基于卷积猫狗识别

一、学习任务

1.按照 https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/5.2-using-convnets-with-small-datasets.ipynb,
利用TensorFlow和Keras，自己搭建卷积神经网络完成狗猫数据集的分类实验；将关键步骤用汉语注释出来。解释什么是overfit（过拟合）?什么是数据增强？如果单独只做数据增强，精确率提高了多少？然后再添加的dropout层，是什么实际效果？
2. 用Vgg19网络模型完成狗猫分类，写出实验结果；

二、学习内容

1.环境配置

1.安装Anaconda
2.配置Pytorch

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple torch
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple torchvision

2.构建数据集

数据集下载地址：https://www.kaggle.com/lizhensheng/-2000

3.猫狗实例

1.导入库

# 导入库
import torch.nn.functional as F
import torch.optim as optim
import torch
import torch.nn as nn
import torch.nn.parallel
 
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets

2.设置超参数

设置超参数
 
BATCH_SIZE = 20
EPOCHS = 10
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

3.图像处理与图像增强

# 数据预处理
transform = transforms.Compose([
 
    transforms.Resize(100),
 
    transforms.RandomVerticalFlip(),
 
    transforms.RandomCrop(50),
 
    transforms.RandomResizedCrop(150),
 
    transforms.ColorJitter(brightness=0.5, contrast=0.5, hue=0.5),
 
    transforms.ToTensor(),
 
    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
 
])

4.读取数据和导入数据

# 读取数据
 
dataset_train = datasets.ImageFolder('data/train', transform)
 
print(dataset_train.imgs)
 
# 对应文件夹的label
 
print(dataset_train.class_to_idx)
 
dataset_test = datasets.ImageFolder('data/val', transform)
 
# 对应文件夹的label
 
print(dataset_test.class_to_idx)
 
# 导入数据
 
train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True)
 
test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=True)

5.定义网络模型

# 定义网络
 
class ConvNet(nn.Module):
 
    def __init__(self):
 
        super(ConvNet, self).__init__()
 
        self.conv1 = nn.Conv2d(3, 32, 3)
 
        self.max_pool1 = nn.MaxPool2d(2)
 
        self.conv2 = nn.Conv2d(32, 64, 3)
 
        self.max_pool2 = nn.MaxPool2d(2)
 
        self.conv3 = nn.Conv2d(