def train(from_version=1, epoches=50):
global embd, disc, embd_optim, disc_optim
embd = Embedding(len(dictionary), VEC_SIZE).load(
'.', from_version, device)
disc = Discriminator(VEC_SIZE).load('.', from_version, device)
embd_optim = optim.SGD(embd.parameters(), lr=lr)
disc_optim = optim.SGD(disc.parameters(), lr=lr)
for i in range(epoches):
train_epoch()
embd.save('.', i+from_version)
disc.save('.', i+from_version)
if torch.cuda.is_available():
torch.cuda.set_device(args.gpuId)
torch.cuda.manual_seed(seed)
embedding.cuda()
tagger.cuda()
print('**** Running with GPU-' + str(args.gpuId) + ' ****\n')
else:
useGpu = False
print('**** Warning: GPU is not available ****\n')
criterionTagger = nn.CrossEntropyLoss(size_average=False, ignore_index=-1)
batchListTrain = utils.buildBatchList(len(corpus.trainData), batchSize)
batchListDev = utils.buildBatchList(len(corpus.devData), batchSize)
totalParams = list(embedding.parameters()) + list(tagger.parameters())
lstmParams = []
mlpParams = []
withoutWeightDecay = []
for name, param in list(embedding.named_parameters()) + list(
tagger.named_parameters()):
if not param.requires_grad:
continue
if 'bias' in name or 'Embedding' in name:
withoutWeightDecay += [param]
elif 'encoder' in name:
lstmParams += [param]
else:
mlpParams += [param]
optParams = [{
'params': lstmParams,