运行时错误:索引 0 处的掩码 [1682] 形状与索引 0 处索引张量 [1， 1682] 的形状不匹配

Question

我正在设计一个堆叠的自动编码器，试图训练我的神经网络对电影评级，如果用户不对任何电影进行评级，它就不会考虑它

我的训练集运行完美，但是当我运行测试集时，它向我显示此错误

运行时错误：索引 0 处的掩码 [1682] 形状与索引 0 处索引张量 [1， 1682] 的形状不匹配我在最后的测试块上遇到错误，我已经在那里发表了评论

法典：-

---

# Auto Encoder

import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data
from torch.autograd import Variable

# Importing dataset
movies= pd.read_csv('ml-1m/movies.dat',sep ='::', header= None,engine ='python', encoding= 'latin-1')
users= pd.read_csv('ml-1m/users.dat',sep ='::', header= None,engine ='python', encoding= 'latin-1')
ratings = pd.read_csv('ml-1m/ratings.dat',sep ='::', header= None,engine ='python', encoding= 'latin-1')
# preparing the training set and the dataset
training_set =pd.read_csv('ml-100k/u1.base',delimiter ='t')
training_set =np.array(training_set, dtype= 'int')
test_set =pd.read_csv('ml-100k/u1.test',delimiter ='t')
test_set =np.array(test_set, dtype= 'int')

# Getting the number of users and  movies
# we are taking the maximum no of values from training set and test set 
nb_users = int(max(max(training_set[:,0]), max(test_set[:,0])))
nb_movies = int(max(max(training_set[:,1]), max(test_set[:,1])))
# converting the data into an array within users in lines and movies in columns
def convert(data):
    new_data = []
    for id_users in range(1, nb_users +1):
        id_movies = data[:,1][data[:,0]==id_users]#movies id from data
        id_ratings = data[:,2][data[:,0]==id_users] #ratings
        ratings= np.zeros(nb_movies)
        ratings[id_movies-1] = id_ratings  # -1 for making it start from 1
        new_data.append(list(ratings))
    return new_data

training_set =convert(training_set)
test_set =convert(test_set)
# Converting the data into Torch tensor
training_set = torch.FloatTensor(training_set)
test_set = torch.FloatTensor(test_set)

# creating the architecture of the neural network
class SAE(nn.Module):
    def  __init__(self, ): # after comma it will consider parameters of module ie parent class
        super(SAE,self).__init__()#parent class inheritence
        self.fc1 = nn.Linear(nb_movies, 20)  #20 nodes in hidden layer
        self.fc2= nn.Linear(20,10)
        self.fc3 = nn.Linear(10,20)  #decoding 
        self.fc4= nn.Linear(20, nb_movies) #decoding
        self.activation= nn.Sigmoid()
            #self.myparameters= nn.ParameterList(self.fc1,self.fc2,self.fc3,self.fc4,self.activation)
    def forward(self, x): 
        x=self.activation(self.fc1(x))#encoding
        x=self.activation(self.fc2(x))#encoding
        x=self.activation(self.fc3(x)) #decoding
        x=self.fc4(x) #last layer machine understand automaically
        return x
sae= SAE()
criterion = nn.MSELoss()
optimizer= optim.RMSprop(sae.parameters(), lr= 0.01 , weight_decay =0.5)
# Training the SAE
nb_epoch = 200
for epoch in range(1, nb_epoch + 1):
    train_loss = 0
    s = 0.
    for id_user in range(nb_users):
        input = Variable(training_set[id_user]).unsqueeze(0)
        target = input.clone()
        if torch.sum(target.data > 0) > 0:
            output = sae(input)
            target.require_grad = False
            output[target == 0] = 0     
            loss = criterion(output, target)
            mean_corrector = nb_movies/float(torch.sum(target.data > 0) + 1e-10)
            loss.backward()
            train_loss += np.sqrt(loss.data.item()*mean_corrector)
            s += 1.
            optimizer.step()
    print('epoch: '+str(epoch)+' loss: '+str(train_loss/s))

# Testing the SAE
test_loss = 0
s = 0.
for id_user in range(nb_users):
    input = Variable(training_set[id_user]).unsqueeze(0)
    target = Variable(test_set[id_user])
    if torch.sum(target.data > 0) > 0:
        output = sae(input)
        target.require_grad = False
        output[target == 0] = 0      # I get error at this line
        loss = criterion(output, target)
        mean_corrector = nb_movies/float(torch.sum(target.data > 0) + 1e-10)
        test_loss += np.sqrt(loss.data.item()*mean_corrector)
        s += 1.
print('test loss: '+str(test_loss/s))

Answer 1

更改：

output[target == 0] = 0      # I get error at this line

至：

output[(target == 0).unsqueeze(0)] = 0

原因：

target == 0返回的torch.Tensor的形状为 [1682]。

(target == 0).unsqueeze(0)会将其转换为[1, 1682]

Answer 2

如果你在训练你的 SAE 中查看，目标是输入的克隆，它通过 .unsqueeze（0） 函数增加了一个维度。

如果您在测试 SAE 中查看，则目标没有添加的维度，因此请按如下方式修改代码

改变
目标 = 变量（test_set[id_user]）

自
target = 变量（test_set[id_user]）.unsqueeze（0）

这样它就可以让你的目标具有张量所需的多个暗淡。