0.1.52

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

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: guan
-Version: 0.1.51
+Version: 0.1.52
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan

PyPI/src/guan/machine_learning.py

@@ -112,4 +112,57 @@ def fully_connected_neural_network_with_three_hidden_layers(input_size=1, hidden
             output = self.output_layer(hidden_output_3)
             return output
     model = model_class()
+    return model
+
+# 使用优化器训练模型
+@guan.function_decorator
+def train_model(model, x_data, y_data, 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):
+        output = model.forward(x_data)
+        loss = criterion(output, y_data)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        losses.append(loss.item())
+        if print_show == 1:
+            if (epoch + 1) % 100 == 0:
+                print(epoch)
+    return model, losses
+
+# 保存完整模型到文件
+@guan.function_decorator
+def save_model(model, filename='./model.pth'):
+    import torch
+    torch.save(model, filename)
+
+# 保存模型参数到文件
+@guan.function_decorator
+def save_model_parameters(model, filename='./model_parameters.pth'):
+    import torch
+    torch.save(model.state_dict(), filename)
+
+# 加载完整模型
+@guan.function_decorator
+def load_model(filename='./model.pth'):
+    import torch
+    model = torch.load(filename)
+    return model
+
+# 加载模型参数（需要输入模型）
+@guan.function_decorator
+def load_model_parameters(model, filename='./model_parameters.pth'):
+    import torch
+    model.load_state_dict(torch.load(filename))
     return model