def trainIters(encoder, decoder, n_iters, print_every=1000, plot_every=100, learning_rate=0.01):
start = time.time()
plot_losses = []
print_loss_total = 0 # Reset every print_every
plot_loss_total = 0 # Reset every plot_every
encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)
training_pairs = [util.tensorsFromPair(random.choice(pairs), input_lang, output_lang) \
for i in range(n_iters)]
criterion = nn.NLLLoss()
for iter in range(1, n_iters + 1):
training_pair = training_pairs[iter - 1]
input_tensor = training_pair[0]
target_tensor = training_pair[1]
loss = train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion)
print_loss_total += loss
plot_loss_total += loss
if iter % print_every == 0:
print_loss_avg = print_loss_total / print_every
print_loss_total = 0
print('%s (%d %d%%) %.4f' % (util.timeSince(start, iter / n_iters),
iter, iter / n_iters * 100, print_loss_avg))
if iter % plot_every == 0:
plot_loss_avg = plot_loss_total / plot_every
plot_losses.append(plot_loss_avg)
plot_loss_total = 0
showPlot(plot_losses)
def train(dataset : SequenceSequenceDataset, hidden_size : int,
learning_rate : float, num_encoder_layers : int,
num_decoder_layers : int, max_length : int, num_epochs : int, batch_size : int,
print_every : int, context_vocab_size : int, tactic_vocab_size : int) -> Iterable[Checkpoint]:
print("Initializing PyTorch...")
in_stream = [inputFromSentence(datum[0], max_length) for datum in dataset]
out_stream = [inputFromSentence(datum[1], max_length) for datum in dataset]
data_loader = data.DataLoader(data.TensorDataset(torch.LongTensor(out_stream),
torch.LongTensor(in_stream)),
batch_size=batch_size, num_workers=0,
shuffle=True, pin_memory=True,
drop_last=True)
encoder = EncoderRNN(context_vocab_size, hidden_size, num_encoder_layers,
batch_size=batch_size)
decoder = DecoderRNN(hidden_size, tactic_vocab_size, num_decoder_layers,
batch_size=batch_size)
encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)
optimizers = [encoder_optimizer, decoder_optimizer]
criterion = maybe_cuda(nn.NLLLoss())
start = time.time()
num_items = len(dataset) * num_epochs
total_loss = 0
print("Training...")
for epoch in range(num_epochs):
print("Epoch {}".format(epoch))
adjustLearningRates(learning_rate, optimizers, epoch)
for batch_num, (output_batch, input_batch) in enumerate(data_loader):
target_length = output_batch.size()[1]
encoder_optimizer.zero_grad()
decoder_optimizer.zero_grad()
predictor_output = decoder.run_teach(encoder
.run(cast(SomeLongTensor, input_batch)),
cast(SomeLongTensor, output_batch))
loss = maybe_cuda(Variable(LongTensor(0)))
output_var = maybe_cuda(Variable(output_batch))
for i in range(target_length):
loss += criterion(predictor_output[i], output_var[:,i])
loss.backward()
encoder_optimizer.step()
decoder_optimizer.step()
total_loss += (loss.data[0] / target_length) * batch_size
if (batch_num + 1) % print_every == 0:
items_processed = (batch_num + 1) * batch_size + epoch * len(dataset)
progress = items_processed / num_items
print("{} ({} {:.2f}%) {:.4f}".
format(timeSince(start, progress),
items_processed, progress * 100,
total_loss / items_processed))
yield encoder.state_dict(), decoder.state_dict()
def trainIters(epoch,
pairs,
lang,
encoder,
decoder,
print_every=100,
plot_every=200,
learning_rate=0.001):
start = time.time()
print("Starting training")
plot_losses = []
num = len(pairs)
encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate)
criterion = nn.NLLLoss()
for epo in range(epoch):
print("epoch: ", epo + 1)
print_loss_total = 0 # Reset every print_every
plot_loss_total = 0 # Reset every plot_every
for iter in range(1, len(pairs) + 1):
training_pair = variablesFromPair(pairs[iter - 1], lang)
input_variable = training_pair[0]
target_variable = training_pair[1]
loss = train(input_variable, target_variable, encoder, decoder,
encoder_optimizer, decoder_optimizer, criterion)
print("loss: ", loss.numpy())
print_loss_total += loss
plot_loss_total += loss
if iter % print_every == 0:
print_loss_avg = print_loss_total / print_every
print_loss_total = 0
print('%s (%d %d%%) %.4f' %
(timeSince(start, iter / num), iter, iter / num * 100,
print_loss_avg))
evaluateRandomly(lang, encoder, decoder, 1)
if iter % plot_every == 0:
plot_loss_avg = plot_loss_total / plot_every
plot_losses.append(plot_loss_avg)
plot_loss_total = 0
showPlot(plot_losses)
def checkpoints(self, inputs : List[List[float]], outputs : List[int]) \
-> Iterable[NeuralPredictorState]:
print("Building tensors")
dataloader = data.DataLoader(data.TensorDataset(
torch.FloatTensor(inputs), torch.LongTensor(outputs)),
batch_size=self.batch_size,
num_workers=0,
shuffle=True,
pin_memory=True,
drop_last=True)
num_batches = int(len(inputs) / self.batch_size)
dataset_size = num_batches * self.batch_size
print("Initializing model...")
