mindspore.ops.dropout

mindspore.ops.dropout(input, p=0.5, training=True, seed=None)[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 x.

Parameters

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

  • p (float, optional) – The dropping rate, between 0 and 1, e.g. p = 0.1, means dropping out 10% of input units. Default: 0.5 .

  • training (bool) – Apply dropout if is True. Default: True.

  • seed (int, optional) – Seed is used as entropy source for Random number engines generating pseudo-random numbers. Default: None , which will be treated as 0 .

Returns

  • output (Tensor) - Zeroed tensor, with the same shape and data type as input.

Raises

  • TypeError – If p is not a float.

  • TypeError – If dtype of input is not float16, float32 or float64.

  • TypeError – If input is not a Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, ops
>>> input = Tensor(((20, 16), (50, 50)), mindspore.float32)
>>> output = ops.dropout(input, p=0.5)
>>> print(output.shape)
(2, 2)