeroes some of the elements of the input tensor with probability \(1 - keep\_prob\) from a Bernoulli distribution. It plays the role of reducing neuron correlation and avoid overfitting.

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

Parameters

keep_prob (float, optional) – The keep rate, between 0 and 1, e.g. keep_prob = 0.9, means dropping out 10% of input units. Default: 0.5 .
Seed0 (int, optional) – Seed0 value for random generating. Default: 0 .
Seed1 (int, optional) – Seed1 value for random generating. Default: 0 .

Inputs:

x (Tensor) - The input Tensor of shape \((*, N)\), with data type of float16, float32 or float64.

Outputs:

output (Tensor) - With the same shape and data type as x.
mask (Tensor) - The mask applied to x.
- On GPU and CPU, mask has the same shape and data type as x.
- On Ascend, to achieve a better performance, it is denoted as a 1-D Tensor with Uint8 data type. It has shape \((byte\_counts, )\) where \(byte\_counts\) is the number of bytes needed to mask the input x, \(byte\_counts\) is calculated using the following formula:
  
  \[byte\_counts = \text{ceil}(\text{cumprod}(x.shape) / 128) * 16\]
  
  If shape of x is \((2, 3, 4, 5, 6)\), the shape of mask will be \((96, )\).

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> dropout = ops.Dropout(keep_prob=0.5)
>>> x = Tensor(np.ones([1, 2, 3, 4, 5]), mindspore.float32)
>>> output, mask = dropout(x)
>>> print(output.shape, mask.shape, mask.dtype)
(1, 2, 3, 4, 5) (16,) UInt8