def train(encoder_decoder: EncoderDecoder, train_data_loader: DataLoader,
model_name, val_data_loader: DataLoader, keep_prob,
teacher_forcing_schedule, lr, max_length, use_decay, data_path):
global_step = 0
loss_function = torch.nn.NLLLoss(ignore_index=0)
optimizer = optim.Adam(encoder_decoder.parameters(), lr=lr)
model_path = './saved/' + model_name + '/'
if (use_decay == False):
gamma = 1.0
else:
gamma = 0.5
scheduler = StepLR(optimizer, step_size=1, gamma=gamma)
#val_loss, val_bleu_score = evaluate(encoder_decoder, val_data_loader)
best_bleu = 0.0
for epoch, teacher_forcing in enumerate(teacher_forcing_schedule):
#scheduler.step()
print('epoch %i' % (epoch), flush=True)
print('lr: ' + str(scheduler.get_lr()))
for batch_idx, (input_idxs, target_idxs, input_tokens,
target_tokens) in enumerate(tqdm(train_data_loader)):
# input_idxs and target_idxs have dim (batch_size x max_len)
# they are NOT sorted by length
'''
print(input_idxs[0])
print(input_tokens[0])
print(target_idxs[0])
print(target_tokens[0])
'''
lengths = (input_idxs != 0).long().sum(dim=1)
sorted_lengths, order = torch.sort(lengths, descending=True)
input_variable = Variable(input_idxs[order, :][:, :max(lengths)])
target_variable = Variable(target_idxs[order, :])
optimizer.zero_grad()
output_log_probs, output_seqs = encoder_decoder(
input_variable,
list(sorted_lengths),
targets=target_variable,
keep_prob=keep_prob,
teacher_forcing=teacher_forcing)
batch_size = input_variable.shape[0]
flattened_outputs = output_log_probs.view(batch_size * max_length,
-1)
batch_loss = loss_function(flattened_outputs,
target_variable.contiguous().view(-1))
batch_loss.backward()
optimizer.step()
batch_outputs = trim_seqs(output_seqs)
batch_targets = [[list(seq[seq > 0])]
for seq in list(to_np(target_variable))]
#batch_bleu_score = corpus_bleu(batch_targets, batch_outputs, smoothing_function=SmoothingFunction().method2)
batch_bleu_score = corpus_bleu(batch_targets, batch_outputs)
'''
if global_step < 10 or (global_step % 10 == 0 and global_step < 100) or (global_step % 100 == 0 and epoch < 2):
input_string = "Amy, Please schedule a meeting with Marcos on Tuesday April 3rd. Adam Kleczewski"
output_string = encoder_decoder.get_response(input_string)
writer.add_text('schedule', output_string, global_step=global_step)
input_string = "Amy, Please cancel this meeting. Adam Kleczewski"
output_string = encoder_decoder.get_response(input_string)
writer.add_text('cancel', output_string, global_step=global_step)
'''
if global_step % 100 == 0:
writer.add_scalar('train_batch_loss', batch_loss, global_step)
writer.add_scalar('train_batch_bleu_score', batch_bleu_score,
global_step)
for tag, value in encoder_decoder.named_parameters():
tag = tag.replace('.', '/')
writer.add_histogram('weights/' + tag,
value,
global_step,
bins='doane')
writer.add_histogram('grads/' + tag,
to_np(value.grad),
global_step,
bins='doane')
global_step += 1
debug = False
if (debug):
if batch_idx == 5:
break
val_loss, val_bleu_score = evaluate(encoder_decoder, val_data_loader)
writer.add_scalar('val_loss', val_loss, global_step=global_step)
writer.add_scalar('val_bleu_score',
val_bleu_score,
global_step=global_step)
encoder_embeddings = encoder_decoder.encoder.embedding.weight.data
encoder_vocab = encoder_decoder.lang.tok_to_idx.keys()
writer.add_embedding(encoder_embeddings,
metadata=encoder_vocab,
global_step=0,
tag='encoder_embeddings')
decoder_embeddings = encoder_decoder.decoder.embedding.weight.data
decoder_vocab = encoder_decoder.lang.tok_to_idx.keys()
writer.add_embedding(decoder_embeddings,
metadata=decoder_vocab,
global_step=0,
tag='decoder_embeddings')
'''
input_string = "Amy, Please schedule a meeting with Marcos on Tuesday April 3rd. Adam Kleczewski"
output_string = encoder_decoder.get_response(input_string)
writer.add_text('schedule', output_string, global_step=global_step)
input_string = "Amy, Please cancel this meeting. Adam Kleczewski"
output_string = encoder_decoder.get_response(input_string)
writer.add_text('cancel', output_string, global_step=global_step)
'''
calc_bleu_score = get_bleu(encoder_decoder, data_path, None, 'dev')
print('val loss: %.5f, val BLEU score: %.5f' %
(val_loss, calc_bleu_score),
flush=True)
if (calc_bleu_score > best_bleu):
print("Best BLEU score! Saving model...")
