,

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="C:\anaconda\envs\pytorch" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="pytorch_gpu" project-jdk-type="Python SDK" />
 </project>

.idea/pytorch2.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="C:\anaconda\envs\pytorch" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="pytorch_gpu" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

浙江电压等级电量/电压等级_输出为5.py

@@ -52,7 +52,7 @@ def data_preprocessing(data):
     return data
 
 # 拼接数据集
-file_dir = r'C:\Users\鸽子\Desktop\浙江各地市分电压日电量数据'
+file_dir = r'C:\Users\user\Desktop\浙江各地市分电压日电量数据'
 excel = os.listdir(file_dir)[0]
 data = pd.read_excel(os.path.join(file_dir, excel), sheet_name=0, index_col='stat_date')
 data.drop(columns='地市',inplace=True)
@@ -83,7 +83,7 @@ for excel in os.listdir(file_dir)[1:]:
         dataset_x = np.concatenate((dataset_x,x))
         dataset_y = np.concatenate((dataset_y,y))
 
-print(dataset_x,dataset_y,dataset_x.shape,dataset_y.shape)
+
 
 # 训练
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@@ -120,7 +120,7 @@ for i in range(1500):
     optimizer.step()
     optimizer.zero_grad()
     train_loss.append(loss.item())
-    print(loss)
+
 # 保存模型
 torch.save(model.state_dict(),'dy5.pth')
 
@@ -128,16 +128,16 @@ torch.save(model.state_dict(),'dy5.pth')
 model = model.eval()  # 转换成测试模式
 # model.load_state_dict(torch.load(os.path.join(model_save_dir,model_file)))  # 读取参数
 dataset_x = dataset_x.reshape(-1, 1, DAYS_FOR_TRAIN)  # (seq_size, batch_size, feature_size)
-dataset_x = torch.from_numpy(dataset_x).to(device)
+dataset_x = torch.from_numpy(dataset_x).to(device).type(torch.float32)
 
 pred_test = model(dataset_x)  # 全量训练集
 # 模型输出 (seq_size, batch_size, output_size)
 pred_test = pred_test.view(-1)
 pred_test = np.concatenate((np.zeros(DAYS_FOR_TRAIN), pred_test.cpu().detach().numpy()))
 
-plt.plot(pred_test, 'r', label='prediction')
-plt.plot(df, 'b', label='real')
-plt.plot((train_size, train_size), (0, 1), 'g--')  # 分割线 左边是训练数据 右边是测试数据的输出
+plt.plot(pred_test.reshape(-1), 'r', label='prediction')
+plt.plot(dataset_y.reshape(-1), 'b', label='real')
+plt.plot((train_size*5, train_size*5), (0, 1), 'g--')  # 分割线 左边是训练数据 右边是测试数据的输出
 plt.legend(loc='best')
 plt.show()