training_start = time.time()
for epoch in range(1, self.num_epochs):
self.adjuster.step()
print("Epoch {} (learning rate {:.6f})".format(
epoch, self._optimizer.param_groups[0]['lr']))
epoch_loss = 0.
for batch_num, data_batch in enumerate(dataloader, start=1):
self._optimizer.zero_grad()
input_batch, output_batch = data_batch
# with autograd.detect_anomaly():
predictionDistribution = self._model(input_batch)
output_var = maybe_cuda(Variable(output_batch))
loss = self._criterion(predictionDistribution, output_var)
loss.backward()
self._optimizer.step()
epoch_loss += loss.item()
if batch_num % self.print_every == 0:
items_processed = batch_num * self.batch_size + \
(epoch - 1) * dataset_size
progress = items_processed / (dataset_size *
self.num_epochs)
print("{} ({:7} {:5.2f}%) {:.4f}".format(
timeSince(training_start, progress), items_processed,
progress * 100, epoch_loss / batch_num))
state = self._model.state_dict()
loss = epoch_loss / num_batches
checkpoint = NeuralPredictorState(epoch, loss, state)
yield checkpoint
def train_vectors(data_path, model_file):
"""
Trains a doc2vec model from character sequences split into 3-letter
words, then saves it to a file.
"""
start = time.time()
print("getting data")
ids, sentences_ls = dp.get_paragraphs(data_path)
print("got processed data")
tagged_data = [
TaggedDocument(words=dp.preprocess_str_hp(_d).split(), tags=[str(i)])
for i, _d in sentences_ls
]
print("made ", len(sentences_ls), " sentences")
embedding = build_model(tagged_data)
print("made model in ", timeSince(start))
embedding.save(model_file)
return ids, embedding
def trainIters(encoder, decoder, pairs, input_lang, output_lang, config):
start = time.time()
plot_losses = []
print_loss_total = 0 # Reset every print_every
plot_loss_total = 0 # Reset every plot_every
encoder_optimizer = optim.SGD(encoder.parameters(),
lr=config.learning_rate)
decoder_optimizer = optim.SGD(decoder.parameters(),
lr=config.learning_rate)
training_pairs = [
variablesFromPair(random.choice(pairs), input_lang, output_lang,
config) for i in range(config.n_iters)
]
criterion = nn.NLLLoss()
for iter in range(1, config.n_iters + 1):
training_pair = training_pairs[iter - 1]
input_variable = training_pair[0]
target_variable = training_pair[1]
loss = train(input_variable, target_variable, encoder, decoder,
encoder_optimizer, decoder_optimizer, criterion, config)
print_loss_total += loss
plot_loss_total += loss
if iter % config.print_every == 0:
print_loss_avg = print_loss_total / config.print_every
print_loss_total = 0
print('%s (%d %d%%) %.4f' %
(timeSince(start, iter / config.n_iters), iter,
iter / config.n_iters * 100, print_loss_avg))
if iter % config.plot_every == 0:
plot_loss_avg = plot_loss_total / config.plot_every
plot_losses.append(plot_loss_avg)
plot_loss_total = 0
showPlot(plot_losses)
# Generate a random episode
#refactor training line
#t = time.time()
train_loss = train_batched_step(samples, model) #TODO
#print("optim time:", time.time() - t)
avg_train_loss += train_loss
counter += 1
if episode == 1 or episode % args.print_freq == 0 or episode == model.num_pretrain_episodes:
val_loss = eval_ll(val_states, model)
print(
'{:s} ({:d} {:.0f}% finished) TrainLoss: {:.4f}, ValLoss: {:.4f}'
.format(
timeSince(
start,
float(episode) / float(model.num_pretrain_episodes)),
episode,
float(episode) / float(model.num_pretrain_episodes) * 100.,
avg_train_loss / counter, val_loss),
flush=True)
avg_train_loss = 0.
