p22神经网络搭建-小实战和Sequential的使用

由官网公式，计算出padding可取2，stride可取1https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html#torch.nn.Conv2d

from torch import nn
from torch.nn import Conv2d, MaxPool2d, Linear, Flatten


class Lixinyu(nn.Module):
    def __init__(self):
        super(Lixinyu, self).__init__()
        self.conv1 = Conv2d(3, 32, 5, stride=1, padding=2)
        self.maxpool1 = MaxPool2d(2)
        self.conv2 = Conv2d(32, 32, 5, padding=2)
        self.maxpool2 = MaxPool2d(2)
        self.conv3 = Conv2d(32, 64, 5, padding=2)
        self.maxpool3 = MaxPool2d(2)
        self.flatten = Flatten()
        self.linear1 = Linear(1024, 64)
        self.linear2 = Linear(64, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.maxpool2(x)
        x = self.conv3(x)
        x = self.maxpool3(x)
        x = self.flatten(x)
        x = self.linear1(x)
        x = self.linear2(x)
        return x

lixinyu = Lixinyu()
print(lixinyu)

D:\anaconda\python.exe C:/Users/ASUS/Desktop/tudui/nn_sequential.py
Lixinyu(
  (conv1): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
  (maxpool1): 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))
  (maxpool2): 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))
  (maxpool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (flatten): 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)
)

Process finished with exit code 0

检查所搭建的网络是否为所想要的

正常无实例化，更改linear中的 self.linear1 = Linear(1024, 64)为 self.linear1 = Linear(102400, 64)，并不会报错！但当运用以下后，出错会报错

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Linear, Flatten


class Lixinyu(nn.Module):
    def __init__(self):
        super(Lixinyu, self).__init__()
        self.conv1 = Conv2d(3, 32, 5, stride=1, padding=2)
        self.maxpool1 = MaxPool2d(2)
        self.conv2 = Conv2d(32, 32, 5, padding=2)
        self.maxpool2 = MaxPool2d(2)
        self.conv3 = Conv2d(32, 64, 5, padding=2)
        self.maxpool3 = MaxPool2d(2)
        self.flatten = Flatten()
        self.linear1 = Linear(1024, 64)
        self.linear2 = Linear(64, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.maxpool2(x)
        x = self.conv3(x)
        x = self.maxpool3(x)
        x = self.flatten(x)
        x = self.linear1(x)
        x = self.linear2(x)
        return x

lixinyu = Lixinyu()
print(lixinyu)

input = torch.ones(64, 3, 32, 32) #######
output = lixinyu(input)
print(output.shape)

使用Sequential定义

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Linear, Flatten, Sequential


class Lixinyu(nn.Module):
    def __init__(self):
        super(Lixinyu, self).__init__()
        self.model1 = Sequential(Conv2d(3, 32, 5, stride=1, padding=2),
                                 MaxPool2d(2),
                                 Conv2d(32, 32, 5, padding=2),
                                 MaxPool2d(2),
                                 Conv2d(32, 64, 5, padding=2),
                                 MaxPool2d(2),
                                 Flatten(),
                                 Linear(1024, 64),
                                 Linear(64, 10))

    def forward(self, x):
        x = self.model1(x)
        return x

lixinyu = Lixinyu()
print(lixinyu)

input = torch.ones(64, 3, 32, 32)
output = lixinyu(input)
print(output.shape)

D:\anaconda\python.exe C:/Users/ASUS/Desktop/tudui/nn_sequential.py
Lixinyu(
  (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])

Process finished with exit code 0

tensorboard可显示网络层

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Linear, Flatten, Sequential
from torch.utils.tensorboard import SummaryWriter


class Lixinyu(nn.Module):
    def __init__(self):
        super(Lixinyu, self).__init__()
        self.model1 = Sequential(Conv2d(3, 32, 5, stride=1, padding=2),
                                 MaxPool2d(2),
                                 Conv2d(32, 32, 5, padding=2),
                                 MaxPool2d(2),
                                 Conv2d(32, 64, 5, padding=2),
                                 MaxPool2d(2),
                                 Flatten(),
                                 Linear(1024, 64),
                                 Linear(64, 10))

    def forward(self, x):
        x = self.model1(x)
        return x

lixinyu = Lixinyu()
print(lixinyu)

input = torch.ones(64, 3, 32, 32)
output = lixinyu(input)
print(output.shape)

writer = SummaryWriter("p22")

writer.add_graph(lixinyu, input) #################
writer.close()

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