Pytroch学习笔记（2）–保存提取|莫凡python

import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt

n_data = torch.ones(100,2)
x0 = torch.normal(2*n_data,1)
y0 = torch.zeros(100)
x1 = torch.normal(-2*n_data,1)
y1 = torch.ones(100)

x = torch.cat((x0,x1),0).type(torch.FloatTensor)  # 200*2
y = torch.cat((y0,y1),).type(torch.LongTensor)  # 200*1


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.out = torch.nn.Linear(n_hidden, n_output)

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


net = Net(n_feature=2, n_hidden= 10, n_output=2)

print(net)

optimizer = torch.optim.SGD(net.parameters(), lr=0.02)
loss_func = torch.nn.CrossEntropyLoss()

plt.ion()

for t in range(100):
    out = net(x)

    loss = loss_func(out, y)

    optimizer.zero_grad()

    loss.backward()

    optimizer.step()

    if t % 5 == 0:
        plt.cla()
        prediction = torch.max(F.softmax(out, dim=0), 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 = float((pred_y == target_y).astype(int).sum()) / float(target_y.size)
        plt.text(1.5, 4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color': 'red'})
        plt.pause(0.1)
        
plt.ioff()
plt.show()