莫烦Pytorch系列之分类代码
莫烦Pytorch系列
import torch
#from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
n_data=torch.ones(100,2) # 数据的基本形态
x0=torch.normal(2*n_data,1) # 类型0 x data (tensor), shape=(100, 2)
y0=torch.zeros(100) # 类型0 y data (tensor), shape=(100, 1)
x1=torch.normal(-2*n_data,1) # 类型1 x data (tensor), shape=(100, 1)
y1=torch.ones(100) # 类型1 y data (tensor), shape=(100, 1)
# 注意 x, y 数据的数据形式是一定要像下面一样 (torch.cat 是在合并数据)
x = torch.cat((x0, x1), 0).type(torch.FloatTensor) # FloatTensor = 32-bit floating
y = torch.cat((y0, y1), ).type(torch.LongTensor) # LongTensor = 64-bit integer
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)
#optimizer是一个训练工具
optimizer=torch.optim.SGD(net.parameters(),lr=0.02)
# 算误差的时候, 注意真实值!不是! one-hot 形式的, 而是1D Tensor, (batch,)
# 但是预测值是2D tensor (batch, n_classes)
loss_func = torch.nn.CrossEntropyLoss()
#开始画图
plt.ion()
plt.show()
#开始训练
for t in range(100):
out=net(x)
loss=loss_func(out,y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
#可视化
if t%2==0:
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()