61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
import torch
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import torch.nn as nn
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import matplotlib.pyplot as plt
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# 定义输入层大小、隐藏层大小、输出层大小和批量大小
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n_in, n_h, n_out, batch_size = 10, 5, 1, 10
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# 创建虚拟输入数据和目标数据
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x = torch.randn(batch_size, n_in) # 随机生成输入数据
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y = torch.tensor([[1.0], [0.0], [0.0],
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[1.0], [1.0], [1.0], [0.0], [0.0], [1.0], [1.0]]) # 目标输出数据
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# 创建顺序模型，包含线性层、ReLU激活函数和Sigmoid激活函数
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model = nn.Sequential(
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nn.Linear(n_in, n_h), # 输入层到隐藏层的线性变换
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nn.ReLU(), # 隐藏层的ReLU激活函数
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nn.Linear(n_h, n_out), # 隐藏层到输出层的线性变换
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nn.Sigmoid() # 输出层的Sigmoid激活函数
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)
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# 定义均方误差损失函数和随机梯度下降优化器
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criterion = torch.nn.MSELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.01) # 学习率为0.01
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# 用于存储每轮的损失值
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losses = []
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# 执行梯度下降算法进行模型训练
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for epoch in range(50): # 迭代50次
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y_pred = model(x) # 前向传播，计算预测值
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loss = criterion(y_pred, y) # 计算损失
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losses.append(loss.item()) # 记录损失值
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print(f'Epoch [{epoch+1}/50], Loss: {loss.item():.4f}') # 打印损失值
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optimizer.zero_grad() # 清零梯度
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loss.backward() # 反向传播，计算梯度
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optimizer.step() # 更新模型参数
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# 可视化损失变化曲线
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plt.figure(figsize=(8, 5))
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plt.plot(range(1, 51), losses, label='Loss')
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plt.xlabel('Epoch')
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plt.ylabel('Loss')
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plt.title('Training Loss Over Epochs')
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plt.legend()
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plt.grid()
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plt.show()
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# 可视化预测结果与实际目标值对比
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y_pred_final = model(x).detach().numpy() # 最终预测值
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y_actual = y.numpy() # 实际值
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plt.figure(figsize=(8, 5))
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plt.plot(range(1, batch_size + 1), y_actual, 'o-', label='Actual', color='blue')
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plt.plot(range(1, batch_size + 1), y_pred_final, 'x--', label='Predicted', color='red')
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plt.xlabel('Sample Index')
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plt.ylabel('Value')
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plt.title('Actual vs Predicted Values')
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plt.legend()
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plt.grid()
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plt.show() |