counter = 0
print('gen sample stats', batchtime.items())
batchtime['max'] = 0
batchtime['mean'] = 0
batchtime['count'] = 0
if episode % args.save_freq == 0 or episode == model.num_pretrain_episodes:
model.save(path)
if episode % 10000 == 0 or episode == model.num_pretrain_episodes:
def supervised_q(args: argparse.Namespace) -> None:
replay_memory = []
with open(args.tmp_file, 'r') as f:
for idx, line in enumerate(tqdm(f, desc="Loading data")):
replay_memory.append(LabeledTransition.from_dict(json.loads(line)))
if args.max_tuples is not None:
replay_memory = replay_memory[-args.max_tuples:]
# Load the predictor
predictor = cast(
features_polyarg_predictor.FeaturesPolyargPredictor,
predict_tactic.loadPredictorByFile(args.predictor_weights))
q_estimator: QEstimator
# Create an initial Q Estimator
if args.estimator == "polyarg":
q_estimator = PolyargQEstimator(args.learning_rate, args.epoch_step,
args.gamma, predictor)
else:
q_estimator = FeaturesQEstimator(args.learning_rate, args.epoch_step,
args.gamma)
if args.start_from:
q_estimator_name, *saved = \
torch.load(args.start_from)
if args.estimator == "polyarg":
assert q_estimator_name == "polyarg evaluator", \
q_estimator_name
else:
assert q_estimator_name == "features evaluator", \
q_estimator_name
q_estimator.load_saved_state(*saved)
training_start = time.time()
training_samples = assign_scores(args,
q_estimator,
predictor,
replay_memory,
progress=True)
input_tensors = q_estimator.get_input_tensors(training_samples)
rescore_lr = args.learning_rate
for epoch in range(1, args.num_epochs + 1):
scores = torch.FloatTensor(
[score for _, _, _, score in training_samples])
batches: Sequence[Sequence[torch.Tensor]] = data.DataLoader(
data.TensorDataset(*(input_tensors + [scores])),
batch_size=args.batch_size,
num_workers=0,
shuffle=True,
pin_memory=True,
drop_last=True)
epoch_loss = 0.
eprint("Epoch {}: Learning rate {:.12f}".format(
epoch, q_estimator.optimizer.param_groups[0]['lr']),
guard=args.show_loss)
for idx, batch in enumerate(batches, start=1):
q_estimator.optimizer.zero_grad()
word_features_batch, vec_features_batch, \
expected_outputs_batch = batch
outputs = q_estimator.model(word_features_batch,
vec_features_batch)
loss = q_estimator.criterion(outputs,
maybe_cuda(expected_outputs_batch))
loss.backward()
q_estimator.optimizer.step()
q_estimator.total_batches += 1
epoch_loss += loss.item()
if idx % args.print_every == 0:
items_processed = idx * args.batch_size + \
(epoch - 1) * len(replay_memory)
progress = items_processed / (len(replay_memory) *
args.num_epochs)
eprint("{} ({:7} {:5.2f}%) {:.4f}".format(
timeSince(training_start, progress), items_processed,
progress * 100, epoch_loss * (len(batches) / idx)),
guard=args.show_loss)
q_estimator.adjuster.step()
q_estimator.save_weights(args.out_weights, args)
if epoch % args.score_every == 0 and epoch < args.num_epochs:
training_samples = assign_scores(args,
q_estimator,
predictor,
replay_memory,
progress=True)
rescore_lr *= args.rescore_gamma
q_estimator.optimizer.param_groups[0]['lr'] = rescore_lr
pass
pass
def train(self,
triplets,
n_iters,
d_steps,
d_optimizer,
g_steps,
g_optimizer,
batch_size,
max_len,
criterion,
word2index,
index2word,
embeddings_index,
embeddings_size,
print_every,
plot_every,
checkpoint_every,
to_file=False,
loss_f=None,
sample_out_f=None,
path_to_exp_out=None):
# criterion is for both G and D
# record start time for logging
begin_time = time.time()
print_d_loss_total = 0 # Reset every print_every
plot_d_loss_total = 0 # Reset every plot_every
print_g_loss_total = 0 # Reset every print_every
plot_g_loss_total = 0 # Reset every plot_every
plot_d_loss_avgs = []
plot_g_loss_avgs = []
for iter in range(1, n_iters + 1):
# train D
for d_train_idx in range(d_steps):
# 1. Train D on real+fake
self.D.zero_grad()
# 1A: Train D on real
# get data
# prepare batch
training_batch, seq_lens = get_random_batch(
triplets, batch_size)
