For my project , i’m trying to predict the ratings that a user will give to an unseen movie, based on the ratings he gave to other movies. I’m using the movielens dataset .The Main folder, which is ml-100k contains informations about 100,000 movies .
Before processing the data, the main data (ratings data) contains user ID, movie ID, user rating from 0 to 5 and timestamps (not considered for this project).I then split the data into Training set(80%) and test data(20%) using sklearn Library.
To create the recommendation systems, the model ‘ Stacked-Autoencoder ’ is being used. I’m using PyTorch and the code is implemented on Google Colab . The project is based on this https://towardsdatascience.com/stacked-auto-encoder-as-a-recommendation-system-for-movie-rating-prediction-33842386338
I’m new to deep Learning and I want to compare this model(Stacked_Autoencoder) to another Deep Learning model. For Instance,I want to use Multilayer Perception(MLP) . This is for research purposes. This is the code below for creating Stacked-Autoencoder model and training the model.
### 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)
)
If I want to train using the MLP model. How can I implement this class model? Also, What other deep learning model(Beside MLP) that I can use to compare with Stacked-Autoencoder?
Thanks.