PyTorch实现Classification分类

跟着莫凡大神学习

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)
# https://ptorch.com/docs/1/torchlists
x0 = torch.normal(2*n_data,1) # class0 x data (tensor), shape=(100, 2)
# torch.normal(means, std, out=None)   means (Tensor) – 均值 , std (Tensor) – 标准差,  out (Tensor) – 可选的输出张量
y0 = torch.zeros(100)
x1 = torch.normal(-2*n_data,1)  # class1 x data (tensor), shape=(100, 2)
y1 = torch.ones(100)

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



x,y=Variable(x),Variable(y)

# plt.scatter(x.data.numpy(),y.data.numpy())
# plt.show()

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)
        self.predict = torch.nn.Linear(n_hidden,n_output)

    def forward(self, x):
        x=F.relu(self.hidden(x))
        x=self.predict(x)
        return x


net =Net(2,10,2)  # define the network
plt.ion()  # something about plotting
plt.show()

optimizer =torch.optim.SGD(net.parameters(),lr=0.002)  #优化参数
loss_func = torch.nn.CrossEntropyLoss()  # the target label is NOT an one-hotted

for t in range(100):
    out =net(x)   #开始训练

    loss = loss_func(out,y)  # 一定要预测的值在前，真实值在后

# below are
    optimizer.zero_grad()  # clear gradients for next train
    loss.backward()        # backpropagation, compute gradients
    optimizer.step()
    if t % 2==0:  # 每训练2次 ，打印一次
        # plot and show learning process
        plt.cla()
        prediction = torch.max(out,1)[1]  # why is 1
        predy = prediction.data.numpy().squeeze()
        target_y = y.data.numpy()
        plt.scatter(x.data.numpy()[:,0], x.data.numpy()[:,1],c=predy,s=100,lw=0,cmap='RdYlGn')
        accuracy = sum(predy == target_y)/200
        plt.text(1.5,-4,'Accuracy=%.2f' % accuracy,fOntdict={'size':20,'color':'red'})
        plt.pause(0.1)

    plt.ioff()
    plt.show()

刚开始不太清楚上面使用的数据，所以自己做了一些其他测试

import torchn_data =torch.ones(4,2)print(n_data)x0 = torch.normal(2*n_data,1)print('x0\n', x0)y0 = torch.zeros(4)print('yo\n',y0)x1 = torch.normal(-2*n_data,1)print('x1\n', x1)y1 = torch.ones(4)print('y1\n',y1)x=torch.cat((x0,x1),0).type(torch.FloatTensor) # shape (200, 2) FloatTensor = 32-bit floatingprint('x\n',x)y=torch.cat((y0,y1),0).type(torch.LongTensor) # shape (200,) LOngTensor= 64-bit integerprint('y\n',y)

输出结果如下：

 1  1 1  1 1  1 1  1[torch.FloatTensor of size 4x2]x0  0.2261  3.0315 2.0241  1.5661 4.7188  2.0684 1.8433  2.0262[torch.FloatTensor of size 4x2]yo  0 0 0 0[torch.FloatTensor of size 4]x1 -0.4156 -1.0854-1.5244 -1.1929-2.2120 -0.3639-1.4513 -2.1948[torch.FloatTensor of size 4x2]y1  1 1 1 1[torch.FloatTensor of size 4]x  （PS： 二维平面的坐标）  0.2261  3.0315 2.0241  1.5661 4.7188  2.0684 1.8433  2.0262-0.4156 -1.0854-1.5244 -1.1929-2.2120 -0.3639-1.4513 -2.1948[torch.FloatTensor of size 8x2]y （针对二维平面每个坐标的 标签）  0 0 0 0 1 1 1 1[torch.LongTensor of size 8]Process finished with exit code 0