mindspore.nn.Dropout
- class mindspore.nn.Dropout(keep_prob=0.5, p=None, dtype=mstype.float32)[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 \(\frac{1}{1-p}\) during training. During the reasoning, this layer returns the same Tensor as the x.
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 keep_prob will be removed in a future version, please use parameter p instead. Parameter p means the probability of the element of the input tensor to be zeroed.
Parameter dtype will be removed in a future version. It is not recommended to define this parameter.
- Parameters
keep_prob (float) – Deprecated. The keep rate, greater than 0 and less equal than 1. E.g. rate=0.9, dropping out 10% of input neurons. Default:
0.5
.p (Union[float, int, None]) – The dropout rate, greater than or equal to 0 and less than 1. E.g. rate=0.9, dropping out 90% of input neurons. Default:
None
.dtype (
mindspore.dtype
) – Data type of input. Default:mstype.float32
.
- Inputs:
x (Tensor) - The input of Dropout with data type of float16 or float32.
- Outputs:
Tensor, output tensor with the same shape as the x.
- Raises
TypeError – If keep_prob is not a float.
TypeError – If the dtype of p is not float or int.
TypeError – If dtype of x is not neither float16 nor float32.
ValueError – If keep_prob is not in range (0, 1].
ValueError – If p is not in range [0, 1).
ValueError – If length of shape of x is less than 1.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> from mindspore import Tensor, nn >>> import numpy as np >>> x = Tensor(np.ones([2, 2, 3]), mindspore.float32) >>> net = nn.Dropout(p=0.2) >>> net.set_train() >>> output = net(x) >>> print(output.shape) (2, 2, 3)