深度学习网络篇之一：LeNet5, VGG, DarkNet19 网络代码（供实验拷贝）

最新推荐文章于 2025-04-18 10:43:18 发布

wuyouyin123

最新推荐文章于 2025-04-18 10:43:18 发布

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分类专栏：深度学习基础知识文章标签：神经网络深度学习 tensorflow

本文链接：https://blog.youkuaiyun.com/wuyouyin123/article/details/105362180

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本文深入探讨了深度学习领域的三个经典模型：LeNet5、VGG系列和DarkNet19，详细介绍了它们的架构和构建过程，为读者提供了对这些模型工作原理的全面理解。

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第一: LeNet5
在这里插入图片描述

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics

model = Sequential()
model.add( layers.Input( shape = (28, 28, 1) ) )
model.add( layers.Conv2D( filters = 6, kernel_size = [5, 5], strides = [1, 1], padding = "same", activation = "relu") )
model.add( layers.MaxPool2D( pool_size=[2, 2], strides = [2, 2], padding = "same") )
model.add( layers.Conv2D( filters = 16, kernel_size = [5, 5], strides = [1, 1], padding = "valid", activation = "relu") )
model.add( layers.MaxPool2D( pool_size=[2, 2], strides = [2, 2], padding = "same") )
model.add( layers.Flatten() )
model.add( layers.Dense( units = 120, activation = "relu" ) )
model.add( layers.Dense( units = 84, activation = "relu" ) )
model.add( layers.Dense( units = 10, activation = "softmax" ) )
model.summary()

第二：VGG
在这里插入图片描述
VGG11-A

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics

model = Sequential()
model.add( layers.Input( shape = (224, 224, 3) ) )
model.add( layers.Conv2D( filters = 64, kernel_size = [3, 3], strides = [1, 1], padding = "same", activation = "relu") )
model.add( layers.MaxPool2D( pool_size=[2, 2], strides = [2, 2], padding = "same") )

model.add( layers.Conv2D( filters = 128, kernel_size = [3, 3], strides = [1, 1], padding = "same", activation = "relu") )
model.add( layers.MaxPool2D( pool_size=[2, 2], strides = [2, 2], padding = "same") )

model.add( layers.Conv2D( filters = 256, kernel_size = [3, 3], strides = [1, 1], padding = "same", activation = "relu") )
model.add( layers.Conv2D( filters = 256, kernel_size = [3, 3], strides = [1, 1], padding = "same", activation = "relu") )
model.add( layers.MaxPool2D( pool_size=[2, 2], strides = [2, 2], padding = "same") )

model.add( layers.Conv2D( filters = 512, kernel_size = [3, 3], strides = [1, 1], padding = "same", activation = "relu") )
model.add( layers.Conv2D( filters = 512, kernel_size = [3, 3], strides = [1, 1], padding = "same", activation = "relu") )
model.add( layers.MaxPool2D( pool_size=[2, 2], strides = [2, 2], padding = "same") )

model.add( layers.Conv2D( filters = 512, kernel_size = [3, 3], strides = [1, 1], padding = "same", activation = "relu") )
model.add( layers.Conv2D( filters = 512, kernel_size = [3, 3], strides = [1, 1], padding = "same", activation = "relu") )
model.add( layers.MaxPool2D( pool_size=[2, 2], strides = [2, 2], padding = "same") )

model.add( layers.Flatten() )
model.add( layers.Dense( units = 4096, activation = "relu" ) )
model.add( layers.Dense( units = 4096, activation = "relu" ) )
model.add( layers.Dense( units = 1000, activation = "softmax" ) )
model.summary()

VGG13-B

model = Sequential()
model.add( layers.Input( shape = (224, 224, 3) ) )
model.add( layers.Conv2D( filters = 64, kernel_size = [3, 3], strides = [1, 1], padding = "same", activation = "relu") )
model.add( layers.Conv2D( filters = 64, kernel_size = [3, 3], strides = [1, 1], padding = "same", activation = "relu") )
model.add( layers.MaxPool2D( pool_size=[2, 2], strides = [2, 2], padding = "same")

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