问创建MLP模型来预测用户使用PyTorch给未看过的电影的评分
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Stack Overflow用户

提问于 2020-07-10 09:02:57

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在我的项目中，我试图根据用户给其他电影的评分来预测用户对一部看不见的电影的评分。我使用的是movielens dataset.The主文件夹，它是ml-100k，包含关于100,000 的信息。

在对数据进行处理之前，主要数据(分级数据)包含用户ID、电影ID、用户评等(从0到5，以及该项目考虑的).I，然后使用sklearn库将数据拆分为培训集(80%)和测试数据(20%)。

为了创建推荐系统，正在使用模型‘Stacked-Autoencoder’。我使用的是PyTorch，代码是在Google 上实现的。这个项目是基于这个https://towardsdatascience.com/stacked-auto-encoder-as-a-recommendation-system-for-movie-rating-prediction-33842386338

我刚开始深造，我想把这个模型(Stacked_Autoencoder)和另一个深度学习模式进行比较。例如，我想使用多层感知(MLP)。这是为了研究目的。这是下面的代码，用于创建堆叠-自动编码器模型和训练模型。

### Part 1 : Archirecture of the AutoEncoder 

#nn.Module is a parent class 
# SAE is a child class of the parent class nn.Module
class SAE(nn.Module): 
# self is the object of the SAE class 

# Archirecture 
    def __init__(self, ): 
    # self can use alll the methods of the class nn.Module
        super(SAE,self).__init__()
    # Full connected layer  n°1, input and 20 neurons-nodes of the first layer
    # one neuron can be the genre of the movie
    
    # Encode step 
        self.fc1 = nn.Linear(nb_movies,20)
    # Full connected layer n°2 
        self.fc2 = nn.Linear(20,10)
    
    # Decode step 
    # Full connected layer n°3
        self.fc3 = nn.Linear(10,20) 
    # Full connected layer n°4
        self.fc4 = nn.Linear(20,nb_movies) 
    # Sigmoid activation function 
        self.activation = nn.Sigmoid()

# Action : activation of the neurons
def forward(self, x) : 
        x = self.activation(self.fc1(x))
        x = self.activation(self.fc2(x))
        x = self.activation(self.fc3(x))
        # dont's use the activation function 
        # use the linear function only 
        x = self.fc4(x)
        # x is th evector of predicted ratings
        return x 

# Create the AutoEncoder object 
sae=SAE()
#MSE Loss : imported from torch.nn 
criterion=nn.MSELoss() 
# RMSProp optimizer (update the weights) imported from torch.optim 
#sea.parameters() are weights and bias adjusted during the training
optimizer=optim.RMSProp(sae.parameters(),lr=0.01, weight_decay=0.5)

### Part 2 : Training of the SAE 
# number of epochs 
nb_epochs = 200 
# Epoch forloop 
for epoch in range(1, nb_epoch+1): 
        # at the beginning the loss is at zero
        s=0.
        train_loss = 0 

        #Users forloop 
        for id_user in range(nb_users)
            # add one dimension to make a two dimension vector.
            # create a new dimension and put it the first position .unsqueeze[0]
            input = Variable(training_set[id_user].unsqueeze[0])
            
            # clone the input to obtain the target  
            target= input.clone()
            
            # target.data are all the ratings 
            # ratings > 0
            if torch.sum(target.data >0) > 0
                output = sae(input)
                # don't compute the gradients regarding the target
                target.require_grad=False 
                # only deal with true ratings 
                output[target==0]=0
                
                # Loss Criterion 
                loss =criterion(output,target)
                
                # Average the error of the movies that don't have zero ratings
                mean_corrector=nb_movies/float(torch.sum(target.data>0)+1e-10)
                
                # Direction of the backpropagation 
                loss.backward()
                train_loss+=np.sqrt(loss.data[0]*mean_corrector)
                s+=1.
                
                # Intensity of the backpropagation 
                optimizer.step()
        
    print('epoch:' +str (epoch)+'loss:' +str(train_loss/s)