Day40 Python Study
@浙大疏锦行
import torch import torch.nn as nn import torch.optim as optim from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split import numpy as np # 仍然用4特征，3分类的鸢尾花数据集作为我们今天的数据集 # 加载鸢尾花数据集 iris = load_iris() X = iris.data # 特征数据 y = iris.target # 标签数据 # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # # 打印下尺寸 # print(X_train.shape) # print(y_train.shape) # print(X_test.shape) # print(y_test.shape) # 归一化数据，神经网络对于输入数据的尺寸敏感，归一化是最常见的处理方式 from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) #确保训练集和测试集是相同的缩放 # 将数据转换为 PyTorch 张量，因为 PyTorch 使用张量进行训练 # y_train和y_test是整数，所以需要转化为long类型，如果是float32，会输出1.0 0.0 X_train = torch.FloatTensor(X_train) y_train = torch.LongTensor(y_train) X_test = torch.FloatTensor(X_test) y_test = torch.LongTensor(y_test) class MLP(nn.Module): # 定义一个多层感知机（MLP）模型，继承父类nn.Module def __init__(self): # 初始化函数 super(MLP, self).__init__() # 调用父类的初始化函数 # 前三行是八股文，后面的是自定义的 self.fc1 = nn.Linear(4, 10) # 输入层到隐藏层 self.relu = nn.ReLU() self.fc2 = nn.Linear(10, 3) # 隐藏层到输出层 # 输出层不需要激活函数，因为后面会用到交叉熵函数cross_entropy，交叉熵函数内部有softmax函数，会把输出转化为概率 def forward(self, x): out = self.fc1(x) out = self.relu(out) out = self.fc2(out) return out # 实例化模型 model = MLP() # 分类问题使用交叉熵损失函数 criterion = nn.CrossEntropyLoss() # 使用随机梯度下降优化器 optimizer = optim.SGD(model.parameters(), lr=0.01) # # 使用自适应学习率的化器 # optimizer = optim.Adam(model.parameters(), lr=0.001) # 训练模型 num_epochs = 20000 # 训练的轮数 # 用于存储每个 epoch 的损失值 losses = [] import time start_time = time.time() # 记录开始时间 for epoch in range(num_epochs): # range是从0开始，所以epoch是从0开始 # 前向传播 outputs = model.forward(X_train) # 显式调用forward函数 # outputs = model(X_train) # 常见写法隐式调用forward函数，其实是用了model类的__call__方法 loss = criterion(outputs, y_train) # output是模型预测值，y_train是真实标签 # 反向传播和优化 optimizer.zero_grad() #梯度清零，因为PyTorch会累积梯度，所以每次迭代需要清零，梯度累计是那种小的bitchsize模拟大的bitchsize loss.backward() # 反向传播计算梯度 optimizer.step() # 更新参数 # 记录损失值 losses.append(loss.item()) # 打印训练信息 if (epoch + 1) % 100 == 0: # range是从0开始，所以epoch+1是从当前epoch开始，每100个epoch打印一次 print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}') time_all = time.time() - start_time # 计算训练时间 print(f'Training time: {time_all:.2f} seconds') import matplotlib.pyplot as plt # 可视化损失曲线 plt.plot(range(num_epochs), losses) plt.xlabel('Epoch') plt.ylabel('Loss') plt.title('Training Loss over Epochs') plt.show()Epoch [100/20000], Loss: 1.0496
Epoch [200/20000], Loss: 0.9832
Epoch [300/20000], Loss: 0.9200
Epoch [400/20000], Loss: 0.8511
Epoch [500/20000], Loss: 0.7790
Epoch [600/20000], Loss: 0.7097
Epoch [700/20000], Loss: 0.6492
......
Epoch [19000/20000], Loss: 0.0620
Epoch [19100/20000], Loss: 0.0618
Epoch [19200/20000], Loss: 0.0617
Epoch [19300/20000], Loss: 0.0616
Epoch [19400/20000], Loss: 0.0615
Epoch [19500/20000], Loss: 0.0614
Epoch [19600/20000], Loss: 0.0613
Epoch [19700/20000], Loss: 0.0612
Epoch [19800/20000], Loss: 0.0611
Epoch [19900/20000], Loss: 0.0610
Epoch [20000/20000], Loss: 0.0609
Training time: 9.49 seconds