writer = SummaryWriter()
global_train_step = 0
global_val_step = 0
# Loop over epochs
for epoch in range(max_epochs):
tqdm.write("Epoch: {}".format(epoch))
progress_bar = tqdm(total=len(train_dataset), leave=True, position=0)
# Training
for local_batch, local_labels in training_generator:
# Transfer to GPU
local_batch, local_labels = local_batch.to(device), local_labels.to(
device)
loss, yhat = model.step(local_batch, local_labels)
yhat = model.softmax(yhat)
yhat = torch.max(yhat, 1)[1].cpu()
acc = pred_acc(local_labels.cpu(), yhat)
writer.add_scalar('Train/Accuracy', acc, global_train_step)
writer.add_scalar('Train/Loss', loss, global_train_step)
global_train_step += params['batch_size']
progress_bar.update(params['batch_size'])
progress_bar.set_postfix(loss=loss)
tqdm.write("Running validation...")
# Validation
y_pred = []
y_true = []
with torch.set_grad_enabled(False):
