• 用pytorch搭建神经网络，将数据分成两类
• 环境 pytoch1.1.0

1. 生成训练数据

# 准备数据
data = torch.ones(100, 2)
x1 = torch.normal(mean=2*data, std=1)
y1 = torch.zeros(100)    # x1 对应的标签

x2 = torch.normal(mean=-2*data, std=1)
y2 = torch.ones(100)     # x2 对应的标签

train_x = torch.cat((x1, x2), dim=0).type(torch.float32)   # 合并两类数据(x1, x2)，生成训练数据
label_y = torch.cat((y1, y2), dim=0).type(torch.int64)     # 合并两类便签(y1,y2)，生成真实标签


# 通过matplotlib可视化生成的数据
# plt.scatter(train_x.numpy()[:, 0], train_x.numpy()[:, 1])
# plt.show()

2. 搭建神经网络

# 搭建神经网络
class Neuro_net(torch.nn.Module):
    """神经网络"""
    def __init__(self, n_feature, n_hidden_layer, n_output):
        super(Neuro_net, self).__init__()
        self.hidden_layer = torch.nn.Linear(n_feature, n_hidden_layer)
        self.output_layer = torch.nn.Linear(n_hidden_layer, n_output)

    def forward(self, input):
        hidden_out = torch.relu(self.hidden_layer(input))
        out = self.output_layer(hidden_out)
        return out

3. 训练神经网络

num_feature = 2
num_hidden_layer = 10
num_output = 2
epoches = 200
net = Neuro_net(num_feature, num_hidden_layer, num_output)
print(net)   # 查看网络结构

# 优化器
optimizer = torch.optim.SGD(net.parameters(), lr=0.002)
# 定义损失函数
loss_function = torch.nn.CrossEntropyLoss()

for epoch in range(epoches):
    out = net(train_x)
    loss = loss_function(out, label_y)

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

4. 为了更好理解训练过程，加入可视化

import matplotlib.pyplot as plt
plt.ion()   # 画图

for epoch in range(epoches):
    ...
    loss.backward()
    optimizer.step()
    
        if epoch % 5 == 0:
        print("已训练{}步 | loss：{}。".format(epoch, loss))
        plt.cla()
        pridect_y = torch.max(out, dim=1)[1]
        pridect_label = pridect_y.data.numpy()  # 预测的label
        true_label = label_y.data.numpy()       # 真实的label

        plt.scatter(train_x.data.numpy()[:, 0], train_x.data.numpy()[:, 1], c=pridect_label)
        # 计算准确率，显示准确率
        accuracy = float((pridect_label == true_label).astype(int).sum()) / float(true_label.size)
        plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 12, 'color': 'blue'})
        plt.pause(0.1)

plt.ioff()
plt.show()

整体代码：

import torch
import matplotlib.pyplot as plt


# 搭建神经网络
class Neuro_net(torch.nn.Module):
    """神经网络"""
    def __init__(self, n_feature, n_hidden_layer, n_output):
        super(Neuro_net, self).__init__()
        self.hidden_layer = torch.nn.Linear(n_feature, n_hidden_layer)
        self.output_layer = torch.nn.Linear(n_hidden_layer, n_output)

    def forward(self, input):
        hidden_out = torch.relu(self.hidden_layer(input))
        out = self.output_layer(hidden_out)
        return out

## 准备数据
data = torch.ones(100, 2)
x1 = torch.normal(mean=2*data, std=1)
y1 = torch.zeros(100)    # x1 对应的标签

x2 = torch.normal(mean=-2*data, std=1)
y2 = torch.ones(100)     # x2 对应的标签

train_x = torch.cat((x1, x2), dim=0).type(torch.float32)   # 合并两类数据(x1, x2)，生成训练数据
label_y = torch.cat((y1, y2), dim=0).type(torch.int64)     # 合并两类便签(y1,y2)，生成真实标签

# # 查看数据分布
# plt.scatter(train_x.numpy()[:, 0], train_x.numpy()[:, 1])
# plt.show()

## 进行训练
num_feature = 2
num_hidden_layer = 10
num_output = 2
epoches = 200
net = Neuro_net(num_feature, num_hidden_layer, num_output)
print(net)   # 查看网络结构

plt.ion()
# 优化器
optimizer = torch.optim.SGD(net.parameters(), lr=0.002)
# 定义损失函数
loss_function = torch.nn.CrossEntropyLoss()

for epoch in range(epoches):
    out = net(train_x)
    loss = loss_function(out, label_y)

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

    if epoch % 5 == 0:
        print("已训练{}步 | loss：{}。".format(epoch, loss))
        plt.cla()
        pridect_y = torch.max(out, dim=1)[1]
        pridect_label = pridect_y.data.numpy()  # 预测的label
        true_label = label_y.data.numpy()       # 真实的label

        plt.scatter(train_x.data.numpy()[:, 0], train_x.data.numpy()[:, 1], c=pridect_label)
        # 计算准确率，显示准确率
        accuracy = float((pridect_label == true_label).astype(int).sum()) / float(true_label.size)
        plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 12, 'color': 'blue'})
        plt.pause(0.1)

plt.ioff()
plt.show()

可视化动态拟合图