Using Python nn.Module Construct simple full link layer instance

Time:2020-12-2

Python version 3.7 uses Python installed in virtual environment, so you can toss around freely without fear of affecting other Python frameworks

1. First define a class linear, inherit nn.Module

import torch as t
from torch import nn
from torch.autograd import Variable as V
 
class Linear(nn.Module):

  '''because variables automatically derive derivatives, we don't need to implement backward()' "
  def __init__(self, in_features, out_features):
    super().__init__()
    self.w =  nn.Parameter ( t.randn( in_ features, out_ Note that parameter is a special variable
    self.b =  nn.Parameter ( t.randn( out_ (features)), partial value b
  
  Def forward (self, x): the parameter x is a variable object
    x = x.mm( self.w )
    return x + self.b.expand_ As (x) ා let the shape of B match the shape of output X

2. Check it out

layer = Linear( 4,3 )
Input = V (T. randn (2,4)) ා package a variable as input
out = layer( input )
out

#The results are as follows:

tensor([[-2.1934, 2.5590, 4.0233], [ 1.1098, -3.8182, 0.1848]], grad_fn=<AddBackward0>)

Next, we use linear to construct a multi-layer network

class Perceptron( nn.Module ):
  def __init__( self,in_features, hidden_features, out_features ):
    super().__init__()
    self.layer1 = Linear( in_features , hidden_features )
    self.layer2 = Linear( hidden_features, out_features )
  def forward ( self ,x ):
    x = self.layer1( x )
    X = t.sigmoid (x) ා activate function with sigmoid()
    return self.layer2( x )

Test it


perceptron = Perceptron ( 5,3 ,1 )
 
for name,param in perceptron.named_parameters(): 
  print( name, param.size() )

The output is as expected:


layer1.w torch.Size([5, 3])
layer1.b torch.Size([3])
layer2.w torch.Size([3, 1])
layer2.b torch.Size([1])

The above article uses python nn.Module Simple structure, full link layer instance is the small editor to share all the content, I hope to give you a reference, also hope you can support developeppaer.

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