best_bleu = calc_bleu_score
torch.save(
encoder_decoder, "%s%s_%i_%.3f.pt" %
(model_path, model_name, epoch, calc_bleu_score))
print('-' * 100, flush=True)
scheduler.step()
def train(encoder_decoder: EncoderDecoder, train_data_loader: DataLoader,
model_name, val_data_loader: DataLoader, keep_prob,
teacher_forcing_schedule, lr, max_length, device,
test_data_loader: DataLoader):
global_step = 0
loss_function = torch.nn.NLLLoss(ignore_index=0)
optimizer = optim.Adam(encoder_decoder.parameters(), lr=lr)
model_path = './model/' + model_name + '/'
trained_model = encoder_decoder
for epoch, teacher_forcing in enumerate(teacher_forcing_schedule):
print('epoch %i' % epoch, flush=True)
correct_predictions = 0.0
all_predictions = 0.0
for batch_idx, (input_idxs, target_idxs, input_tokens,
target_tokens) in enumerate(tqdm(train_data_loader)):
# Empty the cache at each batch
torch.cuda.empty_cache()
# input_idxs and target_idxs have dim (batch_size x max_len)
# they are NOT sorted by length
lengths = (input_idxs != 0).long().sum(dim=1)
sorted_lengths, order = torch.sort(lengths, descending=True)
input_variable = input_idxs[order, :][:, :max(lengths)]
input_variable = input_variable.to(device)
target_variable = target_idxs[order, :]
target_variable = target_variable.to(device)
optimizer.zero_grad()
output_log_probs, output_seqs = encoder_decoder(
input_variable,
list(sorted_lengths),
targets=target_variable,
keep_prob=keep_prob,
teacher_forcing=teacher_forcing)
batch_size = input_variable.shape[0]
output_sentences = output_seqs.squeeze(2)
flattened_outputs = output_log_probs.view(batch_size * max_length,
-1)
batch_loss = loss_function(flattened_outputs,
target_variable.contiguous().view(-1))
batch_outputs = trim_seqs(output_seqs)
batch_inputs = [[list(seq[seq > 0])]
for seq in list(to_np(input_variable))]
batch_targets = [[list(seq[seq > 0])]
for seq in list(to_np(target_variable))]
for i in range(len(batch_outputs)):
y_i = batch_outputs[i]
tgt_i = batch_targets[i][0]
if y_i == tgt_i:
correct_predictions += 1.0
all_predictions += 1.0
batch_loss.backward()
optimizer.step()
batch_bleu_score = corpus_bleu(
batch_targets,
batch_outputs,
smoothing_function=SmoothingFunction().method1)
if global_step % 100 == 0:
writer.add_scalar('train_batch_loss', batch_loss, global_step)
writer.add_scalar('train_batch_bleu_score', batch_bleu_score,
global_step)
for tag, value in encoder_decoder.named_parameters():
tag = tag.replace('.', '/')
writer.add_histogram('weights/' + tag,
value,
global_step,
bins='doane')
writer.add_histogram('grads/' + tag,
to_np(value.grad),
global_step,
bins='doane')
global_step += 1
encoder_embeddings = encoder_decoder.encoder.embedding.weight.data
encoder_vocab = encoder_decoder.lang.tok_to_idx.keys()
writer.add_embedding(encoder_embeddings,
metadata=encoder_vocab,
global_step=0,
tag='encoder_embeddings')
decoder_embeddings = encoder_decoder.decoder.embedding.weight.data
decoder_vocab = encoder_decoder.lang.tok_to_idx.keys()
writer.add_embedding(decoder_embeddings,
metadata=decoder_vocab,
global_step=0,
tag='decoder_embeddings')
print('training accuracy %.5f' %
(100.0 * (correct_predictions / all_predictions)))
torch.save(encoder_decoder,
"%s%s_%i.pt" % (model_path, model_name, epoch))
trained_model = encoder_decoder
print('-' * 100, flush=True)
torch.save(encoder_decoder, "%s%s_final.pt" % (model_path, model_name))
return trained_model