def train(opt):
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
train_dataloader, val_dataloader = create_dataloader(opt)
net = Classification() # 定义训练的网络模型
net.to(device)
net.train()
loss_function = nn.CrossEntropyLoss() # 定义损失函数为交叉熵损失函数
optimizer = optim.Adam(net.parameters(), lr=0.001) # 定义优化器（训练参数，学习率）
for epoch in range(opt.num_epochs): # 一个epoch即对整个训练集进行一次训练
running_loss = 0.0
correct = 0
total = 0
time_start = time.perf_counter()
for step, data in enumerate(train_dataloader,
start=0): # 遍历训练集，step从0开始计算
inputs, labels = data # 获取训练集的图像和标签
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad() # 清除历史梯度
# forward + backward + optimize
# outputs = net(inputs.permute(0,1,3,2)) # 正向传播
outputs = net(inputs) # 正向传播
print('outputs.shape', outputs.shape, labels.shape)
loss = loss_function(outputs, labels) # 计算损失
loss.backward() # 反向传播
optimizer.step() # 优化器更新参数
predict_y = torch.max(outputs, dim=1)[1]
total += labels.size(0)
correct += (predict_y == labels).sum().item()
running_loss += loss.item()
# print statistics
# print('train_dataloader length: ', len(train_dataloader))
acc = correct / total
print('Train on epoch {}: loss:{}, acc:{}%'.format(epoch + 1, running_loss / total, 100 * correct / total))
# 保存训练得到的参数
if opt.model == 'basic':
save_weight_name = os.path.join(opt.save_path,
'Basic_Epoch_{0}_Accuracy_{1:.2f}.pth'.format(
epoch + 1,
acc))
elif opt.model == 'plus':
save_weight_name = os.path.join(opt.save_path,
'Plus_Epoch_{0}_Accuracy_{1:.2f}.pth'.format(
epoch + 1,
acc))
torch.save(net.state_dict(), save_weight_name)
print('Finished Training')
cifar_10_train_dt = MyCustomDataset('data',
download=True,
transform=ToTensor())
cifar_10_train_l = DataLoader(cifar_10_train_dt,
batch_size=batch_size,
shuffle=True,
drop_last=True,
pin_memory=torch.cuda.is_available())
encoder = Encoder().to(device)
loss_fn = DeepInfoMaxLoss(1, 0, 1).to(device)
classification = Classification().to(device)
encoder_optim = Adam(encoder.parameters(), lr=1e-4)
loss_optim = Adam(loss_fn.parameters(), lr=1e-4)
classification_optim = Adam(classification.parameters(), lr=1e-4)
epoch_restart = 0
root = Path(r'models')
if epoch_restart > 0 and root is not None:
enc_file = root / Path('encoder' + str(epoch_restart) + '.wgt')
loss_file = root / Path('loss' + str(epoch_restart) + '.wgt')
classification_loss_file = root / Path('classification_loss' +
str(epoch_restart) + '.wgt')
encoder.load_state_dict(torch.load(str(enc_file)))
loss_fn.load_state_dict(torch.load(str(loss_file)))
classification.load_state_dict(
torch.load(str(classification_loss_file)))
for epoch in range(epoch_restart + 1, 501):
config = args.parse_args()
config2 = Config()
dataset = SentenceClassificationDataset('../data/processing_data',
config.strmaxlen)
print('unique labels = {}'.format(dataset.get_unique_labels_num()))
print('vocab size = {}'.format(dataset.get_vocab_size()))
if config.model_name == 'CNN':
model = Classification(config.embedding, config.strmaxlen,
dataset.get_unique_labels_num(),
dataset.get_vocab_size())
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)
elif config.model_name == 'RCNN':
model = RCNN(config.embedding, config.strmaxlen,
dataset.get_unique_labels_num(), dataset.get_vocab_size())
if config.mode == 'train':
model = model.cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)
elif config.model_name == "DUALRCNN":
model = DualRCNN(config.embedding, config.strmaxlen,
dataset.get_unique_labels_num(),
dataset.get_vocab_size())
if torch.cuda.is_available():
