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import time
import numpy as np
import torch
import tqdm
def train(model, device, train_loader, optimizer, epoch, log_interval):
model.train()
losses = []
accuracy = 0
for batch_idx, (x, y) in enumerate(tqdm.tqdm(train_loader)):
x, y = x.to(device), y.to(device)
optimizer.zero_grad()
out = model(x)
loss = model.loss(out, y)
with torch.no_grad():
pred = torch.argmax(out, dim=1)
accuracy += torch.sum((pred == y))
losses.append(loss.item())
loss.backward()
optimizer.step()
if batch_idx % log_interval == 0:
print('{} Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
time.ctime(time.time()),
epoch, batch_idx * len(x), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
accuracy_mean = (100. * accuracy) / len(train_loader.dataset)
return np.mean(losses), accuracy_mean.item()
def test(model, device, test_loader, log_interval=None):
model.eval()
losses = []
accuracy = 0
with torch.no_grad():
for batch_idx, (x, y) in enumerate(tqdm.tqdm(test_loader)):
x, y = x.to(device), y.to(device)
out = model(x)
test_loss_on = model.loss(out, y).item()
losses.append(test_loss_on)
pred = torch.argmax(out, dim=1)
accuracy += torch.sum((pred == y))
if log_interval is not None and batch_idx % log_interval == 0:
print('{} Test: [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
time.ctime(time.time()),
batch_idx * len(x), len(test_loader.dataset),
100. * batch_idx / len(test_loader), test_loss_on))
test_loss = np.mean(losses)
accuracy_mean = (100. * accuracy) / len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} , ({:.4f})%\n'.format(
test_loss, accuracy, len(test_loader.dataset), accuracy_mean))
return test_loss, accuracy_mean.item()
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