PyTorch-如何在评估模式下停用辍学

Question

这是我定义的模型，它是具有2个完全连接层的简单lstm。

import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

class mylstm(nn.Module):
    def __init__(self,input_dim, output_dim, hidden_dim,linear_dim):
        super(mylstm, self).__init__()
        self.hidden_dim=hidden_dim
        self.lstm=nn.LSTMCell(input_dim,self.hidden_dim)
        self.linear1=nn.Linear(hidden_dim,linear_dim)
        self.linear2=nn.Linear(linear_dim,output_dim)
    def forward(self, input):
        out,_=self.lstm(input)
        out=nn.Dropout(p=0.3)(out)
        out=self.linear1(out)
        out=nn.Dropout(p=0.3)(out)
        out=self.linear2(out)
        return out

x_train和x_val是带有shape的float数据帧(4478,30)，而y_train和y_val是带有shape的float df(4478,10)

    x_train.head()
Out[271]: 
       0       1       2       3    ...        26      27      28      29
0  1.6110  1.6100  1.6293  1.6370   ...    1.6870  1.6925  1.6950  1.6905
1  1.6100  1.6293  1.6370  1.6530   ...    1.6925  1.6950  1.6905  1.6960
2  1.6293  1.6370  1.6530  1.6537   ...    1.6950  1.6905  1.6960  1.6930
3  1.6370  1.6530  1.6537  1.6620   ...    1.6905  1.6960  1.6930  1.6955
4  1.6530  1.6537  1.6620  1.6568   ...    1.6960  1.6930  1.6955  1.7040

[5 rows x 30 columns]

x_train.shape
Out[272]: (4478, 30)

定义变量并做一次bp，我可以发现波动损失为1.4941

model=mylstm(30,10,200,100).double()
from torch import optim
optimizer=optim.RMSprop(model.parameters(), lr=0.001, alpha=0.9)
criterion=nn.L1Loss()
input_=torch.autograd.Variable(torch.from_numpy(np.array(x_train)))
target=torch.autograd.Variable(torch.from_numpy(np.array(y_train)))
input2_=torch.autograd.Variable(torch.from_numpy(np.array(x_val)))
target2=torch.autograd.Variable(torch.from_numpy(np.array(y_val)))
optimizer.zero_grad()
output=model(input_)
loss=criterion(output,target)
loss.backward()
optimizer.step()
moniter=criterion(model(input2_),target2)

moniter
Out[274]: tensor(1.4941, dtype=torch.float64, grad_fn=<L1LossBackward>)

但是我再次调用了正向函数，由于辍学的随机性，我得到了一个不同的数字

moniter=criterion(model(input2_),target2)
moniter
Out[275]: tensor(1.4943, dtype=torch.float64, grad_fn=<L1LossBackward>)

我应该怎么做才能消除预测短语中的所有缺失？

我想eval()：

moniter=criterion(model.eval()(input2_),target2)
moniter
Out[282]: tensor(1.4942, dtype=torch.float64, grad_fn=<L1LossBackward>)

moniter=criterion(model.eval()(input2_),target2)
moniter
Out[283]: tensor(1.4945, dtype=torch.float64, grad_fn=<L1LossBackward>)

并传递一个附加参数p来控制辍学：

import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class mylstm(nn.Module):
    def __init__(self,input_dim, output_dim, hidden_dim,linear_dim,p):
        super(mylstm, self).__init__()
        self.hidden_dim=hidden_dim
        self.lstm=nn.LSTMCell(input_dim,self.hidden_dim)
        self.linear1=nn.Linear(hidden_dim,linear_dim)
        self.linear2=nn.Linear(linear_dim,output_dim)
    def forward(self, input,p):
        out,_=self.lstm(input)
        out=nn.Dropout(p=p)(out)
        out=self.linear1(out)
        out=nn.Dropout(p=p)(out)
        out=self.linear2(out)
        return out

model=mylstm(30,10,200,100,0.3).double()

output=model(input_)
loss=criterion(output,target)
loss.backward()
optimizer.step()
moniter=criterion(model(input2_,0),target2)
Traceback (most recent call last):

  File "<ipython-input-286-e49b6fac918b>", line 1, in <module>
    output=model(input_)

  File "D:\Users\shan xu\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)

TypeError: forward() missing 1 required positional argument: 'p'

但是他们两个都不起作用。

Answer 1

您必须在您的nn.Dropout图层中定义您的图层__init__并将其分配给您的模型以响应调用eval().

所以像这样改变你的模型应该对你有用：

class mylstm(nn.Module):
    def __init__(self,input_dim, output_dim, hidden_dim,linear_dim,p):
        super(mylstm, self).__init__()
        self.hidden_dim=hidden_dim
        self.lstm=nn.LSTMCell(input_dim,self.hidden_dim)
        self.linear1=nn.Linear(hidden_dim,linear_dim)
        self.linear2=nn.Linear(linear_dim,output_dim)

        # define dropout layer in __init__
        self.drop_layer = nn.Dropout(p=p)
    def forward(self, input):
        out,_= self.lstm(input)

        # apply model dropout, responsive to eval()
        out= self.drop_layer(out)
        out= self.linear1(out)

        # apply model dropout, responsive to eval()
        out= self.drop_layer(out)
        out= self.linear2(out)
        return out

如果您像这样更改它，则在您调用eval().

注意：如果您想在之后继续训练，您需要调用train()您的模型以退出评估模式。

您还可以eval()在此处找到用于评估模式的dropout的小工作示例： nn.Dropout vs. F.dropout pyTorch