0.1.74

PyPI/setup.cfg

@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.1.73
+version = 0.1.74
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package

PyPI/src/guan.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: guan
-Version: 0.1.73
+Version: 0.1.74
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan

PyPI/src/guan/machine_learning.py

@@ -131,7 +131,6 @@ def train_model(model, x_data, y_data, optimizer='Adam', learning_rate=0.001, cr
     elif criterion == 'CrossEntropyLoss':
         criterion = torch.nn.CrossEntropyLoss()
     losses = []
-
     for epoch in range(num_epochs):
         output = model.forward(x_data)
         loss = criterion(output, y_data)
@@ -141,7 +140,34 @@ def train_model(model, x_data, y_data, optimizer='Adam', learning_rate=0.001, cr
         losses.append(loss.item())
         if print_show == 1:
             if (epoch + 1) % 100 == 0:
-                print(epoch)
+                print(epoch, loss.item())
+    return model, losses
+
+# 使用优化器批量训练模型
+@guan.statistics_decorator
+def batch_train_model(model, train_loader, optimizer='Adam', learning_rate=0.001, criterion='MSELoss', num_epochs=1000, print_show=1):
+    import torch
+    if optimizer == 'Adam':
+        optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+    elif optimizer == 'SGD':
+        optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
+    
+    if criterion == 'MSELoss':
+        criterion = torch.nn.MSELoss()
+    elif criterion == 'CrossEntropyLoss':
+        criterion = torch.nn.CrossEntropyLoss()
+    losses = []
+    for epoch in range(num_epochs):
+        for batch_x, batch_y in train_loader:
+            output = model.forward(batch_x)
+            loss = criterion(output, batch_y)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+            losses.append(loss.item())
+            if print_show == 1:
+                if (epoch + 1) % 100 == 0:
+                    print(epoch, loss.item())
     return model, losses
 
 # 保存模型参数到文件
@@ -169,3 +195,11 @@ def load_model(filename='./model.pth'):
     import torch
     model = torch.load(filename)
     return model
+
+# 加载训练数据，用于批量加载训练
+@guan.statistics_decorator
+def load_train_data(x_train, y_train, batch_size=32):
+    from torch.utils.data import DataLoader, TensorDataset
+    train_dataset = TensorDataset(x_train, y_train)
+    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+    return train_loader