Pytorch 모델 매개변수 보기

import torch
#   torch.nn     
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        # nn.Module                      
        super(Net, self).__init__()
        
        #     '1'          , '6'       ，'3'      3*3
        self.conv1 = nn.Conv2d(1, 6, 3) 
        #   ，  1350   ，  10   
        self.fc1   = nn.Linear(1350, 10)  #   1350       ？       forward  
    #     
    def forward(self, x): 
        print(x.size()) #   ：[1, 1, 32, 32]
        #    ->    ->    
        x = self.conv1(x) #           ，     30，                           。
        x = F.relu(x)
        print(x.size()) #   ：[1, 6, 30, 30]
        x = F.max_pool2d(x, (2, 2)) #       ，     15
        x = F.relu(x)
        print(x.size()) #   ：[1, 6, 15, 15]
        # reshape，‘-1’     
        #                  [1, 6, 15, 15]  ，   [1, 1350]
        x = x.view(x.size()[0], -1) 
        print(x.size()) #     fc1     1350 
        x = self.fc1(x)        
        return x

net = Net()

for parameters in net.parameters():
    print(parameters)

Parameter containing:
tensor([[[[-0.0104, -0.0555,  0.1417],
          [-0.3281, -0.0367,  0.0208],
          [-0.0894, -0.0511, -0.1253]]],


        [[[-0.1724,  0.2141, -0.0895],
          [ 0.0116,  0.1661, -0.1853],
          [-0.1190,  0.1292, -0.2451]]],


        [[[ 0.1827,  0.0117,  0.2880],
          [ 0.2412, -0.1699,  0.0620],
          [ 0.2853, -0.2794, -0.3050]]],


        [[[ 0.1930,  0.2687, -0.0728],
          [-0.2812,  0.0301, -0.1130],
          [-0.2251, -0.3170,  0.0148]]],


        [[[-0.2770,  0.2928, -0.0875],
          [ 0.0489, -0.2463, -0.1605],
          [ 0.1659, -0.1523,  0.1819]]],


        [[[ 0.1068,  0.2441,  0.3160],
          [ 0.2945,  0.0897,  0.2978],
          [ 0.0419, -0.0739, -0.2609]]]])
Parameter containing:
tensor([ 0.0782,  0.2679, -0.2516, -0.2716, -0.0084,  0.1401])
Parameter containing:
tensor([[ 1.8612e-02,  6.5482e-03,  1.6488e-02,  ..., -1.3283e-02,
          1.8715e-02,  5.4037e-03],
        [ 1.8569e-03,  1.8022e-02, -2.3465e-02,  ...,  1.6527e-03,
          2.0443e-02, -2.2009e-02],
        [ 9.9104e-03,  6.6134e-03, -2.7171e-02,  ..., -5.7119e-03,
          2.4532e-02,  2.2284e-02],
        ...,
        [ 6.9182e-03,  1.7279e-02, -1.7783e-03,  ...,  1.9354e-02,
          2.1105e-03,  8.6245e-03],
        [ 1.6877e-02, -1.2414e-02,  2.2409e-02,  ..., -2.0604e-02,
          1.3253e-02, -3.6008e-03],
        [-2.1598e-02,  2.5892e-02,  1.9372e-02,  ...,  1.4159e-02,
          7.0983e-03, -2.3713e-02]])
Parameter containing:
tensor(1.00000e-02 *
       [ 1.4703,  1.0289,  2.5069, -2.2603, -1.5218, -1.7019,  1.2569,
         0.4617, -2.3082, -0.6282])

for name,parameters in net.named_parameters():
    print(name,':',parameters.size())

conv1.weight : torch.Size([6, 1, 3, 3])
conv1.bias : torch.Size([6])
fc1.weight : torch.Size([10, 1350])
fc1.bias : torch.Size([10])