删除重复数据集

浙江电压等级电量/400v各地市模型/400v_丽水.py

@@ -26,4 +26,10 @@ print(result)
 print((result['eval'][-3:].sum()-result['pred'][-3:].sum())/result['eval'].sum())
 
 
+import numpy as np
 
+X_eval = np.array([
+                   [22.3,16.19,23,1,0],
+                       [23.69,14.5,23,0,0],
+                       [23.69,14,23,0,0]])
+print(model.predict(X_eval))

浙江电压等级电量/电压等级_输入10_输出3.py

@@ -118,48 +118,48 @@ eval_y = torch.from_numpy(eval_y).to(device).type(torch.float32)
 
 model = LSTM_Regression(DAYS_FOR_TRAIN, 32, output_size=3, num_layers=2).to(device)  # 导入模型并设置模型的参数输入输出层、隐藏层等
 
-loss_function = nn.MSELoss()
+# loss_function = nn.MSELoss()
-optimizer = torch.optim.Adam(model.parameters(), lr=0.005, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
+# optimizer = torch.optim.Adam(model.parameters(), lr=0.005, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
-
+#
-min_loss = 1
+# min_loss = 1
-for i in range(2500):
+# for i in range(2500):
-    model.train()
+#     model.train()
-    out = model(train_x)
+#     out = model(train_x)
-    loss = loss_function(out, train_y)
+#     loss = loss_function(out, train_y)
-    loss.backward()
+#     loss.backward()
-    optimizer.step()
+#     optimizer.step()
-    optimizer.zero_grad()
+#     optimizer.zero_grad()
-
+#
-    model.eval()
+#     model.eval()
-    with torch.no_grad():
+#     with torch.no_grad():
-        pred = model(eval_x)
+#         pred = model(eval_x)
-        eval_loss = loss_function(pred,eval_y)
+#         eval_loss = loss_function(pred,eval_y)
-        if eval_loss <= min_loss:
+#         if eval_loss <= min_loss:
-            min_loss = eval_loss
+#             min_loss = eval_loss
-            best_param = model.state_dict()
+#             best_param = model.state_dict()
-
+#
-    if (i+1) % 100 == 0:
+#     if (i+1) % 100 == 0:
-        print(f'epoch {i+1}/1500 loss:{round(loss.item(),5)}')
+#         print(f'epoch {i+1}/1500 loss:{round(loss.item(),5)}')
-
+#
-# 保存模型
+# # 保存模型
-torch.save(best_param,'best_dy3.pth')
+# torch.save(best_param,'best_dy3.pth')
 
 # for test
-model = model.eval()
+# model = model.eval()
-
+#
-dataset_x = dataset_x.reshape(-1, 1, DAYS_FOR_TRAIN)  # (seq_size, batch_size, feature_size)
+# 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).type(torch.float32)
+# dataset_x = torch.from_numpy(dataset_x).to(device).type(torch.float32)
-
+#
-pred_test = model(dataset_x)
+# pred_test = model(dataset_x)
-# 模型输出 (seq_size, batch_size, output_size)
+# # 模型输出 (seq_size, batch_size, output_size)
-pred_test = pred_test.view(-1)
+# pred_test = pred_test.view(-1)
-pred_test = np.concatenate((np.zeros(DAYS_FOR_TRAIN), pred_test.cpu().detach().numpy()))
+# pred_test = np.concatenate((np.zeros(DAYS_FOR_TRAIN), pred_test.cpu().detach().numpy()))
-
+#
-plt.plot(pred_test.reshape(-1), 'r', label='prediction')
+# plt.plot(pred_test.reshape(-1), 'r', label='prediction')
-plt.plot(dataset_y.reshape(-1), 'b', label='real')
+# plt.plot(dataset_y.reshape(-1), 'b', label='real')
-plt.plot((train_size*3, train_size*3), (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((train_size*3, train_size*3), (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.legend(loc='best')
-plt.show()
+# plt.show()
 
 
 # 创建测试集