def get_validation_metrics(model: NeuralMachineTranslator,
iterations: int,
training_evaluator: Evaluator,
validation_evaluator: Evaluator,
training_iterator: BucketIterator,
validation_iterator: BucketIterator) -> Tuple[Metrics, Metrics]:
# get predictor
predictor = Predictor(model)
validation_evaluator.clear_sentences()
# loop over validation sentences and add predictions to evaluator
for i in range(iterations):
validation_batch = next(iter(validation_iterator))
predicted_sentence, _ = predictor.predict(validation_batch)
validation_evaluator.add_sentences(validation_batch.trg[0], predicted_sentence, model.EOS)
# get validation metrics
validation_metrics = Metrics(validation_evaluator.bleu(), validation_evaluator.ter(), 0)
training_evaluator.clear_sentences()
# get 50 batches from training data and add predictions to evaluator
for i in range(50):
batch = next(iter(training_iterator))
predicted_sentence, _ = predictor.predict(batch)
training_evaluator.add_sentences(batch.trg[0], predicted_sentence, model.EOS)
# get training metrics
training_metrics = Metrics(training_evaluator.bleu(), training_evaluator.ter(), 0)
return validation_metrics, training_metrics
training_batches = next(
iter(
BucketIterator(
dataset=training_data,
batch_size=1,
train=True,
sort_key=lambda x: interleave_keys(len(x.src), len(x.trg)))))
predictor = Predictor(model)
evaluator = Evaluator(training_data.english.vocab,
training_data.french.vocab)
# evaluator.add_sentences(input_data.trg[0], predictor.predict(input_data))
for i in range((len(data) // model.batch_size) + 1):
sentence = next(iter(input_data))
predicted_sentence, _ = predictor.predict(sentence)
evaluator.add_sentences(sentence.trg[0], predicted_sentence, eos_token)
#
# for i in range((len(data) // batch_size) + 1):
# sentence = next(iter(input_data))
# src, trg = evaluator.convert_sentences(sentence)
# file.write(' '.join(src) + '\n')
# file.write(' '.join(trg) + '\n')
# file.write('\n')
print('bleu:', evaluator.bleu())
print('ter: ', evaluator.ter())
evaluator.write_to_file('results/beam_search')
def train_epochs(
training_data: ParallelData,
embedding_dimension: int,
n_epochs: int,
batch_size: int,
max_sentence_length: int,
evaluator: Evaluator,
validation_evaluator: Evaluator,
dropout=0.3,
learning_rate=0.01,
max_iterations_per_epoch=math.inf,
teacher_forcing=False
) -> Dict[int, Metrics]:
n_english = len(training_data.english.vocab)
n_french = len(training_data.french.vocab)
# iterators
train_iterator = Iterator(dataset=training_data, batch_size=batch_size,
sort_key=lambda x: interleave_keys(len(x.src), len(x.trg)), train=True)
validation_data = TestData("data/BPE/valid/val.BPE", training_data.english.vocab, training_data.french.vocab)
validation_iterations = (len(validation_data) // batch_size) + 1
validation_iterator = Iterator(dataset=validation_data, batch_size=batch_size,
sort_key=lambda x: interleave_keys(len(x.src), len(x.trg)), train=True)
iterations_per_epoch = min(max_iterations_per_epoch, (len(training_data) // batch_size) + 1)
model = NeuralMachineTranslator(
embedding_dimension,
n_french,
max_sentence_length,
dropout,
n_english,
n_english,
2*embedding_dimension,
batch_size,
training_data.english.vocab.stoi['<EOS>'],
training_data.english.vocab.stoi['<SOS>'],
training_data.english.vocab.stoi['<PAD>'],
max_prediction_length=max_sentence_length
)
model.train()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)
print("Parameters to train: ")
for name, param in model.named_parameters():
if param.requires_grad:
print(name)
print()
metrics = {}
validation_metrics = {}
training_metrics = {}
print("Start training..")
for epoch in range(1, n_epochs + 1):
epoch_loss = 0
iteration_loss = 0
start_time = time()
for iteration in range(iterations_per_epoch):
# set gradients to zero
optimizer.zero_grad()
model.zero_grad()
# get next batch
batch = next(iter(train_iterator))
# forward pass
prediction, loss = train(batch, model, teacher_forcing)
# # backward pass final step without retaining graph
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 5.)
# update parameters final step
optimizer.step()
# save losses and add predicted sentences to evaluator
epoch_loss += loss.item()
iteration_loss += loss.item()
evaluator.add_sentences(batch.trg[0], prediction, model.EOS)
if iteration > 1 and iteration % 200 == 0:
current_time = (time() - start_time) / 200
print('batch {}/{}'.format(iteration, iterations_per_epoch))
print('average loss per batch: {:5.3}'.format(iteration_loss / 200))
print("time per batch {:3}".format(current_time))
iteration_loss = 0
start_time = time()
# save evaluation metrics
metrics[epoch] = Metrics(evaluator.bleu(), evaluator.ter(), float(epoch_loss))
evaluator.write_to_file('output/predictions_epoch{}'.format(epoch))
# clear sentences from evaluator
evaluator.clear_sentences()
print(
'Epoch {}: training metrics: loss {:.3}, BLEU {:.3}, TER {:.3}, LR {:.3}'.format(
epoch, float(metrics[epoch].loss), float(metrics[epoch].BLEU), float(metrics[epoch].TER), float(learning_rate)
)
)
print("Getting validation metrics..")
validation_metrics[epoch], training_metrics[epoch] = get_validation_metrics(
model,
validation_iterations,
evaluator,
validation_evaluator,
train_iterator,
validation_iterator
)
# clear sentences out of evaluators
evaluator.clear_sentences()
validation_evaluator.clear_sentences()
print(
'Epoch {}: validation metrics: BLEU {:.3}, TER {:.3}'.format(
epoch, float(validation_metrics[epoch].BLEU), float(validation_metrics[epoch].TER)
)
)
if epoch > 1 and metrics[epoch].loss > metrics[epoch - 1].loss:
learning_rate /= 4
optimizer = optim.SGD(model.parameters(), lr=learning_rate)
with open('training_progress.csv', 'w') as file:
filewriter = csv.writer(file)
filewriter.writerow(['Epoch', 'loss', 'BLEU', 'TER'])
for epoch, metric in metrics.items():
filewriter.writerow([epoch, metric.loss, metric.BLEU, metric.TER])
with open('validation_progress.csv', 'w') as file:
filewriter = csv.writer(file)
filewriter.writerow(['Epoch', 'training BLEU', 'valid BLEU', 'training TER', 'valid TER'])
for epoch, metric in validation_metrics.items():
filewriter.writerow([epoch, training_metrics[epoch].BLEU, validation_metrics[epoch].BLEU,
training_metrics[epoch].TER, validation_metrics[epoch].TER])
with open('output/model_epoch{}.pickle'.format(epoch), 'wb') as file:
pickle.dump(model, file)
return metrics