Question

我想要一个RNN模型并教它学习从“hihell”生成“ihello”。我是Pytorch的新手，并按照视频中的指令编写代码。我写了两个名为train.py和model.py的python文件。这是model.py：

#----------------- model for teach rnn hihell to ihello
#-----------------  OUR MODEL ---------------------
import torch
import torch.nn as nn
from torch import autograd

class Model(nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        self.rnn=nn.RNN(input_size=input_size,hidden_size=hidden_size,batch_first=True)
    def forward(self,x,hidden):
        #Reshape input in (batch_size,sequence_length,input_size)
        x=x.view(batch_size,sequence_length,input_size)
        #Propagate input through RNN
        #Input:(batch,seq+len,input_size)
        out,hidden=self.rnn(x,hidden)
        out=out.view(-1,num_classes)
        return hidden,out
    def init_hidden(self):
        #Initialize hidden and cell states
        #(num_layers*num_directions,batch,hidden_size)
        return autograd.Variable(torch.zeros(num_layers,batch_size,hidden_size))

这是train.py：

"""----------------------train for teach rnn to hihell to ihello--------------------------"""
#-----------------  DATA PREPARATION ---------------------
#Import
import torch
import torch.nn as nn
from torch import autograd
from model import Model
import sys


idx2char=['h','i','e','l','o']
#Teach hihell->ihello
x_data=[0,1,0,2,3,3]#hihell
y_data=[1,0,2,3,3,4]#ihello
one_hot_lookup=[[1,0,0,0,0],#0
                [0,1,0,0,0],#1
                [0,0,1,0,0],#2
                [0,0,0,1,0],#3
                [0,0,0,0,1]]#4
x_one_hot=[one_hot_lookup[x] for x in x_data]
inputs=autograd.Variable(torch.Tensor(x_one_hot))
labels=autograd.Variable(torch.LongTensor(y_data))
""" ----------- Parameters Initialization------------"""
num_classes = 5
input_size = 5  # one hot size
hidden_size = 5  # output from LSTM to directly predict onr-hot
batch_size = 1  # one sequence
sequence_length = 1  # let's do one by one
num_layers = 1  # one layer RNN
"""-----------------  LOSS AND TRAINING ---------------------"""
#Instantiate RNN model
model=Model()
#Set loss and optimizer function
#CrossEntropyLoss=LogSoftmax+NLLLOSS
criterion=torch.nn.CrossEntropyLoss()
optimizer=torch.optim.Adam(model.parameters(),lr=0.1)

"""----------------Train the model-------------------"""
for epoch in range(100):
    optimizer.zero_grad()
    loss=0
    hidden=model.init_hidden()
    sys.stdout.write("Predicted String:")
    for input,label in zip(inputs,labels):
        #print(input.size(),label.size())
        hidden,output=model(input,hidden)
        val,idx=output.max(1)
        sys.stdout.write(idx2char[idx.data[0]])
        loss+=criterion(output,label)
    print(",epoch:%d,loss:%1.3f"%(epoch+1,loss.data[0]))
    loss.backward()
    optimizer.step()

当我运行train.py时，收到此错误：

self.rnn = nn.RNN（input_size = input_size，hidden_size = hidden_size，batch_first =真） NameError：名称'input_size'未定义

我不知道为什么会收到此错误，因为我在代码的上面一行中有input_size=5。有人能帮助我吗？感谢。

Answer 1

train.py（num_classes，input_size，...）中定义的变量范围是train.py本身。它们仅在此文件中可见。 model.py没有注意到这些。我建议在构造函数中包含这些参数：

class Model(nn.Module):
  def __init__(self, hidden_size, input_size):
    # same

然后将模型称为：

model = Model(hidden_size, input_size)

同样，对于您在train.py中定义的其他变量（并希望在model.py中使用它们），您必须将它们作为参数传递给它们各自的函数，或者传递给构造函数并存储它们作为属性。

在Pytorch训练RNN

1 个答案: