# Keep track of loss
print_loss_total += loss
plot_loss_total += loss
if epoch == 0:
continue
if epoch % print_every == 0:
print_loss_avg = print_loss_total / print_every
print_loss_total = 0
time_since = helpers.time_since(start, epoch / n_epochs)
print('%s (%d %d%%) %.4f' %
(time_since, epoch, epoch / n_epochs * 100, print_loss_avg))
if epoch % plot_every == 0:
plot_loss_avg = plot_loss_total / plot_every
plot_losses.append(plot_loss_avg)
plot_loss_total = 0
# Save our models
torch.save(encoder.state_dict(),
'../data/encoder_params_{}'.format(args.language))
torch.save(decoder.state_dict(),
'../data/decoder_params_{}'.format(args.language))
torch.save(decoder.attention.state_dict(),
'../data/attention_params_{}'.format(args.language))
# Plot loss
helpers.show_plot(plot_losses)
'number': 0,
'total_payment': 0
},
'C#': {
'number': 0,
'total_payment': 0
},
'Java': {
'number': 0,
'total_payment': 0
},
'PHP': {
'number': 0,
'total_payment': 0
},
'Other': {
'number': 0,
'total_payment': 0
}
}
vacancies = work_with_file.read_from_file(
input('Please input path to a source file: '))
helpers.add_lang_to_vacancies(vacancies, languages)
helpers.count_vacancies_per_lang(vacancies, languages)
helpers.add_total_payment_per_lang(vacancies, languages)
helpers.avg_payment_per_lang(languages)
helpers.print_stats(languages)
helpers.show_plot(languages)
# )
# )
message = "epoch: {} current loss: {:.4f} time taken: {}s".format(
epoch, loss_contrastive.item(),
str(time.time() - start)[:5])
try:
slack_callback.write(message)
except Exception as e:
pass
print("epoch: {} current loss: {:.4f} time taken: {}s".format(
epoch, loss_contrastive.item(),
str(time.time() - start)[:5]))
# iteration_number += 10
counter.append(epoch)
loss_history.append(loss_contrastive.item())
if epoch % 50 == 0 and epoch > 0:
state_dict = {
"epoch": epoch + 1,
"state_dict": net.state_dict(),
"optim_dict": optimizer.state_dict(),
"loss_history": loss_history,
"counter": counter,
}
torch.save(state_dict, f"models/model_state_dict_{epoch}.pt")
print()
print(f"model checkpoint saved to models/model_state_dict_{epoch}")
show_plot(counter, loss_history, save=True)