training_flag = 'training' in dict(inspect.signature(layer.call).parameters)
时间: 2023-11-03 17:05:01 浏览: 36
这行代码的作用是检查一个keras层(layer)的call函数中是否有名为`training`的参数。
在keras中,训练时和测试时有不同的行为,例如在训练时会使用dropout来防止过拟合,而在测试时不会使用dropout。因此,在定义keras模型时,需要通过将`training`参数传递给层的`call`函数来指示当前是在训练还是测试状态。
这行代码使用了Python的inspect模块来获取给定层的`call`函数的参数列表,然后检查其中是否有名为`training`的参数。如果有,则将`training_flag`设置为True,否则为False。
相关问题
这段代码中加一个test loss功能 class LSTM(nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, device): super().__init__() self.device = device self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.num_directions = 1 # 单向LSTM self.batch_size = batch_size self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True) self.linear = nn.Linear(65536, self.output_size) def forward(self, input_seq): h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) output, _ = self.lstm(input_seq, (h_0, c_0)) pred = self.linear(output.contiguous().view(self.batch_size, -1)) return pred if __name__ == '__main__': # 加载已保存的模型参数 saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth' device = 'cuda:0' lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device='cuda:0').to(device) state_dict = torch.load(saved_model_path) lstm_model.load_state_dict(state_dict) dataset = ECGDataset(X_train_df.to_numpy()) dataloader = DataLoader(dataset, batch_size=256, shuffle=True, num_workers=0, drop_last=True) loss_fn = nn.CrossEntropyLoss() optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4) for epoch in range(200000): print(f'epoch:{epoch}') lstm_model.train() epoch_bar = tqdm(dataloader) for x, y in epoch_bar: optimizer.zero_grad() x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor)) loss = loss_fn(x_out, y.long().to(device)) loss.backward() epoch_bar.set_description(f'loss:{loss.item():.4f}') optimizer.step() if epoch % 100 == 0 or epoch == epoch - 1: torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth") print("权重成功保存一次")
以下是加入 Test Loss 功能后的代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
class LSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, device):
super().__init__()
self.device = device
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.output_size = output_size
self.num_directions = 1 # 单向LSTM
self.batch_size = batch_size
self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True)
self.linear = nn.Linear(65536, self.output_size)
def forward(self, input_seq):
h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device)
c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device)
output, _ = self.lstm(input_seq, (h_0, c_0))
pred = self.linear(output.contiguous().view(self.batch_size, -1))
return pred
if __name__ == '__main__':
# 加载已保存的模型参数
saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth'
device = 'cuda:0'
lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device=device).to(device)
state_dict = torch.load(saved_model_path)
lstm_model.load_state_dict(state_dict)
# 加载测试数据集
test_dataset = ECGDataset(X_test_df.to_numpy())
test_loader = DataLoader(test_dataset, batch_size=256, shuffle=False, num_workers=0, drop_last=False)
# 定义损失函数和优化器
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4)
# 开始训练
for epoch in range(200000):
print(f'epoch:{epoch}')
lstm_model.train()
epoch_bar = tqdm(dataloader)
for x, y in epoch_bar:
optimizer.zero_grad()
x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor))
loss = loss_fn(x_out, y.long().to(device))
loss.backward()
epoch_bar.set_description(f'loss:{loss.item():.4f}')
optimizer.step()
# 计算测试集上的平均损失
test_loss = 0.0
lstm_model.eval()
with torch.no_grad():
for x, y in test_loader:
x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor))
loss = loss_fn(x_out, y.long().to(device))
test_loss += loss.item() * x.size(0)
test_loss /= len(test_dataset)
print(f'Test Loss: {test_loss:.4f}')
if epoch % 100 == 0 or epoch == epoch - 1:
torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth")
print("权重成功保存一次")
```
在这个代码中,我们首先加载了测试数据集 `test_dataset` 和测试数据加载器 `test_loader`。在每个 epoch 完成后,我们计算测试集上的平均损失值。我们使用 `lstm_model.eval()` 将模型设为评估模式,并利用 `torch.no_grad()` 避免计算梯度,以加速计算。最后,我们输出测试集上的平均损失值。
select tpi.id, sd.dept_id, sd.dept_name, tpi.year, tpi.project_name, tpi.construction_company, tpc.company_name as constructionCompanyName, (select sdd.dict_label from sys_dict_data sdd where sdd.dict_type = 'contract_segment' and sdd.dict_value = tpi.contract_segment) contractSegmentName, tpi.is_section from t_project_info tpi left join t_participating_company tpc on tpi.construction_company = tpc.id left join sys_dept sd on tpi.dept_id = sd.dept_id where tpi.del_flag = 0 and tpi.year = Date_format(now(),'%Y') and tpi.is_section = 0 and tpi.id not in(select group_concat(tci.project_id) from t_contract_info tci where tci.del_flag = 0 and tci.project_id != '');子查询语句和手动填的一致,结果却不一样
如果子查询语句和手动填的一致,但结果却不一样,可能是因为使用了 GROUP_CONCAT 函数来返回子查询结果中多个项目ID的字符串,而 NOT IN 语句中只能比较单个值,不能直接比较字符串。因此,你需要使用 INSTR 函数来判断子查询结果中是否包含指定的项目ID。例如:
SELECT tpi.id, sd.dept_id, sd.dept_name, tpi.year, tpi.project_name, tpi.construction_company, tpc.company_name as constructionCompanyName, (SELECT sdd.dict_label FROM sys_dict_data sdd WHERE sdd.dict_type = 'contract_segment' AND sdd.dict_value = tpi.contract_segment) contractSegmentName, tpi.is_section
FROM t_project_info tpi
LEFT JOIN t_participating_company tpc ON tpi.construction_company = tpc.id
LEFT JOIN sys_dept sd ON tpi.dept_id = sd.dept_id
WHERE tpi.del_flag = 0 AND tpi.year = DATE_FORMAT(NOW(), '%Y') AND tpi.is_section = 0 AND NOT EXISTS (
SELECT 1
FROM t_contract_info tci
WHERE tci.del_flag = 0 AND tci.project_id != '' AND INSTR(CONCAT(',', tci.project_id, ','), CONCAT(',', tpi.id, ','))
)
这里使用了 CONCAT 函数将子查询结果中多个项目ID拼接成一个字符串,然后使用 INSTR 函数来判断是否包含指定的项目ID。注意,这里在子查询中也使用了 NOT IN,但是为了避免子查询结果中包含 NULL 值导致 NOT IN 失效,使用了 NOT EXISTS 来避免这个问题。
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