Pytorch 实现简单的二分类

import torch
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# data = np.array(pd.read_csv(r"D:\RegressionData\sin.csv"))
# x = data[:,0]
# y = data[:,-1]
# print(x.shape)
# print(y.shape)
# # x = torch.unsqueeze(torch.from_numpy(x),dim=1)
# x = torch.from_numpy(x)
# y = torch.from_numpy(y)

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)
y = torch.cat((y0,y1),).type(torch.LongTensor)



x, y = Variable(x), Variable(y)
print(x.shape)
print(y.shape)

plt.scatter(x.data.numpy()[:,0],x.data.numpy()[:,1],c = 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(n_feature=2, n_hidden=20,  n_output=2)
print(net)
optimizer = torch.optim.SGD(net.parameters(), lr=0.02)
loss_func = torch.nn.CrossEntropyLoss()

plt.ion()
plt.show()
for t in range(100):
    print("epochs:{}".format(t))
    out = net(x)

    loss = loss_func(out,y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if t % 2 == 0:
        # plot and show learning process
        plt.cla()
        prediction = torch.max(out,1)[1]
        pred_y = prediction.data.numpy()
        true_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 == true_y).astype(int).sum()) / float(true_y.size)
        plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color': 'red'})
        plt.pause(0.1)
plt.ioff()
plt.show()