# concat the context_ans batch with the question batch
# each element in the training batch is context + question + answer
cqa_batch, _, cqa_lens = prepare_batch_var(training_batch,
seq_lens,
batch_size,
word2index,
embeddings_index,
embeddings_size,
mode=['word'],
concat_opt='cqa')
train_input = Variable(cqa_batch[0].cuda(
)) if use_cuda else Variable(
cqa_batch[0]
) # embeddings vectors, size = [seq len x batch size x embedding dim]
d_real_decision = self.D.forward(train_input, cqa_lens[0])
real_target = Variable(torch.FloatTensor([1]*batch_size)).cuda() if use_cuda else \
Variable(torch.FloatTensor([1]*batch_size))
d_real_error = criterion(d_real_decision,
real_target) # ones = true
d_real_error.backward(
) # compute/store gradients, but don't change params
# 1B: Train D on fake
fake_cqa_batch, fake_cqa_lens = prepare_fake_batch_var(
self.G,
training_batch,
max_len,
batch_size,
word2index,
index2word,
embeddings_index,
embeddings_size,
mode=('word'))
# # sanity check: rpepare fake batch and prepare batch have the same order
# print(fake_cqa_batch[0][12] == cqa_batch[0][12])
d_fake_data = Variable(
fake_cqa_batch[0].cuda()) if use_cuda else Variable(
fake_cqa_batch[0])
d_fake_decision = self.D.forward(d_fake_data, fake_cqa_lens[0])
fake_target = Variable(torch.FloatTensor([0]*batch_size)).cuda() if use_cuda else \
Variable(torch.FloatTensor([0]*batch_size))
# d_fake_error = criterion(d_fake_decision, fake_target) # zeros = fake
# d_fake_error.backward()
# d_optimizer.step()
# accumulate loss
# FIXME I dont think below implementation works for batch version
d_error = torch.mean(d_fake_decision) - torch.mean(
d_real_decision) # W_GAN loss
# d_error = -torch.mean(self.log(1 - d_fake_decision)) - torch.mean(self.log(d_real_decision)) # GAN loss
d_error.backward()
d_optimizer.step()
# d_error = d_real_error + d_fake_error
# train G
for g_train_idx in range(g_steps):
self.G.zero_grad()
# conditional data for generator
training_batch, seq_lens = get_random_batch(
triplets, batch_size)
fake_cqa_batch, fake_cqa_lens = prepare_fake_batch_var(
self.G,
training_batch,
max_len,
batch_size,
word2index,
index2word,
embeddings_index,
embeddings_size,
mode=('word'),
detach=False)
g_fake_data = Variable(
fake_cqa_batch[0].cuda()) if use_cuda else Variable(
fake_cqa_batch[0])
dg_fake_decision = self.D.forward(g_fake_data,
fake_cqa_lens[0])
target = Variable(torch.FloatTensor([1]*batch_size).cuda()) if use_cuda else \
Variable(torch.FloatTensor([1]*batch_size))
# g_error = criterion(dg_fake_decision, target)
g_error = -torch.mean(dg_fake_decision) # wgan loss
# G_error = -torch.mean(self.log(dg_fake_decision)) # gan loss
g_error.backward()
g_optimizer.step() # Only optimizes G's parameters
# log error
print_d_loss_total += d_error.data[0]
print_g_loss_total += g_error.data[0]
plot_d_loss_total += d_error.data[0]
plot_g_loss_total += g_error.data[0]
if iter % print_every == 0:
print_d_loss_avg = print_d_loss_total / print_every
print_g_loss_avg = print_g_loss_total / print_every
print_d_loss_total = 0
print_g_loss_total = 0
if not to_file:
print('%s (%d %d%%)' %
(timeSince(begin_time, iter / float(n_iters)), iter,
iter / n_iters * 100))
# print("errors: D: real-%s/fake-%s G: %s " % ( d_real_error.data[0], d_fake_error.data[0], g_error.data[0]) )
print("errors: D: %s G: %s " %
(print_d_loss_avg, print_g_loss_avg))
print('---sample generated question---')
# sample a triple and print the generated question
evaluate(self.G, triplets, embeddings_index,
embeddings_size, word2index, index2word, max_len)
else:
sample_out_f.write(
unicode('%s (%d %d%%)\n' %
(timeSince(begin_time, iter / float(n_iters)),
iter, float(iter) / float(n_iters) * 100)))
evaluate(self.G, triplets, embeddings_index,
embeddings_size, word2index, index2word, max_len,
to_file, sample_out_f)
sample_out_f.write(unicode('\n'))
if iter % plot_every == 0:
plot_d_loss_avg = plot_d_loss_total / plot_every
plot_d_loss_avgs.append(plot_d_loss_avg)
