optimizer = torch.optim.RMSprop(net.parameters(), lr=lr)
# Define loss function
loss_fn = nn.CrossEntropyLoss()
# Start training
for epoch in range(args.num_epochs):
if ((epoch + 1)%100 == 0):
print('##################################')
print('## EPOCH %d' % (epoch + 1))
print('##################################')
# Iterate batches
for batch_sample in dataloader:
# Extract batch
batch_onehot = batch_sample['encoded_onehot'].to(device)
# Update network
batch_loss = train_batch(net, batch_onehot, loss_fn, optimizer)
if ((epoch + 1)%100 == 0):
print('\t Training loss (single batch):', batch_loss)
loss_temp.append(batch_loss)
if (np.mean(loss_temp)>4):
flag=False
# Compare with the previous results
loss_avg = np.mean(np.array(loss_temp))
if (loss_avg<=loss_opt):
loss_opt = loss_avg
lr_opt = lr
crop_len_opt = crop_len
batchsize_opt = batchsize
hidden_units_opt = hidden_units
# Start training
for epoch in range(args.num_epochs):
start = time.time()
if epoch % verbose == 0:
print('\n##################################')
print('## EPOCH %d' % (epoch + 1))
print('##################################')
b_losses = []
# Iterate batches
for batch_sample in dataloader:
# Extract batch
batch_onehot = batch_sample['encoded_onehot'].to(device)
if batch_onehot.shape[0] != validation_batch.shape[0]:
# Update network
batch_loss, out, y_true = network.train_batch(
net, batch_onehot, loss_fn, optimizer)
b_losses.append(batch_loss)
with torch.no_grad():
y_validation = validation_batch[:, -1, :]
# Remove the labels from the input tensor
val_input = validation_batch[:, :-1, :]
validation_pred, _ = net(val_input)
### Update network
# Evaluate loss only for last output
loss_val = loss_fn(validation_pred[:, -1, :], y_validation)
val_loss.append(loss_val)
train_loss.append(torch.mean(torch.tensor(b_losses)))
print('Avarage loss: {}'.format(torch.mean(torch.tensor(b_losses))),
end='\t')
