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前言

介绍了如何处理多维特征的输入问题

一、模型说明

多维问题分类模型

二、示例

1.求解步骤

1.载入数据集：数据集用路径D:\anaconda\Lib\site-packages\sklearn\datasets\data下的diabetes.csv，输入有8个维度
2.创建模型：维度8-6-4-2-1
3.选择损失函数和优化器
3.进行训练

2.示例代码

代码如下（示例）：

import numpy as np
import torch
import matplotlib.pyplot as plt

# prepare dataset
xy = np.loadtxt('diabetes.csv', delimiter=',', dtype=np.float32)
x_data = torch.from_numpy(xy[:, :-1])  # 第一个‘：’是指读取所有行，第二个‘：’是指从第一列开始，最后一列不要
print("input data.shape", x_data.shape)
y_data = torch.from_numpy(xy[:, [-1]])  # [-1] 最后得到的是个矩阵


# print(x_data.shape)
# design model using class


class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(8, 6)
        self.linear2 = torch.nn.Linear(6, 4)
        self.linear3 = torch.nn.Linear(4, 2)
        self.linear4 = torch.nn.Linear(2, 1)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self, x):
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))  # y hat
        x = self.sigmoid(self.linear4(x))  # y hat
        return x


model = Model()

# construct loss and optimizer
criterion = torch.nn.BCELoss(size_average = True)

optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

epoch_list = []
loss_list = []
# training cycle forward, backward, update
for epoch in range(1000):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    # print(epoch, loss.item())
    print(epoch, loss.item())
    epoch_list.append(epoch)
    loss_list.append(loss.item())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if epoch % 100 == 99:
        y_pred_label = torch.where(y_pred >= 0.5, torch.tensor([1.0]), torch.tensor([0.0]))

        acc = torch.eq(y_pred_label, y_data).sum().item() / y_data.size(0)
        print("loss = ", loss.item(), "acc = ", acc)


plt.plot(epoch_list, loss_list)
plt.ylabel('loss')
plt.xlabel('epoch')
plt.show()

得到如下结果：

总结

PyTorch学习6：多维特征输入