pytorch搭建神經網絡是很簡單明了的，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) |
這一種是將整個網絡寫在一個Sequential中，網絡參數設置可以在網絡搭建好后單獨設置：self.model[0].weight.data.uniform_(-3e-3,3e-3)，這是設置第一個linear的權重是（-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) |
網絡參數的設置可以在定義完線性層之后直接設置如這里對于第一個線性層是這樣設置：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的類型是網絡的parameter。而self.Linear1.weight.data是FloatTensor。
以上這篇關于pytorch中全連接神經網絡搭建兩種模式詳解就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持服務器之家。
原文鏈接：https://blog.csdn.net/geter_CS/article/details/80015957
