if glove_vocab.getIndex(word):
emb[vocab.getIndex(word)] = glove_emb[glove_vocab.getIndex(word)]
torch.save(emb, emb_file)
# plug these into embedding matrix inside model
model.emb.weight.data.copy_(emb)
# +
###For changing embeddings
# -
# +
model.to(device), criterion.to(device)
if args.optim == 'adam':
optimizer = optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()), lr=args.lr, weight_decay=args.wd)
elif args.optim == 'adagrad':
optimizer = optim.Adagrad(filter(lambda p: p.requires_grad,
model.parameters()), lr=args.lr, weight_decay=args.wd)
elif args.optim == 'sgd':
optimizer = optim.SGD(filter(lambda p: p.requires_grad,
model.parameters()), lr=args.lr, weight_decay=args.wd)
elif args.optim == 'rmsprop':
optimizer = optim.RMSprop(filter(lambda p: p.requires_grad,
model.parameters()), lr=args.lr, weight_decay=args.wd)
# -
cfg.mem_dim(),
cfg.hidden_dim(),
cfg.num_classes(),
cfg.sparse(),
cfg.freeze_embed())
criterion = nn.KLDivLoss()
cfg.logger.info("model:\n" + str(model))
emb = get_embd(cfg, D.vocab)
# plug these into embedding matrix inside model
model.emb.weight.data.copy_(emb)
model.to(cfg.device()), criterion.to(cfg.device())
metrics = Metrics(cfg.num_classes())
# create trainer object for training and testing
trainer = Trainer(cfg, model, criterion, cfg.optimizer(model), cfg.device())
best = -float('inf')
for epoch in range(cfg.num_epochs()):
train_loss = trainer.train(train_dataset)
train_loss, train_pred = trainer.test(train_dataset)
test_loss, test_pred = trainer.test(test_dataset)