r"""undocumented"""
__all__ = [
"MLP"
]
import torch
import torch.nn as nn
from ..utils import initial_parameter
[文档]class MLP(nn.Module):
r"""
多层感知器
.. note::
隐藏层的激活函数通过activation定义。一个str/function或者一个str/function的list可以被传入activation。
如果只传入了一个str/function，那么所有隐藏层的激活函数都由这个str/function定义；
如果传入了一个str/function的list，那么每一个隐藏层的激活函数由这个list中对应的元素定义，其中list的长度为隐藏层数。
输出层的激活函数由output_activation定义，默认值为None，此时输出层没有激活函数。
Examples::
>>> net1 = MLP([5, 10, 5])
>>> net2 = MLP([5, 10, 5], 'tanh')
>>> net3 = MLP([5, 6, 7, 8, 5], 'tanh')
>>> net4 = MLP([5, 6, 7, 8, 5], 'relu', output_activation='tanh')
>>> net5 = MLP([5, 6, 7, 8, 5], ['tanh', 'relu', 'tanh'], 'tanh')
>>> for net in [net1, net2, net3, net4, net5]:
>>> x = torch.randn(5, 5)
>>> y = net(x)
>>> print(x)
>>> print(y)
"""
[文档] def __init__(self, size_layer, activation='relu', output_activation=None, initial_method=None, dropout=0.0):
r"""
:param List[int] size_layer: 一个int的列表，用来定义MLP的层数，列表中的数字为每一层是hidden数目。MLP的层数为 len(size_layer) - 1
:param Union[str,func,List[str]] activation: 一个字符串或者函数的列表，用来定义每一个隐层的激活函数，字符串包括relu，tanh和
sigmoid，默认值为relu
:param Union[str,func] output_activation: 字符串或者函数，用来定义输出层的激活函数，默认值为None，表示输出层没有激活函数
:param str initial_method: 参数初始化方式
:param float dropout: dropout概率，默认值为0
"""
super(MLP, self).__init__()
self.hiddens = nn.ModuleList()
self.output = None
self.output_activation = output_activation
for i in range(1, len(size_layer)):
if i + 1 == len(size_layer):
self.output = nn.Linear(size_layer[i - 1], size_layer[i])
else:
self.hiddens.append(nn.Linear(size_layer[i - 1], size_layer[i]))
self.dropout = nn.Dropout(p=dropout)
actives = {
'relu': nn.ReLU(),
'tanh': nn.Tanh(),
'sigmoid': nn.Sigmoid(),
}
if not isinstance(activation, list):
activation = [activation] * (len(size_layer) - 2)
elif len(activation) == len(size_layer) - 2:
pass
else:
raise ValueError(
f"the length of activation function list except {len(size_layer) - 2} but got {len(activation)}!")
self.hidden_active = []
for func in activation:
if callable(func):
self.hidden_active.append(func)
elif func.lower() in actives:
self.hidden_active.append(actives[func])
else:
raise ValueError("should set activation correctly: {}".format(activation))
if self.output_activation is not None:
if callable(self.output_activation):
pass
elif self.output_activation.lower() in actives:
self.output_activation = actives[self.output_activation]
else:
raise ValueError("should set activation correctly: {}".format(activation))
initial_parameter(self, initial_method)
[文档] def forward(self, x):
r"""
:param torch.Tensor x: MLP接受的输入
:return: torch.Tensor : MLP的输出结果
"""
for layer, func in zip(self.hiddens, self.hidden_active):
x = self.dropout(func(layer(x)))
x = self.output(x)
if self.output_activation is not None:
x = self.output_activation(x)
x = self.dropout(x)
return x