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 as0
.
- Returns
output (Tensor) - Zeroed tensor, with the same shape and data type as input.
- Raises
- 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)