recsys8.py

# create a model object
# y_range has been extended(0-11) than required(1-10) to make the
# values lie in the linear region of the sigmoid function
model = EmbeddingModel(10, len(users), len(items), [0,11], initialise = 0.01).cuda()

# split the data, returns a list [train, valid]
data = get_data(ratings, 0.1)

# loss = mean((target_rating - predicted_rating)**2)
loss_function = nn.MSELoss()

# optimizer function will update the weights of the Neural Net
optimizer = optim.SGD(model.parameters(), lr=0.05, momentum=0.9)

# batch size for each input
bs = 128