def build_textcnn_model(vocab, config, train=True):
model = TextCNN(vocab.vocab_size, config)
if train:
model.train()
else:
model.eval()
if torch.cuda.is_available():
model.cuda()
else:
model.cpu()
return model
def build_textcnn_model(vocab, config, train=True):
model = TextCNN(vocab.vocab_size, config)
if train:
model.train()
#在训练模型时会在前面加上train();
else:
model.eval()
#在测试模型时在前面使用eval()，会将BN和DropOut固定住，不会取平均，而是用训练好的值
if torch.cuda.is_available():
model.cuda()
else:
model.cpu()
return model
def build_textcnn_model(vocab, config, train=True):
model = TextCNN(vocab.vocab_size, config)
if train:
model.train()
#在训练模型时会在前面加上train();
else:
model.eval()
#在测试模型时在前面使用eval()，会将BN和DropOut固定住，不会取平均，而是用训练好的值
#train()与eval()两个方法是针对网络train和eval时采用不同方式的情况
#比如Batch Normalization和Dropout
#BN的作用主要是对网络中间的每层进行归一化处理，并且使用变换重构保证所提取的特征分布不会被破坏；
#由于训练完毕后参数都是固定的，所有BN的训练和测试时的操作不同
#Dropopt能够克服过拟合，在每个训练batch中，通过忽略一般的特征检测器，可以明显地减少过拟合现象。
if torch.cuda.is_available():
model.cuda()
else:
model.cpu()
return model
if it % len_print == 0 and it > 0 and args.verbose:
if args.gpu:
outputs = outputs.cpu()
labels = labels.cpu()
ground_true = labels.numpy()
dist = softmax(outputs).detach().numpy()
acc = (np.argmax(dist,axis=1) == ground_true).sum()/dist.shape[0]
print('[%d,%5d] loss: %.3f acc: %.3f' % (ep, it, loss, acc))
ep += 1
if args.verbose:
print("Testing...")
net = best_model
testset = TextDataset(args.dataset, args.percent, test=True, wo_unlabel=args.without_unlabel)
testLoader = DataLoader(testset, batch_size=500,shuffle=False,num_workers=4)
net.eval()
net.cpu()
f1_micro = 0.0
f1_macro = 0.0
cum_loss = 0.0
ground_true = []
pred = []
for it, data in enumerate(testLoader):
inputs, labels = data
outputs = net(inputs)
dist = softmax(outputs).detach().numpy()
ground_true = np.concatenate([ground_true, labels.numpy()],axis=0)
pred = np.concatenate([pred, np.argmax(dist,axis=1)],axis=0)
f1_micro = f1_score(ground_true, pred, average='micro')
f1_macro = f1_score(ground_true, pred, average='macro')
print("F1 score: micro-%.5f\tmacro-%.5f" % (f1_micro, f1_macro))
