输入-加工-输出
PyTorch 是Torch 在 Python上的衍生.因为Torch是一个使用Lua语言的神经网络库,Torch 很好用，但是 Lua又不是特别流行,所有开发团队将Lua的Torch移植到了更流行的语言Python 上.
import torch
import numpy as np
线性，非线性
y=AF（Wx）
AF就是激励函数（relu（卷积），sigmoid，tanh（循环神经网络），softplus）
import torch
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
x = torch.unsqueeze(torch.linspace(-1,1,100),dim=1)
y = x.pow(2) + 0.2*torch.rand(x.size())
x,y =Variable(x),Variable(y)
plt.scatter(x.data.numpy(),y.data.numpy())
plt.show()
class Net(torch.nn.Module):
def __init__(self,n_features,n_hidden,n_output):
super(Net,self).__init__()
self.hidden =torch.nn.Linear(n_features,n_hidden)
self.predict =torch.nn.Linear(n_hidden,n_output)
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()
plt.show()
optimizer = torch.optim.SGD(net.parameters(),lr=0.5)
loss_func = torch.nn.MSELoss()
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.data[0], fontdict={'size': 20, 'color': 'red'})
plt.pause(0.1)
plt.ioff()
plt.show()
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(x)
loader = Data.DataLoader(
dataset=torch_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=2,
)
for epoch in range(3):
for step,(batch_x,batch_y) in enumerate(loader):
print('Epoch:',epoch,'| Step:',step,'| batch x:',
batch_x.numpy(),'| batch y:',batch_y.numpy)
import torch.utils.data as Data
from torch.utils.data import TensorDataset
BATCH_SIZE = 5
x = torch.linspace(1,10,10)
y = torch.linspace(10,1,10)
torch_dataset = Data.TensorDataset(x)
loader = Data.DataLoader(
dataset=torch_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=2,
)
for epoch in range(3):
for step,(batch_x,batch_y) in enumerate(loader):
print('Epoch:',epoch,'| Step:',step,'| batch x:',
batch_x.numpy(),'| batch y:',batch_y.numpy)
Momentum 参数
AdaGrad 学习率改变
RMSProp 两者结合
Adam 结合改进
输入-卷积-池化-全连接-分类器
class CNN(nn.Module):
def __init__(self):
super(CNN,self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(
in_channels=1,
out_channels=16,
kernel_size=5,
stride=1,
padding=2,
),
nn.ReLU(),
nn.MaxPool1d()
)
NN（分析）
RNN（误差（信息源到达终点，得到误差），梯度消失（反向传递得到误差，乘以自己（小于0）），梯度爆炸（大于1时））
LSTM RNN(长短期记忆) 输入，输出，忘记控制
(回归)
纬度值,
压缩-解压(incode-encode)
PCA(给特征属性降维)
自编码
class AutoEncoder(nn.Module):
def __init__(self):
super(AutoEncoder,self).__init__()
self.encoder = nn.Sequential(
nn.Linear(28*28,12),
nn.Tanh(),
nn.Linear(128,64),
nn.Tanh(),
nn.Linear(64,12),
nn.Tanh(),
nn.Linear(12, 3),
)
self.decoder = nn.Sequential(
nn.Linear(3, 12),
nn.Tanh(),
nn.Linear(12, 64),
nn.Tanh(),
nn.Linear(64, 128),
nn.Tanh(),
nn.Linear(128, 28*28),
nn.Sigmoid(),
)
def forward(self,x):
encoder = self.encoder(x)
decoded = self.decoder(encoder)
return encoder,decoded
autoencoder = AutoEncoder()
optimizer = torch.optim.Adam(autoencoder.parameters(),lr=LR)
loss_func = nn.MSELoss()
encoded,decoded = autoencoder(b_x)
Q-learning(离线学习经历)
q值(神经网络)
接收信息-生成新的
机器模型过于自信，（自负）
解决：增加数据量，正规化（y=Wx+ans（w1））
dropout
batch-normolization
p（数据） -处理
x-全连接层-激励函数
Original: https://blog.csdn.net/Hulk_liu/article/details/123848902
Author: Hulk_liu
Title: pytorch神经网络
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