,

.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>

bp神经网络.py

@@ -0,0 +1,81 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import torch
+from sklearn import preprocessing
+
+data = pd.read_excel(r'C:\Users\user\PycharmProjects\pytorch2\入模数据\杭州数据.xlsx',index_col='dtdate')
+print(data.columns)
+
+
+y = np.array(data['售电量'])  # 制作标签，用于比对训练结果
+x = data.drop(columns=['售电量','city_name'])  # 在特征数据集中去掉label
+# df.drop(label, axis=0)
+# label：要删除的列或者行，如果要删除多个，传入列表
+# axis:轴的方向，0为行，1为列，默认为0
+fea_train = np.array(x)  # 转换为ndarray格式
+
+# 数据标准化操作：(x-均值μ) / 标准差σ ，使数据关于原点对称，提升训练效率
+input_features = preprocessing.StandardScaler().fit_transform(fea_train)  # fit：求出均值和标准差 transform：求解
+
+# y归一化 防止梯度爆炸
+y = (y - np.min(y))/(np.max(y) - np.min(y))
+print(y)
+
+# 设定神经网络的输入参数、隐藏层神经元、输出参数的个数
+input_size = input_features.shape[1]  # 设定输入特征个数
+# np.shape[1]
+# 0为行，1为列，默认为0
+# 在此表格中因为每行为各样本的值，每列为不同的特征分类，所以此处0表示样本数，1表示特征数
+hidden_size = 64  # 设定隐藏层包含64个神经元
+output_size = 1  # 设定输出特征个数为1
+batch_size = 32  # 每一批迭代的特征数量
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")  # 选择使用GPU训练
+
+my_nn = torch.nn.Sequential(
+    torch.nn.Linear(input_size, hidden_size).to(device),  # 输入层 → 第一层
+    torch.nn.ReLU().to(device),
+    torch.nn.Linear(hidden_size, hidden_size).to(device),  # 第一层 → 第二层
+    torch.nn.ReLU().to(device),
+    torch.nn.Linear(hidden_size, hidden_size).to(device),  # 第二层 → 第三层
+    torch.nn.ReLU().to(device),
+    torch.nn.Linear(hidden_size, hidden_size).to(device),  # 第三层 → 第四层
+    torch.nn.ReLU().to(device),
+    torch.nn.Linear(hidden_size, output_size).to(device)  # 第四层 → 输出层
+).to(device)
+cost = torch.nn.MSELoss().to(device)
+optimizer = torch.optim.Adam(my_nn.parameters(), lr=0.001)
+
+# 训练网络
+losses = []
+for i in range(300):
+    batch_loss = []
+    # 采用MINI-Batch的方法进行训练
+    for start in range(0, len(input_features), batch_size):
+        end = start + batch_size if start + batch_size < len(input_features) else len(input_features)
+        x_train = torch.tensor(input_features[start:end], dtype=torch.float32, requires_grad=True).to(device)
+        y_train = torch.tensor(y[start:end], dtype=torch.float32, requires_grad=True).to(device)
+        prediction = my_nn(x_train)
+        loss = cost(y_train, prediction)
+        optimizer.zero_grad()
+        loss.backward(retain_graph=True)
+        optimizer.step()
+        batch_loss.append(loss.data.cpu().numpy())
+
+    if i % 10 == 0:
+        losses.append(np.mean(batch_loss))
+        print(losses)
+        print(i, np.mean(batch_loss))
+
+# 保存模型
+# torch.save(my_nn, 'BP.pt')
+
+# 绘制图像
+dev_x = [i * 10 for i in range(20)]
+plt.xlabel('step count')
+plt.ylabel('loss')
+plt.xlim((0, 200))
+plt.ylim((0, 1000))
+plt.plot(dev_x, losses)
+plt.show()

浙江行业电量/输出为3.py

@@ -87,14 +87,14 @@ def run(file_dir,excel):
         train_loss = []
         loss_function = nn.MSELoss()
         optimizer = torch.optim.Adam(model.parameters(), lr=0.005, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
-        for i in range(1500):
+        for i in range(2500):
             out = model(train_x)
             loss = loss_function(out, train_y)
             loss.backward()
             optimizer.step()
             optimizer.zero_grad()
             train_loss.append(loss.item())
-            # print(loss)
+            print(loss)
         # 保存模型
         # torch.save(model.state_dict(),save_filename)
         # torch.save(model.state_dict(),os.path.join(model_save_dir,model_file))
@@ -121,7 +121,7 @@ def run(file_dir,excel):
         # result_list = []
         # 以x为基础实际数据，滚动预测未来3天
         x = torch.from_numpy(df[-14:-4]).to(device)
-        pred = model(x.reshape(-1,1,DAYS_FOR_TRAIN)).view(-1).detach().numpy()
+        pred = model(x.reshape(-1,1,DAYS_FOR_TRAIN)).view(-1).cpu().detach().numpy()
 
 
         # for i in range(3):
@@ -147,7 +147,7 @@ def run(file_dir,excel):
         print(target)
         print(result_eight)
     final_df = pd.concat(list_app,ignore_index=True)
-    final_df.to_csv('市行业电量.csv',encoding='gbk')
+    # final_df.to_csv('市行业电量.csv',encoding='gbk')
     print(final_df)
 
         # result_eight.to_csv(f'./月底预测结果/9月{excel[:2]}.txt', sep='\t', mode='a')
@@ -155,7 +155,7 @@ def run(file_dir,excel):
         #     f.write(f'{excel[:2]}{industry}:{round(target, 5)}\n')
 
 if __name__ == '__main__':
-    file_dir = r'C:\python-project\pytorch3\浙江行业电量\浙江所有地市133行业数据'
+    file_dir = r'C:\Users\user\PycharmProjects\pytorch2\浙江行业电量\浙江所有地市133行业数据'
 
     run(file_dir,'丽水133行业数据（全）.xlsx')
     # p = Pool(4)