6.5 使用 VGG 预训练网络实现猫狗分类_vgg 猫狗图像分类迁移学习代码-优快云博客

本文链接：https://blog.youkuaiyun.com/weixin_46994240/article/details/123943819

本文详细介绍了如何运用深度学习中的VGG预训练网络进行猫狗分类任务。通过在预训练的卷积基上添加全连接层和输出层，然后只训练新增加层的权重，经过模型编译和训练，实现了迁移学习的目标。

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一）代码实现迁移学习

import tensorflow as tf
import keras
from keras import layers
import numpy as np
import os
import shutil


base_dir = './dataset/cat_dog'
train_dir = base_dir + '/train'
train_dog_dir = train_dir + '/dog'
train_cat_dir = train_dir + '/cat'
test_dir = base_dir + '/test'
test_dog_dir = test_dir + '/dog'
test_cat_dir = test_dir + '/cat'
dc_dir = './dataset/dc/train' 



if not os.path.exists(base_dir):

    os.mkdir(base_dir)

    os.mkdir(train_dir)
    os.mkdir(train_dog_dir)
    os.mkdir(train_cat_dir)
    os.mkdir(test_dir)
    os.mkdir(test_dog_dir)
    os.mkdir(test_cat_dir)

    fnames = ['cat.{}.jpg'.format(i) for i in range(1000)] 
    for fname in fnames:
        src = os.path.join(dc_dir, fname)
        dst = os.path.join(train_cat_dir, fname)
        shutil.copyfile(src, dst)

    fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)] 
    for fname in fnames:
        src = os.path.join(dc_dir, fname)
        dst = os.path.join(test_cat_dir, fname)
        shutil.copyfile(src, dst)

    fnames = ['dog.{}.jpg'.format(i) for i in range(1000)] 
    for fname in fnames:
        src = os.path.join(dc_dir, fname)
        dst = os.path.join(train_dog_dir, fname)
        shutil.copyfile(src, dst)

    fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)] 
    for fname in fnames