plot_g_loss_avg = plot_g_loss_total / plot_every
plot_g_loss_avgs.append(plot_g_loss_avg)
plot_d_loss_total = 0
plot_g_loss_total = 0
if to_file:
loss_f.write(
unicode('%s (%d %d%%)\n' %
(timeSince(begin_time, iter / float(n_iters)),
iter, float(iter) / float(n_iters) * 100)))
loss_f.write(
unicode("errors: D: %s G: %s " %
(print_d_loss_avg, print_g_loss_avg)))
loss_f.write(unicode('\n'))
if (iter % checkpoint_every == 0) or (iter == n_iters):
checkpoint_fname = 'checkpoint_iter_' + str(iter) + '.pth.tar'
state = {
'iteration': iter + 1,
'd_state_dict': self.D.state_dict(),
'g_state_dict': self.G.state_dict(),
'd_optimizer': d_optimizer.state_dict(),
'g_optimizer': g_optimizer.state_dict(),
}
torch.save(state, path_to_exp_out + '/' + checkpoint_fname)
plotLoss(plot_d_loss_avgs,
plot_every,
save_path=path_to_exp_out,
f_name='d_loss_itr_' + str(iter) + '.png',
title='training loss D (monitoring purpose)',
from_file=False)
plotLoss(plot_g_loss_avgs,
plot_every,
save_path=path_to_exp_out,
f_name='g_loss_itr_' + str(iter) + '.png',
title='training loss G (monitoring purpose)',
from_file=False)
def train(dataset : List[Sentence],
token_vocab_size : int, max_length : int, hidden_size : int,
learning_rate : float, epoch_step : int, gamma : float,
num_encoder_layers : int, num_decoder_layers : int,
num_epochs : int, batch_size : int, print_every : int,
optimizer_f : Callable[..., Optimizer]) \
-> Iterable[Checkpoint]:
curtime = time.time()
print("Building pytorch dataset...", end="")
sys.stdout.flush()
data_loader = data.DataLoader(data.TensorDataset(
torch.LongTensor(dataset[:]), torch.LongTensor(dataset[:])),
batch_size=batch_size,
num_workers=0,
shuffle=True,
pin_memory=True,
drop_last=True)
print(" {:.2f}s".format(time.time() - curtime))
curtime = time.time()
print("Initializing model...", end="")
sys.stdout.flush()
encoder = maybe_cuda(
EncoderRNN(token_vocab_size,
hidden_size,
num_encoder_layers,
batch_size=batch_size))
decoder = maybe_cuda(
DecoderRNN(hidden_size,
token_vocab_size,
num_decoder_layers,
batch_size=batch_size))
encoder_optimizer = optimizer_f(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optimizer_f(decoder.parameters(), lr=learning_rate)
encoder_adjuster = scheduler.StepLR(encoder_optimizer, epoch_step, gamma)
decoder_adjuster = scheduler.StepLR(decoder_optimizer, epoch_step, gamma)
criterion = maybe_cuda(nn.NLLLoss())
print(" {:.2f}s".format(time.time() - curtime))
start = time.time()
num_items = len(dataset) * num_epochs
total_loss = 0
print("Training...")
for epoch in range(num_epochs):
print("Epoch {}".format(epoch))
# Adjust learning rates if needed
encoder_adjuster.step()
decoder_adjuster.step()
# Process batches of data
for batch_num, (input_batch, output_batch) in enumerate(data_loader):
# Reset the optimizers
encoder_optimizer.zero_grad()
decoder_optimizer.zero_grad()
# Run the autoencoder
decoded_output = \
decoder.run_teach(
encoder.run(cast(torch.LongTensor, input_batch)),
cast(torch.LongTensor, output_batch))
# Gather the losses
loss = maybe_cuda(Variable(torch.zeros(1, dtype=torch.float32)))
output_var = maybe_cuda(Variable(output_batch))
target_length = output_batch.size()[1]
for i in range(target_length):
loss += criterion(decoded_output[i], output_var[:, i])
total_loss += (loss.data.item() / target_length) * batch_size
assert total_loss == total_loss
assert isinstance(total_loss, float)
# Update the weights
loss.backward()
encoder_optimizer.step()
decoder_optimizer.step()
# Print status every once in a while
if (batch_num + 1) % print_every == 0:
items_processed = (batch_num +
1) * batch_size + epoch * len(dataset)
progress = items_processed / num_items
print("{} ({} {:.2f}%) {:.4f}".format(
timeSince(start, progress), items_processed,
progress * 100, total_loss / items_processed))
yield Checkpoint(encoder_state=encoder.state_dict(),
decoder_state=decoder.state_dict(),
training_loss=total_loss)
pass
