def run():
print('Loading data...')
with open('data/data_emb', 'rb') as f:
all_sets, embs, word2idx = pickle.load(f)
emb_layer = nn.Embedding(embs.shape[0], embs.shape[1])
emb_layer.weight = nn.Parameter(torch.from_numpy(embs))
model = Model(emb_layer).cuda()
optimizer = torch.optim.Adam(model.parameters(),
lr=hparams['learning_rate'])
train_set = Dataset(all_sets[0], shuffle=True, pad_keys=('q1', 'q2'))
dev_set = Dataset(all_sets[1], shuffle=False, pad_keys=('q1', 'q2'))
test_set = Dataset(all_sets[2], shuffle=False, pad_keys=('q1', 'q2'))
step = 0
sum_loss = 0
dev_best = 0
test_score = 0
print("Starting training...")
print(hparams)
start_time = time.time()
for epoch in range(hparams['max_epoch']):
batches, batch_lengths = train_set.get_batches(hparams['batch_size'],
('q1', 'q2', 'y'))
for b_data, b_lengths in zip(batches, batch_lengths):
sum_loss += run_batch(b_data, b_lengths, model, optimizer)
step += 1
if step % hparams['display_step'] == 0:
avg_loss = sum_loss / hparams['display_step']
sum_loss = 0
dev_score = run_epoch_eval(dev_set, model)
out_str = f'Epoc {epoch} iter {step} took {time.time() - start_time:.1f}s\n' \
f'loss:\t{avg_loss:.5f}\tdev score:\t{dev_score:.4f}'
if dev_score > dev_best:
dev_best = dev_score
output_file = f'pred/{get_script_short_name(__file__)}.pred'
test_score = run_epoch_eval(test_set, model, output_file)
out_str += f'\t*** New best dev ***\ttest score:\t{test_score:.4f}'
print(out_str)
start_time = time.time()
print('Best model on dev: dev:{:.4f}\ttest:{:.4f}'.format(
dev_best, test_score))
train_data = Dataset(
'C:\\Users\\sanujb\\PycharmProjects\\CS585_FinalProject\\MaLSTM\\data\\train.csv',
model)
config = Config(len(train_data.vocab))
rnn = SiameseLSTM(config, train_data.model)
loss_function = nn.BCELoss()
optimizer = optim.Adadelta(rnn.encoder_params())
num_epochs = 100
train_size = 80000
for epoch in range(num_epochs):
start_time = timeit.default_timer()
epoch_loss = 0
batch_num = 1
for batch, targets in train_data.get_batches(config.batch_size,
train_size):
preds = rnn(batch)
batch_loss = loss_function(preds, targets)
epoch_loss += batch_loss
optimizer.zero_grad() # reset the gradients from the last batch
batch_loss.backward() # does backprop!!!
torch.nn.utils.clip_grad_norm_(rnn.encoder_params(), 0.25)
optimizer.step() # updates parameters using gradients
if batch_num % 100 == 0:
print('Batch number: {}, batch loss: {}, epoch loss {}'.format(
batch_num, batch_loss, epoch_loss))
batch_num += 1
print(epoch, epoch_loss)
