def inference(args, cnn_features):
tf.reset_default_graph()
with tf.Session() as sess:
net = LSTM(sess, max_seq_len=25, h_dim=1024)
net.build_model()
net.inference(cnn_features=cnn_features,
label_file=args.label_file,
gen_from=args.gen_from,
out_path=args.output_folder,
bsize=args.bsize)
def main(args):
if args.model == 'base':
postprocessing = None
elif args.model == 'jump':
postprocessing = pick_fix_length(400, PAD_TOKEN)
TEXT = data.Field(lower=True,
postprocessing=postprocessing,
pad_token=PAD_TOKEN,
include_lengths=True)
LABEL = data.Field(sequential=False, pad_token=None, unk_token=None)
train, test = datasets.IMDB.splits(TEXT, LABEL)
TEXT.build_vocab(train)
LABEL.build_vocab(train)
train_iter, test_iter = data.BucketIterator.splits(
(train, test),
batch_sizes=(args.batch, args.batch * 4),
device=args.gpu,
repeat=False,
sort_within_batch=True)
if args.model == 'base':
model = LSTM(len(TEXT.vocab), 300, 128, len(LABEL.vocab))
elif args.model == 'jump':
model = LSTMJump(len(TEXT.vocab), 300, 128, len(LABEL.vocab), args.R,
args.K, args.N, 80, 8)
model.load_pretrained_embedding(
get_word2vec(TEXT.vocab.itos,
'.vector_cache/GoogleNews-vectors-negative300.bin'))
model.cuda(args.gpu)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
max_accuracy = 0
for i in range(args.epoch):
print('Epoch: {}'.format(i + 1))
sum_loss = 0
model.train()
for batch in train_iter:
optimizer.zero_grad()
xs, lengths = batch.text
loss = model(xs, lengths, batch.label)
loss.backward()
torch.nn.utils.clip_grad_norm(model.parameters(), 1.)
optimizer.step()
sum_loss += loss.data[0]
print(f'Loss: {sum_loss / len(train_iter)}')
sum_correct = 0
total = 0
model.eval()
for batch in test_iter:
y = model.inference(*batch.text)
sum_correct += y.eq(batch.label).sum().float()
total += batch.label.size(0)
accuracy = (sum_correct / total).data[0]
max_accuracy = max(accuracy, max_accuracy)
print(f'Accuracy: {accuracy}')
print(f'Max Accuracy: {max_accuracy}')
