0.实验效果

1.实验目的

实现用神经网络拟合线性模型的可视化

• 运行python文件的时候可视化

• 运行python文件后保存为gif图

2.运行python文件的时候直接可视化

import torch
from torch import optim
from torch import nn

from matplotlib import pyplot as plt

# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 准备数据
inputs = torch.unsqueeze(torch.linspace(-10,10, 600),dim=1)

targets = 2.55*inputs + 1.89 + torch.normal(0, 1, inputs.shape)

real = 2.55*inputs + 1.89 

# 搭建网络
class NetWork(nn.Module):
    def __init__(self):
        super(NetWork,self).__init__()
        self.linear = torch.nn.Linear(1,1)
    
    def forward(self,x):
        x = self.linear(x)
        return x


# 定义网络
network = NetWork()

# 定义损失函数
loss_fn = nn.MSELoss()

# 定义优化器
optimizer = optim.SGD(network.parameters(),lr=0.001)

fig = plt.figure()

for i in range(100):
    out = network(inputs)
    loss = loss_fn(out,targets)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    # 可视化训练结果
    if i%2 == 0:
        # 清除当前的Axes对象
        plt.cla()
        # 绘制真实的曲线
        plt.plot(inputs.numpy(),real.numpy(),c='blue',lw='3')
        # 绘制有误差的散点图
        plt.plot(inputs.numpy(),targets.numpy(),c='orange')
        # 绘制神经网络实时预测的曲线
        plt.plot(inputs.numpy(),out.data.numpy(),c='red',lw='3')
        plt.text(1, -6, 'Time=%d Loss=%.4f' % (i, loss.data.numpy()), fontdict={'size': 15, 'color': 'red'})
        # 必须暂停以显示
        plt.pause(0.1)
        for name, param in network.named_parameters():
            if param.requires_grad:
                print(name,param)

3.运行python文件后保存为gif图片

运行下面代码后将在同一目录下生成dynamic_images.gif图片。 （如本文开头所示）

• 多个plot怎么放到一帧中

• 更新每一帧中的文字

import torch
from torch import optim
from torch import nn

from matplotlib import pyplot as plt

import matplotlib.animation as animation

# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 准备数据
inputs = torch.unsqueeze(torch.linspace(-10,10, 600),dim=1)

targets = 2.55*inputs + 1.89 + torch.normal(0, 1, inputs.shape)

real = 2.55*inputs + 1.89 

# 搭建网络
class NetWork(nn.Module):
    def __init__(self):
        super(NetWork,self).__init__()
        self.linear = torch.nn.Linear(1,1)
    
    def forward(self,x):
        x = self.linear(x)
        return x



# 定义网络
network = NetWork()


# 定义损失函数
loss_fn = nn.MSELoss()

# 定义优化器
optimizer = optim.SGD(network.parameters(),lr=0.001)

fig, ax = plt.subplots()
ims = []
for i in range(101):
    out = network(inputs)
    loss = loss_fn(out,targets)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    # 可视化训练结果
    if i%2 == 0:
        # 绘制真实的曲线
        frame0, = ax.plot(inputs.numpy(),real.numpy(),c='blue',lw='3') 
        # 绘制有误差的散点图
        frame1, = ax.plot(inputs.numpy(),targets.numpy(),c='orange')
        # 绘制神经网络实时预测的曲线
        frame2, =  ax.plot(inputs.numpy(),out.data.numpy(),c='red',lw='3')
        title = ax.text(1, -6, 'Time=%d Loss=%.4f' % (i, loss.data.numpy()), fontdict={'size': 15, 'color': 'red'})
        # 这里是关键，更新图片中文字
        ims.append([frame0,frame1,frame2,title])
        
ani = animation.ArtistAnimation(fig, ims, interval=50, blit=False,
                                repeat_delay=1000)
ani.save('dynamic_images.gif')

4.REFERENCE

https://stackoverflow.com/questions/49158604/matplotlib-animation-update-title-using-artistanimation