mindspore.mint.nn.functional.dropout
- mindspore.mint.nn.functional.dropout(input, p=0.5, training=True)[source]
During training, randomly zeroes some of the elements of the input tensor with probability p from a Bernoulli distribution. It plays the role of reducing neuron correlation and avoid overfitting. And the return will be multiplied by \(\frac{1}{1-p}\) during training. During the reasoning, this operation returns the same Tensor as the input.
- Parameters
input (Tensor) – The input Tensor of shape \((*, N)\).
p (float) – The dropping rate of input neurons, between 0 and 1, e.g. p = 0.1, means dropping out 10% of input neurons. Default:
0.5
.training (bool) – Apply dropout if it is
True
, if it isFalse
, the input is returned directly, and p is invalid. Default:True
.
- Returns
output (Tensor) - Zeroed tensor, with the same shape and data type as input.
- Raises
- Supported Platforms:
Ascend
Examples
>>> import mindspore >>> from mindspore import Tensor, mint >>> input = Tensor(((20, 16), (50, 50)), mindspore.float32) >>> output = mint.nn.functional.dropout(input, p=0.5) >>> print(output.shape) (2, 2)