RuntimeError: The shape of the mask [1682] at index 0 does not match the shape of the indexed tensor [1, 1682] at index 0

Question

我正在设计一个堆叠式自动编码器，试图在电影评分方面训练我的神经网络，如果用户不对任何电影评分，它就不会考虑

我的训练集运行非常完美，但是当我运行测试集时它显示这个错误

RuntimeError：索引 0 处的掩码 [1682] 的形状与索引 0 处的索引张量 [1, 1682] 的形状不匹配我在最后的测试块中出错，我在那里评论过

代码：-

# 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

变化:

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

到:

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

原因:

target == 0返回的torch.Tensor的形状是[1682]。

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

Answer 2

如果您查看 SAE 的训练，目标是输入的克隆，它通过 .unsqueeze(0) 函数增加了维度。

如果你在测试你的SAE时，目标没有添加维度，因此修改你的代码如下

改变
目标=变量(test_set[id_user])

到
目标=变量(test_set[id_user]).unsqueeze(0)

因此您的目标具有张量所需的不止一个暗淡。

RuntimeError: The shape of the mask [1682] at index 0 does not match the shape of the indexed tensor [1, 1682] at index 0

RuntimeError: The shape of the mask [1682] at index 0 does not match the shape of the indexed tensor [1, 1682] at index 0

autoencoder

tensorflow

pytorch