0.实验效果

1.实验目的

• 巩固matplotlib中的动态画图过程

• 实现用神经网络去拟合一个三次函数

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(-4,4, 600),dim=1)
# 添加随机噪声~N(0,1)
targets = inputs.pow(3) + 1.89 + 3 * torch.randn(inputs.shape)

real = inputs.pow(3) + 1.89 

# 搭建网络
class NetWork(nn.Module):
    def __init__(self,n_feature,n_hidden,n_output):
        super(NetWork,self).__init__()
        self.m = torch.nn.Sequential(
            torch.nn.Linear(n_feature,n_hidden),
            torch.nn.ReLU(),
            torch.nn.Linear(n_hidden,n_output)
        )
    
    def forward(self,x):
        return self.m(x)

# 定义网络
network = NetWork(1,100,1)


# 定义损失函数
loss_fn = nn.MSELoss()

# 定义优化器
optimizer = optim.Adam(network.parameters(),lr=0.1)

for i in range(10000):
    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:
        # 清空上一次显示的结果
        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'})
        plt.pause(0.1)

3.运行python文件后保存为gif图片

import torch
from torch import optim
from torch import nn

from matplotlib import pyplot as plt
from matplotlib.animation import ArtistAnimation

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

# 准备数据
inputs = torch.unsqueeze(torch.linspace(-4,4, 600),dim=1)
# 添加随机噪声~N(0,1)
targets = inputs.pow(3) + 1.89 + 3 * torch.randn(inputs.shape)

real = inputs.pow(3) + 1.89 

# 搭建网络
class NetWork(nn.Module):
    def __init__(self,n_feature,n_hidden,n_output):
        super(NetWork,self).__init__()
        self.m = torch.nn.Sequential(
            torch.nn.Linear(n_feature,n_hidden),
            torch.nn.ReLU(),
            torch.nn.Linear(n_hidden,n_output)
        )
    
    def forward(self,x):
        return self.m(x)



# 定义网络
network = NetWork(1,100,1)

# 定义损失函数
loss_fn = nn.MSELoss()

# 定义优化器
optimizer = optim.Adam(network.parameters(),lr=0.1)

fig, ax = plt.subplots()

ims = []

for i in range(200):
    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, = plt.plot(inputs.numpy(),real.numpy(),c='blue',lw='3')
        # 绘制有误差的散点图
        frame1, = plt.plot(inputs.numpy(),targets.numpy(),c='orange')
        # 绘制神经网络实时预测的曲线
        frame2, = plt.plot(inputs.numpy(),out.data.numpy(),c='red',lw='3')
        frame3 = plt.text(1, -6, 'Time=%d Loss=%.4f' % (i, loss.data.numpy()), fontdict={'size': 15, 'color': 'red'})
        ims.append([frame0,frame1,frame2,frame3])

ani = ArtistAnimation(fig, ims, interval=100, blit=False,
                                repeat_delay=1000)
ani.save('x_3.gif')