mindspore.mint.nn.Dropout
- class mindspore.mint.nn.Dropout(p=0.5)[source]
Dropout layer for the input.
Dropout is a means of regularization that reduces overfitting by preventing correlations between neuronal nodes. The operator randomly sets some neurons output to 0 according to p, which means the probability of discarding during training. And the return will be multiplied by
This technique is proposed in paper Dropout: A Simple Way to Prevent Neural Networks from Overfitting and proved to be effective to reduce over-fitting and prevents neurons from co-adaptation. See more details in Improving neural networks by preventing co-adaptation of feature detectors.
Note
Each channel will be zeroed out independently on every construct call.
Parameter p means the probability of the element of the input tensor to be zeroed.
- Parameters
p (float) – The dropout rate of input neurons, E.g. p =0.9, dropping out 90% of input neurons. Default:
0.5.
- Inputs:
x (Tensor) - The input of Dropout.
- Outputs:
Tensor, output tensor with the same shape as the x.
- Raises
TypeError – If the dtype of p is not float.
ValueError – If length of shape of x is less than 1.
- Supported Platforms:
Ascend
Examples
>>> import mindspore >>> from mindspore import Tensor, mint >>> import numpy as np >>> x = Tensor(np.ones([2, 2, 3]), mindspore.float32) >>> net = mint.nn.Dropout(p=0.2) >>> net.set_train() >>> output = net(x) >>> print(output.shape) (2, 2, 3)
