"""
pytorch中数据标签默认的数据格式是LongTensor,即64位的整数
"""
import torch
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
# 制作数据
n_data = torch.ones(100, 2)
x0 = torch.normal(2*n_data, 1) # x0的横纵坐标
y0 = torch.zeros(100) # x0对应的标签
x1 = torch.normal(-2*n_data, 1) # x1的横纵坐标
y1 = torch.ones(100) # x1对应的标签
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
x, y = Variable(x), Variable(y)
# 以下显示出散点图
# plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy(), s=100, lw=0, cmap='RdYlGn')
# 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.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) # 定义网络
print(net) # 打印出网络结构
optimizer = torch.optim.SGD(net.parameters(), lr=0.02)
loss_func = torch.nn.CrossEntropyLoss() # 用于分类问题
plt.ion() # 设置为实时打印
for t in range(100):
out = net(x) # 输入x经过网络的前向传播，得到预测值，此时还不是概率
loss = loss_func(out, y) # 预测值在前，真实值在后
optimizer.zero_grad() # 清除上一次的梯度
loss.backward() # 反向传播，计算梯度
optimizer.step() # 优化梯度
if t % 2 == 0:
# 打印
plt.cla()
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.pause(0.1)
plt.ioff()
plt.show()
