成功搭建神经网络（二）

关系拟合（分类）

一、建立数据集

import torch
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt

 

# make fake data
n_data = torch.ones(100, 2) # 数据的基本形态
x0 = torch.normal(2*n_data, 1) # class0 x data (tensor), shape=(100, 2)
y0 = torch.zeros(100) # class0 y data (tensor), shape=(100, 1)
x1 = torch.normal(-2*n_data, 1) # class1 x data (tensor), shape=(100, 2)
y1 = torch.ones(100) # class1 y data (tensor), shape=(100, 1)

 

#torch.cat 是在合并数据
x = torch.cat((x0, x1), 0).type(torch.FloatTensor) # shape (200, 2) FloatTensor = 32-bit floating
y = torch.cat((y0, y1), ).type(torch.LongTensor) # shape (200,) LongTensor = 64-bit integer

# torch can only train on Variable, so convert them to Variable
x, y = Variable(x), Variable(y)

plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy(), s=100, lw=0, cmap='RdYlGn')
plt.show()

运行截图：

二、建立神经网络

class Net(torch.nn.Module):
    def __init__(self, n_feature, n_hidden, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer
        self.out = torch.nn.Linear(n_hidden, n_output)   # output layer

    def forward(self, x):
        x = F.relu(self.hidden(x))      # activation function for hidden layer
        x = self.out(x)
        return x

net = Net(n_feature=2, n_hidden=10, n_output=2)     # 定义神经网络，几个类别就几个 output
print(net)  # net architecture

运行截图：

 可见，搭建的神经网络输入的特征值有2类，隐藏层神经元有10个，输出值也是2类。

三、训练网络

# Loss and Optimizer
# Softmax is internally computed.
# Set parameters to be updated.
optimizer = torch.optim.SGD(net.parameters(), lr=0.02)
loss_func = torch.nn.CrossEntropyLoss()  

分类问题中损失函数用交叉熵表示，详情见对交叉熵的理解 - Tzy0425 - 博客园 (cnblogs.com)

四、对搭建的神经网络进行优化然后输出结果

for t in range(100):
    out = net(x)                 # input x and predict based on x
    loss = loss_func(out, y)     # must be (1. nn output, 2. target), the target label is NOT one-hotted

    optimizer.zero_grad()   # clear gradients for next train
    loss.backward()         # backpropagation, compute gradients
    optimizer.step()        # apply gradients
    
    if t % 10 == 0 or t in [3, 6]:
        # plot and show learning process
        plt.cla()
        # 经过一次 softmax 的激励函数后的最大概率才是预测值
        prediction = torch.max(F.softmax(out), 1)[1]
        pred_y = prediction.data.numpy().squeeze()
        target_y = y.data.numpy()
        plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=pred_y, s=100, lw=0, cmap='RdYlGn')
        accuracy = sum(pred_y == target_y)/200.
        plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color':  'red'})
        plt.show()
        plt.pause(0.1)

plt.ioff()         #停止画图

运行截图：