神经网络案例实战

def create_dataset():
 
    data = pd.read_csv('predict.csv')
 
    # 特征值和目标值
    x, y = data.iloc[:, :-1], data.iloc[:, -1]
    x = x.astype(np.float32)
    y = y.astype(np.int64)
 
    # 数据集划分
    x_train, x_valid, y_train, y_valid = train_test_split(x, y, train_size=0.8, random_state=88, stratify=y)
 
    # 构建数据集
    train_dataset = TensorDataset(torch.tensor(x_train.values), torch.tensor(y_train.values))
    valid_dataset = TensorDataset(torch.tensor(x_valid.values), torch.tensor(y_valid.values))
 
    return train_dataset, valid_dataset, x_train.shape[1], len(np.unique(y))
 
 
train_dataset, valid_dataset, input_dim, class_num = create_dataset()

# 构建网络模型
class PhonePriceModel(nn.Module):
 
    def __init__(self, input_dim, output_dim):
        super(PhonePriceModel, self).__init__()
 
        self.linear1 = nn.Linear(input_dim, 128) # 输入层到隐藏层的线性变换
        self.linear2 = nn.Linear(128, 256)  # 隐藏层到输出层的线性变换
        self.linear3 = nn.Linear(256, output_dim)  # 输出层到最终输出的线性变换
 
    def _activation(self, x):
        return torch.sigmoid(x)
 
    def forward(self, x):
 
        x = self._activation(self.linear1(x))
        # 通过第一层线性变换和激活函数
        x = self._activation(self.linear2(x))
        # 通过第二层线性变换和激活函数
        output = self.linear3(x)
        # 通过第三层线性变换得到最终输出
 
        return output

def train():
 
    # 固定随机数种子
    torch.manual_seed(66)
 
    
    model = PhonePriceModel(input_dim, class_num)
    # 损失函数
    criterion = nn.CrossEntropyLoss()
    # 优化方法
    optimizer = optim.SGD(model.parameters(), lr=1e-3)
    
    num_epoch = 50
 
    for epoch_idx in range(num_epoch):
 
        # 初始化数据加载器
        dataloader = DataLoader(train_dataset, shuffle=True, batch_size=8)
        # 训练时间
        start = time.time()
        # 计算损失
        total_loss = 0.0
        total_num = 1
        # 准确率
        correct = 0
 
        for x, y in dataloader:
 
            output = model(x)
            # 计算损失
            loss = criterion(output, y)
            # 梯度清零
            optimizer.zero_grad()
            # 反向传播
            loss.backward()
            # 参数更新
            optimizer.step()
 
            total_num += len(y)
            total_loss += loss.item() * len(y)
 
        print('epoch: %4s loss: %.2f, time: %.2fs' %
              (epoch_idx + 1, total_loss / total_num, time.time() - start))
 
    # 模型保存
    torch.save(model.state_dict(), 'price-model.bin')

def test():
 
    # 加载模型
    model = PhonePriceModel(input_dim, class_num)
    model.load_state_dict(torch.load('price-model.bin'))
 
    # 构建加载器
    dataloader = DataLoader(valid_dataset, batch_size=8, shuffle=False)
 
    # 评估测试集
    correct = 0
    for x, y in dataloader:
 
        output = model(x)
        y_pred = torch.argmax(output, dim=1)
        correct += (y_pred == y).sum()
 
    print('Acc: %.5f' % (correct.item() / len(valid_dataset)))