莫烦Pytorch神经网络第三部分代码修改
2021/6/20 23:28:37
本文主要是介绍莫烦Pytorch神经网络第三部分代码修改，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！
3.1Regression回归
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt
"""
创建数据
"""
x = torch.unsqueeze(torch.linspace(-1,1,100),dim=1)
y = x.pow(2) + 0.2*torch.rand(x.size()) #增加噪点
x , y = Variable(x),Variable(y)
# plt.scatter(x.data.numpy(),y.data.numpy()) #打印数据
# plt.show()
"""
搭建网络
"""
class Net(torch.nn.Module):
def __init__(self,n_features,n_hidden,n_out):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_features,n_hidden)
self.predict = torch.nn.Linear(n_hidden,n_out)
def forward(self,x):
x = F.relu(self.hidden(x))
x = self.predict(x)
return x
net = Net(1,10,1)
# print(net)
plt.ion() #实时打印的
plt.show()
"""
优化网络
"""
optimizer = torch.optim.SGD(net.parameters(),lr=0.5)
loss_func = torch.nn.MSELoss() #MSELoss是用在线性预测
#打印环节
for t in range(100):
prediction = net(x)
loss = loss_func(prediction,y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if t % 5 ==0:
plt.cla()
plt.scatter(x.data.numpy(),y.data.numpy())
plt.plot(x.data.numpy(),prediction.data.numpy(),'r-',lw=5)
plt.text(0.5,0,'Loss=%.4f' % loss.item(),fontdict={'size':20,'color':'red'}) #注意莫老师这里loss.data[0]得换成loss.item()
plt.pause(0.1)
plt.ioff()
plt.show()
3.2Classification分类
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt
"""
创建数据
"""
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)
# plt.scatter(x.data.numpy()[:,0],x.data.numpy()[:,1],c=y.data.numpy(),s=100,lw=0,cmap='RdYlGn')
# plt.show()
"""
网络搭建
"""
class Net(torch.nn.Module):
def __init__(self,n_features,n_hidden,n_out):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_features,n_hidden)
self.predict = torch.nn.Linear(n_hidden,n_out)
def forward(self,x):
x = F.relu(self.hidden(x))
x = self.predict(x)
return x
net = Net(2,10,2)
# print(net)
plt.ion() #实时打印的
plt.show()
optimizer = torch.optim.SGD(net.parameters(),lr=0.02)
loss_func = torch.nn.CrossEntropyLoss() #CrossEntropyLoss用在分类的损失函数中
"""
结果打印
"""
for t in range(100):
out = net(x)
loss = loss_func(out,y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if t % 2 == 0:
plt.cla()
prediction = torch.max(F.softmax(out),1)[1] #输出的结果在第二位，因为输出是二维，例如输出结果为[0,1],是指最大值为0，类型是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()
3.3快速搭建法
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt
"""
创建数据
"""
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)
# plt.scatter(x.data.numpy()[:,0],x.data.numpy()[:,1],c=y.data.numpy(),s=100,lw=0,cmap='RdYlGn')
# plt.show()
"""
普通网络搭建
"""
class Net(torch.nn.Module):
def __init__(self,n_features,n_hidden,n_out):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_features,n_hidden)
self.predict = torch.nn.Linear(n_hidden,n_out)
def forward(self,x):
x = F.relu(self.hidden(x))
x = self.predict(x)
return x
net1 = Net(2,10,2)
"""
快速网络搭建
"""
net2 = torch.nn.Sequential(
torch.nn.Linear(2,10),
torch.nn.ReLU(),
torch.nn.Linear(10,2)
)
print(net1)
print(net2)
3.4保存提取
import torch
from torch.autograd import Variable
import matplotlib.pyplot as plt
#fake data
x = torch.unsqueeze(torch.linspace(-1,1,100),dim=1)
y = x.pow(2) + 0.2*torch.rand(x.size())
x,y = Variable(x,requires_grad=False),Variable(y,requires_grad=False)
"""
保存
"""
def save():
net1 = torch.nn.Sequential(
torch.nn.Linear(1, 10),
torch.nn.ReLU(),
torch.nn.Linear(10, 1)
)
