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train_model.py
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train_model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset, TensorDataset
import pandas as pd
import numpy as np
import wandb
from tqdm.auto import tqdm
def train(factor_model, dataloader, optimizer, scheduler, args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
factor_model.to(device)
factor_model.train()
total_loss = 0
with tqdm(total=len(dataloader), desc="Training") as pbar:
for char_with_label, _ in dataloader:
char = char_with_label[:,:,:-1]
returns = char_with_label[:,:,-1]
inputs = char.to(device)
labels = returns[:,-1].reshape(-1,1).to(device)
inputs = inputs.float()
labels = labels.float()
optimizer.zero_grad()
loss, reconstruction, factor_mu, factor_sigma, pred_mu, pred_sigma = factor_model(inputs, labels)
total_loss += loss.item()
loss.backward()
optimizer.step()
if scheduler is not None:
scheduler.step()
pbar.set_postfix({'batch_loss': loss.item()})
pbar.update(1)
avg_loss = total_loss / len(dataloader)
return avg_loss
@torch.no_grad()
def validate(factor_model, dataloader, args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
factor_model.to(device)
factor_model.eval()
total_loss = 0
with tqdm(total=len(dataloader), desc="Validation") as pbar:
for char_with_label, _ in dataloader:
char = char_with_label[:,:,:-1]
returns = char_with_label[:,:,-1]
inputs = char.to(device)
labels = returns[:,-1].reshape(-1,1).to(device)
inputs = inputs.float()
labels = labels.float()
loss, _, _, _, _, _ = factor_model(inputs, labels)
total_loss += loss.item()
pbar.update(1)
avg_loss = total_loss / len(dataloader)
return avg_loss
@torch.no_grad()
def test(factor_model, dataloader, args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
factor_model.to(device)
factor_model.eval()
total_loss = 0
with tqdm(total=len(dataloader), desc="Validation") as pbar:
for char_with_label, _ in dataloader:
char = char_with_label[:,:,:-1]
returns = char_with_label[:,:,-1]
inputs = char.to(device)
labels = returns[:,-1].reshape(-1,1).to(device)
inputs = inputs.float()
labels = labels.float()
loss, _, _, _, _, _ = factor_model(inputs, labels)
total_loss += loss.item() * inputs.size(0)
pbar.update(1)
avg_loss = total_loss / len(dataloader)
return avg_loss