I am learning and trying to create a time series forecasting model using LSTMs. I am using the data from https://github.com/neelabhpant/Deep-Learning-in-Python/blob/master/USD_INR.csv. This is the code I have written so far.
import torch
from torch import nn
from torch.utils import data as torch_data
import torch.nn.functional as F
import pandas as pd
class PandasReader(torch_data.Dataset):
def __init__(self, data, target):
self.data = data
self.target = target
self.shape = data.shape
def __getitem__(self, ix):
row = self.data.iloc[ix]
features = torch.tensor(row.drop(self.target).values)
target = torch.tensor(row[[self.target]].values)
return features.to(device), target.to(device)
def __len__(self):
return self.data.shape[0]
class RNN(nn.Module):
def __init__(self, input_dim):
super(RNN, self).__init__()
self.lstm_1 = nn.LSTM(
input_size=input_dim, hidden_size=64, num_layers=2, batch_first=True
)
self.fc_1 = nn.Linear(in_features=64, out_features=16)
self.fc_2 = nn.Linear(in_features=16, out_features=1)
self.dropout = nn.Dropout()
self.hidden = None
def forward(self, x):
y, self.hidden = self.lstm_1(x, self.hidden)
y = self.dropout(y)
y = self.dropout(F.leaky_relu(self.fc_1(y)))
y = self.dropout(F.leaky_relu(self.fc_2(y)))
return y
def train(train_loader, val_loader, epochs):
model = RNN(train_loader.dataset.shape[1] - 1).to(device)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
for epoch in range(epochs):
train_loss = 0.0
val_loss = 0.0
for _, (features, target) in enumerate(train_loader):
x = features.unsqueeze(dim=0)
y = model.forward(x).squeeze(dim=0)
loss = criterion(y, target)
optimizer.zero_grad()
loss.backward(retain_graph=True)
train_loss += loss.item()
optimizer.step()
train_loss = train_loss / len(train_loader)
with torch.no_grad():
model.eval()
for _, (features, target) in enumerate(val_loader):
x = features.unsqueeze(dim=0)
preds = model.forward(x).squeeze(dim=0)
loss = criterion(preds, target)
val_loss += loss.item()
model.train()
val_loss = val_loss / len(val_loader)
print(f"EPOCH {epoch+1}: TRAIN LOSS - {train_loss}, VAL LOSS - {val_loss}")
return model
if __name__ == "__main__":
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("USING DEVICE:", device)
torch.set_default_tensor_type("torch.DoubleTensor")
df_train = pd.read_csv("./train.csv").drop("Date", axis="columns")
train_loader = torch_data.DataLoader(
PandasReader(df_train, target="Price"), batch_size=30, shuffle=True
)
df_val = pd.read_csv("./val.csv").drop("Date", axis="columns")
val_loader = torch_data.DataLoader(
PandasReader(df_val, target="Price"), batch_size=30, shuffle=True
)
print("TRAINING DATA SHAPE:", train_loader.dataset.shape)
print("VALIDATION DATA SHAPE:", val_loader.dataset.shape)
model = train(train_loader, val_loader, epochs=50)
The loss decreases for the first few epochs, and then it keeps bouncing up and down. I have a few questions,
-
Do I need to shift the input data whenever trying to do time series forecasting? In other words, do I need to manually modify the data so that the previous day’s data is input to the model for the current day?
-
I kept getting error
Trying to backward through the graph a second time, but the buffers have already been freed. Specify retain_graph=True when calling backward the first time.. I addedretain_graph=Truetoloss.backward(), and then it worked fine. When do I useretain_graph=True? Is there any rule-of-thumb or something for it? I read somewhere on the forums that I should set it toFalsefor the last iteration. -
Right now, it’s a stateful LSTM… and in the forward function, if I do
self.hidden=Noneas the first step, then it becomes a stateless LSTM, right?
I would appreciate any corrections and suggestions for my code.
EDIT 1: The validation set is the data of year 2017 from the linked CSV file.
. Thanks a lot!