megengine.module.dropout 源代码

# -*- coding: utf-8 -*-
from ..functional import dropout
from .module import Module

[文档]class Dropout(Module): r"""Randomly sets some elements of inputs to zeros with the probability :math:`drop\_prob` during training. Commonly used in large networks for regularization and prevent overfitting, see `Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors<>`. Note that we perform dropout only during training, we also rescale(multiply) the output tensor by :math:`\frac{1}{1 - drop\_prob}`. During inference :class:`~.Dropout` is equal to :class:`~.module.identity.Identity`. Args: drop_prob(:class:`float`): The probability to drop (set to zero) each single element. Default: 0.0 Shape: - input: `(*)`. Input can be of any shape. - output: `(*)`. Output is of the same shape as input. Examples: >>> import numpy as np >>> data = Tensor(np.ones(10000000, dtype=np.float32)) >>> out = F.nn.dropout(data, 1.0 / 3.0, training=True) >>> assert not out.numpy().all() >>> out = F.nn.dropout(data, 1.0 / 3.0, training=False) >>> assert out.numpy().all() >>> out.numpy() array([1., 1., 1., ..., 1., 1., 1.], dtype=float32) """ def __init__(self, drop_prob=0.0, **kwargs): super().__init__(**kwargs) self.drop_prob = drop_prob def forward(self, inputs): if return dropout(inputs, self.drop_prob, training=True) else: return inputs def _module_info_string(self) -> str: return "drop_prob={drop_prob}".format(drop_prob=self.drop_prob)