From 8e746829026b32b2f7cd0d32ba46b17168c39911 Mon Sep 17 00:00:00 2001
From: 17427 <1742785947@qq.com>
Date: Wed, 18 Oct 2023 23:46:58 +0800
Subject: [PATCH] cws

---
 杭州日电量/cws_to_data/台州.txt               |  1 +
 杭州日电量/industry_elec_cws.py                 | 12 +++++++++++-
 .../pip3正确安装PyTorch GPU版本.txt            | 11 +++++++++++
 3 files changed, 23 insertions(+), 1 deletion(-)
 create mode 100644 杭州日电量/pip3正确安装PyTorch GPU版本.txt

diff --git a/杭州日电量/cws_to_data/台州.txt b/杭州日电量/cws_to_data/台州.txt
index 9f0a9e2..bc9092d 100644
--- a/杭州日电量/cws_to_data/台州.txt
+++ b/杭州日电量/cws_to_data/台州.txt
@@ -58,3 +58,4 @@
 {'city': '台州', 'industry': '17.橡胶和塑料制品业', 'month_deviation_rate': 0.00067}
 {'city': '台州', 'industry': '其中:橡胶制品业', 'month_deviation_rate': 0.00118}
 {'city': '台州', 'industry': '塑料制品业', 'month_deviation_rate': 0.00314}
+{'city': '台州', 'industry': '18.非金属矿物制品业', 'month_deviation_rate': -0.00198}
diff --git a/杭州日电量/industry_elec_cws.py b/杭州日电量/industry_elec_cws.py
index 77f1d5c..da734cf 100644
--- a/杭州日电量/industry_elec_cws.py
+++ b/杭州日电量/industry_elec_cws.py
@@ -71,15 +71,22 @@ def to_data(file_dir, excel):
         train_x = train_x.reshape(-1, 1, DAYS_FOR_TRAIN)
         train_y = train_y.reshape(-1, 1, 1)
 
+        # 使用GPU
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        train_x.to(device)
+        train_y.to(device)
+
         # 转为pytorch的tensor对象
         train_x = torch.from_numpy(train_x)
         train_y = torch.from_numpy(train_y)
 
         model = LSTM_Regression(DAYS_FOR_TRAIN, 32, output_size=1, num_layers=2)  # 导入模型并设置模型的参数输入输出层、隐藏层等
 
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        # 使用GPU
         print("cuda" if torch.cuda.is_available() else "cpu")
         model.to(device)
+        # train_x.to(device)
+        # train_y.to(device)
 
         train_loss = []
         loss_function = nn.MSELoss()
@@ -106,8 +113,11 @@ def to_data(file_dir, excel):
         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)
+        dataset_x.to(device)
 
         pred_test = model(dataset_x)  # 全量训练集
         # 模型输出 (seq_size, batch_size, output_size)
diff --git a/杭州日电量/pip3正确安装PyTorch GPU版本.txt b/杭州日电量/pip3正确安装PyTorch GPU版本.txt
new file mode 100644
index 0000000..e10657d
--- /dev/null
+++ b/杭州日电量/pip3正确安装PyTorch GPU版本.txt	
@@ -0,0 +1,11 @@
+# 首先卸载
+pip3 uninstall torch
+pip3 uninstall torchvision
+pip3 uninstall torchaudio
+
+# 然后安装
+pip3 install torch==2.1.0+cu121 torchvision==0.16.0+cu121 torchaudio==2.1.0+cu121 -f https://download.pytorch.org/whl/cu121/torch_stable.html
+
+
+# 可临时清华源加速
+pip3 install torch==2.1.0+cu121 torchvision==0.16.0+cu121 torchaudio==2.1.0+cu121 -f https://download.pytorch.org/whl/cu121/torch_stable.html -i https://pypi.tuna.tsinghua.edu.cn/simple/
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