pytorch搭建神經(jīng)網(wǎng)絡(luò)是很簡單明了的，這里介紹兩種自己常用的搭建模式：
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import torchimport torch.nn as nn |
first：
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class NN(nn.Module): def __init__(self): super(NN,self).__init__() self.model=nn.Sequential( nn.Linear(30,40), nn.ReLU(), nn.Linear(40,60), nn.Tanh(), nn.Linear(60,10), nn.Softmax() ) self.model[0].weight.data.uniform_(-3e-3, 3e-3) self.model[0].bias.data.uniform(-1,1) def forward(self,states): return self.model(states) |
這一種是將整個網(wǎng)絡(luò)寫在一個Sequential中，網(wǎng)絡(luò)參數(shù)設(shè)置可以在網(wǎng)絡(luò)搭建好后單獨設(shè)置：self.model[0].weight.data.uniform_(-3e-3,3e-3)，這是設(shè)置第一個linear的權(quán)重是（-3e-3，3e-3）之間的均勻分布,bias是-1至1之間的均勻分布。
second：
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class NN1(nn.Module): def __init__(self): super(NN1,self).__init__() self.Linear1=nn.Linear(30,40) self.Linear1.weight.data.fill_(-0.1) #self.Linear1.weight.data.uniform_(-3e-3,3e-3) self.Linear1.bias.data.fill_(-0.1) self.layer1=nn.Sequential(self.Linear1,nn.ReLU()) self.Linear2=nn.Linear(40,60) self.layer2=nn.Sequential(self.Linear2,nn.Tanh()) self.Linear3=nn.Linear(60,10) self.layer3=nn.Sequential(self.Linear3,nn.Softmax()) def forward(self,states): return self.model(states) |
網(wǎng)絡(luò)參數(shù)的設(shè)置可以在定義完線性層之后直接設(shè)置如這里對于第一個線性層是這樣設(shè)置：self.Linear1.weight.data.fill_(-0.1),self.Linear1.bias.data.fill_(-0.1)。
你可以看一下這樣定義完的參數(shù)的效果：
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Net=NN()print("0:",Net.model[0])print("weight:",type(Net.model[0].weight))print("weight:",type(Net.model[0].weight.data))print("bias",Net.model[0].bias.data)print('1:',Net.model[1])#print("weight:",Net.model[1].weight.data)print('2:',Net.model[2])print('3:',Net.model[3])#print(Net.model[-1])Net1=NN1() |
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print(Net1.Linear1.weight.data) |
輸出：
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0: Linear (30 -> 40)weight: <class 'torch.nn.parameter.Parameter'>weight: <class 'torch.FloatTensor'>bias -0.6287-0.6573-0.0452 0.9594-0.7477 0.1363-0.1594-0.1586 0.0360 0.7375 0.2501-0.1371 0.8359-0.9684-0.3886 0.7200-0.3906 0.4911 0.8081-0.5449 0.9872 0.2004 0.0969-0.9712 0.0873 0.4562-0.4857-0.6013 0.1651 0.3315-0.7033-0.7440 0.6487 0.9802-0.5977 0.3245 0.7563 0.5596 0.2303-0.3836[torch.FloatTensor of size 40]1: ReLU ()2: Linear (40 -> 60)3: Tanh ()-0.1000 -0.1000 -0.1000 ... -0.1000 -0.1000 -0.1000-0.1000 -0.1000 -0.1000 ... -0.1000 -0.1000 -0.1000-0.1000 -0.1000 -0.1000 ... -0.1000 -0.1000 -0.1000 ... ? ... -0.1000 -0.1000 -0.1000 ... -0.1000 -0.1000 -0.1000-0.1000 -0.1000 -0.1000 ... -0.1000 -0.1000 -0.1000-0.1000 -0.1000 -0.1000 ... -0.1000 -0.1000 -0.1000[torch.FloatTensor of size 40x30]Process finished with exit code 0 |
這里要注意self.Linear1.weight的類型是網(wǎng)絡(luò)的parameter。而self.Linear1.weight.data是FloatTensor。
以上這篇關(guān)于pytorch中全連接神經(jīng)網(wǎng)絡(luò)搭建兩種模式詳解就是小編分享給大家的全部內(nèi)容了，希望能給大家一個參考，也希望大家多多支持服務(wù)器之家。
原文鏈接：https://blog.csdn.net/geter_CS/article/details/80015957
