Created
February 8, 2019 02:13
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# loss stats | |
if counter % print_every == 0: | |
# Get validation loss | |
val_h = net.init_hidden(batch_size) | |
val_losses = [] | |
net.eval() | |
for x, y in get_batches(val_data, batch_size, seq_length): | |
# One-hot encode our data and make them Torch tensors | |
x = one_hot_encode(x, n_chars) | |
x, y = torch.from_numpy(x), torch.from_numpy(y) | |
# Creating new variables for the hidden state, otherwise | |
# we'd backprop through the entire training history | |
val_h = tuple([each.data for each in val_h]) | |
inputs, targets = x, y | |
if(train_on_gpu): | |
inputs, targets = inputs.cuda(), targets.cuda() | |
output, val_h = net(inputs, val_h) | |
val_loss = criterion(output, targets.view(batch_size*seq_length).long()) | |
val_losses.append(val_loss.item()) | |
net.train() # reset to train mode after iterationg through validation data | |
print("Epoch: {}/{}...".format(e+1, epochs), | |
"Step: {}...".format(counter), | |
"Loss: {:.4f}...".format(loss.item()), | |
"Val Loss: {:.4f}".format(np.mean(val_losses))) |
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