class LSTMTrainer(Trainer):
def __init__(self, opt, emb_matrix=None):
self.opt = opt
self.emb_matrix = emb_matrix
self.model = LSTMClassifier(opt, emb_matrix=emb_matrix)
self.criterion = nn.CrossEntropyLoss()
self.parameters = [p for p in self.model.parameters() if p.requires_grad]
if opt['cuda']:
self.model.cuda()
self.criterion.cuda()
self.optimizer = torch_utils.get_optimizer(opt['optim'], self.parameters, opt['lr'])
def update(self, batch):
inputs, labels = unpack_batch(batch)
# Step 1 init and forward
self.model.train()
self.optimizer.zero_grad()
logits = self.model(inputs)
loss = self.criterion(logits, labels)
loss_val = loss.item()
# Step 2 backward
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.opt['max_grad_norm'])
# Step 3 update
self.optimizer.step()
return loss_val
def predict(self, batch, unsort=True):
inputs, labels = unpack_batch(batch)
self.model.eval()
logits = self.model(inputs)
loss = self.criterion(logits, labels)
loss_val = loss.item()
probs = F.softmax(logits, 1).data.cpu().numpy().tolist()
predictions = np.argmax(logits.data.cpu().numpy(), axis=1).tolist()
labels = labels.data.cpu().numpy().tolist()
return predictions, probs, labels, loss_val
def train(dim, args):
import torch
from torch import nn, optim
import numpy as np
from features import ExtractWordEmbeddings
from preprocess_data import batchify, padBatch
from models.lstm import LSTMClassifier
from sklearn.utils import shuffle
# hyperparameters
embedding_dim = 300 # changes only with different word embeddings
hidden_dim = args.hidden_dim
max_epochs = args.max_epochs
is_cuda = True
batch_size = 60
lr = args.lr
n_decreases = 10
save_dir = 'weights/LSTM/%s' % dim
if not os.path.exists(save_dir):
os.makedirs(save_dir)
"""
Loading train / validation datasets
X_tr: a list of tokenized sentences
y_tr: a list of 0 and 1
"""
X_tr, y_tr = loadDatasetForLSTM(dim,
'train') # a list of tokenized sentences
X_d, y_d = loadDatasetForLSTM(dim, 'dev')
# load model and settings for training
model = LSTMClassifier(embedding_dim=embedding_dim, hidden_dim=hidden_dim)
if is_cuda:
model.cuda()
optimizer = optim.AdamW(model.parameters(), lr=lr)
flag = True
old_val = np.inf # previous validation error
em = ExtractWordEmbeddings(emb_type='glove')
loss_fn = nn.BCELoss()
# train model
epoch = 0
cnt_decrease = 0
while (flag):
tr_loss = 0.0
epoch += 1
if (epoch > max_epochs) | (cnt_decrease > n_decreases):
break
# train
model.train()
# for each iteration, shuffles X_tr and y_tr and puts them into batches
X_tr, y_tr = shuffle(X_tr, y_tr)
tr_batches = batchify(X_tr, y_tr, batch_size)
for X_b, y_b in tr_batches:
# X_b is still a list of tokenized sentences (list of list of words)
optimizer.zero_grad()
"""
obtain_vectors_from_sentence(sent=list of words, include_unk=True)
: changes each word into an embedding, and returns a list of embeddings
padBatch(list of embedding lists, max_seq=None)
: for each batch, returns a tensor fixed to the max size, applies zero padding
"""
inputs = torch.tensor(
padBatch([
em.obtain_vectors_from_sentence(sent, True) for sent in X_b
])).float()
# here, inputs become a tensor of shape (B * seq_len * dim)
targets = torch.tensor(y_b, dtype=torch.float32)
if is_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
outputs = model(inputs)
loss = loss_fn(outputs, targets) # error here
loss.backward()
tr_loss += loss.item()
optimizer.step()
print("[Epoch %d] train loss: %1.3f" % (epoch, tr_loss))
# validate
model.eval()
current_loss = 0.0
X_d, y_d = shuffle(X_d, y_d)
val_batches = batchify(X_d, y_d, batch_size)
with torch.no_grad():
for X_b, y_b in val_batches:
inputs = torch.tensor(
padBatch([
em.obtain_vectors_from_sentence(sent, True)
for sent in X_b
])).float()
targets = torch.tensor(y_b, dtype=torch.float32)
if is_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
outputs = model(inputs)
loss = loss_fn(outputs, targets) # error here
current_loss += loss.item()
print("[Epoch %d] validation loss: %1.3f" % (epoch, current_loss))
if current_loss < old_val:
# if current round is better than the previous round
best_state = model.state_dict() # save this model
torch.save(best_state, join(save_dir, 'best-weights.pth'))
print("Updated model")
old_val = current_loss
cnt_decrease = 0
else:
# if the current round is doing worse
cnt_decrease += 1
if cnt_decrease >= n_decreases:
flag = False
return