(三)PyTorch神经网络结构及模型结构及参数深度解析

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1 神经网络结构

import numpy as np
import torch
from torch.autograd import Variable
import matplotlib.pyplot as plt
import torch.nn.functional as F

from matplotlib.font_manager import FontProperties
from mpl_toolkits.mplot3d import Axes3D
# Ubuntu system font path
font = FontProperties(fname='/usr/share/fonts/truetype/arphic/ukai.ttc')
PI = np.pi
class DynamicNet(torch.nn.Module):
    def __init__(self, D_in, H, D_out):
        super(DynamicNet, self).__init__()
        self.input_linear = torch.nn.Linear(D_in, H)
        self.output_linear = torch.nn.Linear(H, D_out)
    def forward(self, x):
        h_relu = self.input_linear(x).clamp(min=0)
        y_pred = self.output_linear(h_relu)
        return y_pred

N, D_in, H, D_out = 100, 1, 100, 1
x = torch.unsqueeze(torch.linspace(-PI, PI, 100), dim=1)
y = torch.sin(x) + 0.2 * torch.rand(x.size())
print("x size: {}".format(x.size()))
print("y size: {}".format(y.size))

model = DynamicNet(D_in, H, D_out)
criterion = torch.nn.MSELoss(reduction="sum")
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9)

print("model inside: {}".format(model))
print("Model's state_dict:")
for param_tensor in model.state_dict():
    print(param_tensor, "\t", model.state_dict()[param_tensor].size())
print("Optimizer's state_dict:")
for var_name in optimizer.state_dict():
    print(var_name, "\t", optimizer.state_dict()[var_name])

• Result

x size: torch.Size([100, 1])
y size: <built-in method size of Tensor object at 0x7f647c8b6ca8>
model inside: 
DynamicNet(
  (input_linear): Linear(in_features=1, out_features=100, bias=True)
  (output_linear): Linear(in_features=100, out_features=1, bias=True)
)
Model's state_dict:
input_linear.weight 	 torch.Size([100, 1])
input_linear.bias 	 torch.Size([100])
output_linear.weight 	 torch.Size([1, 100])
output_linear.bias 	 torch.Size([1])
Optimizer's state_dict:
state 	 {}
param_groups 	 [{'lr': 0.0001, 'momentum': 0.9, 'dampening': 0, 'weight_decay': 0, 'nesterov': False, 'params': [140069562969328, 140069562969400, 140069562969472, 140069562969544]}]

• Analysis

(1) 神经网络结构:输入为维度为[100, 1]即100组数据,每组数据中有一个输入,代表输入为单输入变量.
(2) 神经网络内部结构:

参数	描述
in_features	神经网络输入数据数量,本次为1和100,即单输入和100输入(隐藏层)
out_features	神经网络输出数据数量,本次为100和1,即100输出(隐藏层)和单输出
bias	是否有偏置向,True则有,否则为无

(3) 正常神经网络结构

结构描述	维度
输入层	[100, 1],100组数据,每组数据中有一个数据,表示单输入
输入层 $\mapsto$隐藏层	[1, 100],隐藏层有100个节点,即权重100个,偏置100个
隐藏层 $\mapsto$ 输出层	[100, 1],输出数据维度为1,即单输出,偏置1个

(4) PyTorch中神经网络结构:

结构描述	维度
输入层 $\mapsto$隐藏层权重	[100, 1]
输入层 $\mapsto$隐藏层偏置	[100]
隐藏层 $\mapsto$输出层权重	[1, 100]
隐藏层 $\mapsto$输出层偏置	[1]

2 模型数据读取

import torch

N, D_in, H, D_out = 100, 1, 100, 1
x = torch.unsqueeze(torch.linspace(-PI, PI, 100), dim=1)
y = torch.sin(x) + 0.2 * torch.rand(x.size())
class DynamicNet(torch.nn.Module):
    def __init__(self, D_in, H, D_out):
        super(DynamicNet, self).__init__()
        self.input_linear = torch.nn.Linear(D_in, H)
        self.output_linear = torch.nn.Linear(H, D_out)
    def forward(self, x):
        h_relu = self.input_linear(x).clamp(min=0)
        y_pred = self.output_linear(h_relu)
        return y_pred
    
model = DynamicNet(D_in, H, D_out)
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9)
checkpoint = torch.load("./pytorch_models/pre_sin_state_dict.pth")

model.load_state_dict(checkpoint["model_state_dict"])
print("model structure: {}".format(model.modules()))

print("model dict: {}".format(model.state_dict()))
for name in model.state_dict():
    print("model node name: {}".format(name))

• Result

model structure: <generator object Module.modules at 0x7f64ac475408>
model dict: OrderedDict([('input_linear.weight', tensor([[ 0.6214],
        [-0.5627],
        [ 0.0381],
        ...
         [ 0.0678]])), ('input_linear.bias', tensor([-0.3924,  0.7454,  0.8201,  0.2507,  0.6988,  0.8026,  0.1359, -0.0758,
         0.5269,  0.2195,  0.5729, -0.9007,  0.4940, -0.2502,  0.5416, -0.4938,
         ...
          0.2432, -0.1625, -0.8684, -0.0433])), ('output_linear.weight', tensor([[-0.0078, -0.1481, -0.0208, -0.0123, -0.1144,  0.1286, -0.0427, -0.1011,
          0.0800, -0.0469, -0.0094, -0.3129, -0.1281,  0.0780, -0.0076,  0.1603,
          ...
          0.0296,  0.0402, -0.0111,  0.0254]])), ('output_linear.bias', tensor([0.1300]))])
model node name: input_linear.weight
model node name: input_linear.bias
model node name: output_linear.weight
model node name: output_linear.bias

• Analysis
  (1) 模型是generator对象;
  (2) 获取模型结构使用方法:state_dict(),模型参数结构是OrderDict即"字典","key"为网络结构名称,如权重input_linear.weight和偏置input_linear.bias,"value"为PyTorch数据结构Tensor.
  (3) 参数提取:可使用对字典数据提取的方式.

keys = model.state_dict().keys()
values = model.state_dict().values()
for key, value in zip(keys, values):
	print(key, value)

3 总结

(1) PyTorch神经网络结构构建方式与正常方式一致,但是网络内部结构:权重则为正常网络结构的转置.
(2) PyTorch模型结构为字典,可利用对字典的遍历方式获取模型参数.
(3) Tensorflow模型结构为列表.
Tensorflow对比:
Tensorflow基础知识：模型保存与载入深度解析(二)
(一)Tensorflow搭建神经网络

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[参考文献]
[1]https://pytorch.org/tutorials/beginner/saving_loading_models.html
[2]https://pytorch.org/docs/stable/torch.html
[3]https://pytorch.org/docs/stable/tensors.html
[4]https://pytorch.org/docs/stable/nn.html

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