Pytorch LSTM Prediction not learning - machine-learning

Question

I'm using a LSTM model to predict BABA stock price using this dataset: "/kaggle/input/price-volume-data-for-all-us-stocks-etfs/Data/Stocks/baba.us.txt".
I'm not sure why my model is not learning and the y_test_prediction is so different from the actual y_test. I really appreciate your help as I'm beginning to learn machine learning. Thank you!
I have scaled the data with minMaxScaler before splitting it. This is how I split the data:
x_train, y_train, x_test, y_test = [], [], [], []
lags = 3
for t in range(len(train_data)-lags-1):
x_train.append(train_data[t:(t+lags),:])
y_train.append(train_data[(t+lags),:])
for t in range(len(test_data)-lags-1):
x_test.append(test_data[t:(t+lags),:])
y_test.append(test_data[(t+lags),:])
x_train = torch.FloatTensor(np.array(x_train))
y_train = torch.FloatTensor(np.array(y_train))
x_test = torch.FloatTensor(np.array(x_test))
y_test = torch.FloatTensor(np.array(y_test))
x_train = np.reshape(x_train,(x_train.shape[0],x_train.shape[1],1))
x_test = np.reshape(x_test,(x_test.shape[0],x_test.shape[1],1))
print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)
This is my LSTM model:
input_dim = 1
hidden_layer_dim = 32
num_layers = 1
output_dim = 1
class LSTM(nn.Module):
def __init__(self, input_dim,hidden_layer_dim, num_layers, output_dim ):
super(LSTM, self).__init__()
self.input_dim = input_dim
self.hidden_layer_dim = hidden_layer_dim
self.num_layers = num_layers
self.output_dim = output_dim
self.lstm = nn.LSTM(input_dim, hidden_layer_dim,num_layers,batch_first = True)
self.fc = nn.Linear(hidden_layer_dim, output_dim)
def forward(self, x):
# initial hidden state & cell state as zeros
h0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_layer_dim))
c0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_layer_dim))
# lstm output with hidden and cell state
output, (hn, cn) = self.lstm(x, (h0,c0))
# get hidden state to be passed to dense layer
hn = hn.view(-1, self.hidden_layer_dim)
output = self.fc(hn)
return output
This is my training:
num_epochs = 100
learning_rate = 0.01
model = LSTM(input_dim,hidden_layer_dim, num_layers, output_dim)
loss = torch.nn.MSELoss() # mean-squared error for regression
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
hist = np.zeros(num_epochs)
# train model
for epoch in range(num_epochs):
outputs = model(x_train)
optimizer.zero_grad()
#get loss function
loss_fn = loss(outputs, y_train.view(1,-1))
hist[epoch] = loss_fn.item()
loss_fn.backward()
optimizer.step()
if epoch %10==0:
print("Epoch: %d, loss: %1.5f" % (epoch, hist[epoch]))
This is the training loss and prediction vs actual
training loss
prediction vs actual

Answer 1

You are initialising hidden layers every time forward is being called, which might cause errors with backprop. You do not even have to initialise them. PyTorch takes care of that for you. You can check this implementation for the details. Also, as a side note, you might want to take a look at PyTorch dataloaders(just an easier way to make splits).