学习pytorch13 神经网络-搭建小实战&Sequential的使用
神经网络-搭建小实战&Sequential的使用
-
- 官网
- 模型结构
- 根据模型结构和数据的输入shape，计算用在模型中的超参数
- code
- running log
- 网络结构可视化
B站小土堆pytorch视频学习
官网
https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential
sequential 将模型结构组合起来 以逗号分割，按顺序执行，和compose使用方式类似。
模型结构
根据模型结构和数据的输入shape，计算用在模型中的超参数
箭头指向部分还需要一层flatten层，展开输入shape为一维
code
import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.tensorboard import SummaryWriter
class MySeq(nn.Module):
def __init__(self):
super(MySeq, self).__init__()
self.conv1 = Conv2d(3, 32, kernel_size=5, stride=1, padding=2)
self.maxp1 = MaxPool2d(2)
self.conv2 = Conv2d(32, 32, kernel_size=5, stride=1, padding=2)
self.maxp2 = MaxPool2d(2)
self.conv3 = Conv2d(32, 64, kernel_size=5, stride=1, padding=2)
self.maxp3 = MaxPool2d(2)
self.flatten1 = Flatten()
self.linear1 = Linear(1024, 64)
self.linear2 = Linear(64, 10)
def forward(self, x):
x = self.conv1(x)
x = self.maxp1(x)
x = self.conv2(x)
x = self.maxp2(x)
x = self.conv3(x)
x = self.maxp3(x)
x = self.flatten1(x)
x = self.linear1(x)
x = self.linear2(x)
return x
class MySeq2(nn.Module):
def __init__(self):
super(MySeq2, self).__init__()
self.model1 = Sequential(Conv2d(3, 32, kernel_size=5, stride=1, padding=2),
MaxPool2d(2),
Conv2d(32, 32, kernel_size=5, stride=1, padding=2),
MaxPool2d(2),
Conv2d(32, 64, kernel_size=5, stride=1, padding=2),
MaxPool2d(2),
Flatten(),
Linear(1024, 64),
Linear(64, 10)
)
def forward(self, x):
x = self.model1(x)
return x
myseq = MySeq()
input = torch.ones(64, 3, 32, 32)
print(myseq)
print(input.shape)
output = myseq(input)
print(output.shape)
myseq2 = MySeq2()
print(myseq2)
output2 = myseq2(input)
print(output2.shape)
wirter = SummaryWriter('logs')
wirter.add_graph(myseq, input)
wirter.add_graph(myseq2, input)
running log
MySeq(
(conv1): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(maxp1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(maxp2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(maxp3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(flatten1): Flatten(start_dim=1, end_dim=-1)
(linear1): Linear(in_features=1024, out_features=64, bias=True)
(linear2): Linear(in_features=64, out_features=10, bias=True)
)
torch.Size([64, 3, 32, 32])
torch.Size([64, 10])
MySeq2(
(model1): Sequential(
(0): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(4): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Flatten(start_dim=1, end_dim=-1)
(7): Linear(in_features=1024, out_features=64, bias=True)
(8): Linear(in_features=64, out_features=10, bias=True)
)
)
torch.Size([64, 10])
网络结构可视化
from torch.utils.tensorboard import SummaryWriter
wirter = SummaryWriter('logs')
wirter.add_graph(myseq, input)
tensorboard --logdir=logs
tensorboard 展示图文件, 双击每层网络，可查看层定义细节