optimizer = torch.optim.SGD(net1.parameters(), lr=0.5)
loss_func = torch.nn.MSELoss()
for t in range(100):
prediction = net1(x)
loss = loss_func(prediction, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
torch.save(net1,'net.pkl') #保存网络
torch.save(net1.state_dict(),'net_params.pkl') #保存参数
#画图
plt.figure(1,figsize=(10,3))
plt.subplot(131)
plt.title('Net1')
plt.scatter(x.data.numpy(),y.data.numpy())
plt.plot(x.data.numpy(),prediction.data.numpy(),'r-',lw=5)
"""
提取网络模型
"""
def restore_net():
net2 = torch.load('net.pkl')
prediction = net2(x)
plt.subplot(132)
plt.title('Net2')
plt.scatter(x.data.numpy(), y.data.numpy())
plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)
"""
提取网络参数
"""
def restore_params():
net3 = torch.nn.Sequential(
torch.nn.Linear(1, 10),
torch.nn.ReLU(),
torch.nn.Linear(10, 1)
)
net3.load_state_dict(torch.load('net_params.pkl'))
prediction = net3(x)
plt.subplot(133)
plt.title('Net3')
plt.scatter(x.data.numpy(), y.data.numpy())
plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)
plt.show()
save()
restore_net()
restore_params()
3.5批数据训练
import torch
import torch.utils.data as Data
BATCH_SIZE = 5
x = torch.linspace(1,10,10)
y = torch.linspace(10,1,10)
#torch_dataset = Data.TensorDataset(data_tensor=x,target_tensor=y) #莫老师使用的这个方法在高版本报错 使用下边的语句可以解决
torch_dataset = Data.TensorDataset(x,y)
loader = Data.DataLoader(
dataset=torch_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
#num_workers=2, #线程数 windows用户这里要去掉 因为windows系统中没有Fork函数，多线程会报错
)
for epoch in range(3):
for step,(batch_x,batch_y) in enumerate(loader):
#training
print('Epoch:',epoch,'|Step:',step,'|batch x:',batch_x.numpy(),'|batch y:',batch_y.numpy())
3.6Optimizer优化器
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt
import torch.utils.data as Data
#hyper parameters
LR = 0.01
BATCH_SIZE = 32
EPOCH = 12
x = torch.unsqueeze(torch.linspace(-1,1,100),dim=1)
y = x.pow(2) + 0.2*torch.rand(x.size())
# plt.scatter(x.numpy(),y.numpy())
# plt.show()
torch_dataset = Data.TensorDataset(x,y)
loader = Data.DataLoader(dataset=torch_dataset,batch_size=BATCH_SIZE,shuffle=True)
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(1,20)
self.predict = torch.nn.Linear(20,1)
def forward(self,x):
x = F.relu(self.hidden(x))
x = self.predict(x)
return x
net_SGD = Net()
net_Momentum = Net()
net_RMSprop = Net()
net_Adam = Net()
nets = [net_SGD,net_Momentum,net_RMSprop,net_Adam]
opt_SGD = torch.optim.SGD(net_SGD.parameters(),lr=LR)
opt_Momentum = torch.optim.SGD(net_Momentum.parameters(),lr=LR,momentum=0.8)
opt_RMSprop = torch.optim.RMSprop(net_RMSprop.parameters(),lr=LR,alpha=0.9)
opt_Adam = torch.optim.Adam(net_Adam.parameters(),lr=LR,betas=(0.9,0.99))
optimizers = [opt_SGD,opt_Momentum,opt_RMSprop,opt_Adam]
loss_func = torch.nn.MSELoss()
losses_his=[[],[],[],[]] #记录损失
for epoch in range(EPOCH):
print(epoch)
for step,(batch_x,batch_y) in enumerate(loader):
# b_x = Variable(batch_x) #新版本pytorch不用这个了
# b_y = Variable(batch_y)
for net,opt,l_his in zip(nets,optimizers,losses_his):
output = net(batch_x)
loss = loss_func(output,batch_y)
opt.zero_grad()
loss.backward()
opt.step()
l_his.append(loss.item())
labels = ['SGD','Momentum','RMSprop','Adam']
for i,l_his in enumerate(losses_his):
plt.plot(l_his,label = labels[i])
plt.legend(loc = 'best')
plt.xlabel('Steps')
plt.ylabel('Loss')
plt.show()
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