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mindspore.nn.Dropout

class mindspore.nn.Dropout(keep_prob=0.5, dtype=mstype.float32)

Dropout layer for the input.

Randomly set some elements of the input tensor to zero with probability $1-keep\mathrm{\_}prob$ during training using samples from a Bernoulli distribution.

The outputs are scaled by a factor of $\frac{1}{keep\mathrm{\_}prob}$ during training so that the output layer remains at a similar scale. During inference, 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.

Parameters

• keep_prob (float) – The keep rate, greater than 0 and less equal than 1. E.g. rate=0.9, dropping out 10% of input units. Default: 0.5.

• dtype (mindspore.dtype) – Data type of x. Default: mindspore.float32.

Inputs:

• x (Tensor) - The input of Dropout with data type of float16 or float32. The shape is $(N,\ast )$ where $\ast$ means, any number of additional dimensions.

Outputs:

Tensor, output tensor with the same shape as the x.

Raises

• TypeError – If keep_prob is not a float.

• TypeError – If dtype of x is not neither float16 nor float32.

• ValueError – If keep_prob is not in range (0, 1].

• ValueError – If length of shape of x is less than 1.

Supported Platforms:

Ascend GPU CPU

Examples

>>> x = Tensor(np.ones([2, 2, 3]), mindspore.float32)
>>> net = nn.Dropout(keep_prob=0.8)
>>> net.set_train()
Dropout<keep_prob=0.8>
>>> output = net(x)
>>> print(output.shape)
(2, 2, 3)