深度学习100例 | 第4例：水果识别 – PyTorch实现

文章目录

*
- 一、导入数据
-
+ 1. 获取类别名字
+ 2. 加载数据文件
+ 3. 划分数据
- 二、自建模型
- 三、模型训练
-
+ 1. 优化器与损失函数
+ 2. 模型的训练
- 四、结果分析

大家好，我是K同学啊，今天讲《深度学习100例》PyTorch版的第4个例子，前面一些例子主要还是以带大家了解PyTorch为主， 建议手动敲一下代码，只有自己动手了，才能真正体会到里面的内容，光看不练是没有用的。今天的重点是在PyTorch调用VGG-16算法模型。先来了解一下PyTorch与TensorFlow的区别

PyTorch VS TensorFlow：

• TensorFlow：简单，模块封装比较好， 容易上手，对新手比较友好。在工业界最重要的是模型落地，目前国内的大部分企业支持TensorFlow模型在线部署，不支持Pytorch。
• PyTorch： 前沿算法多为PyTorch版本，如果是你高校学生or研究人员，建议学这个。相对于TensorFlow，Pytorch在易用性上更有优势，更加方便调试。

当然，如果你有足够的时间，我建议你需要理解这两种模式，这两种模式都很重要。

🍨 本文的重点：将讲解如何使用PyTorch构建神经网络模型（将对这一块展开详细的讲解）

🍖 我的环境：

• 语言环境：Python3.8
• 编译器：Jupyter Lab
• 深度学习环境：
• torch==1.10.0+cu113
• torchvision==0.11.1+cu113

深度学习环境配置教程：小白入门深度学习 | 第四篇：配置PyTorch环境

👉 往期精彩内容

1. 深度学习100例 | 第1例：猫狗识别 - PyTorch实现

2. 深度学习100例 | 第2例：人脸表情识别 - PyTorch实现

3. 🔥 本文选自专栏：《深度学习100例》Pytorch版

4. ✨ 镜像专栏：《深度学习100例》TensorFlow版

一、导入数据

from torchvision.transforms import transforms
from torch.utils.data       import DataLoader
from torchvision            import datasets
import torchvision.models   as models
import torch.nn.functional  as F
import torch.nn             as nn
import torch,torchvision

1. 获取类别名字

import os,PIL,random,pathlib

data_dir = './04-data/'
data_dir = pathlib.Path(data_dir)

data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[1] for path in data_paths]
classeNames

['Apple',
 'Banana',
 'Carambola',
 'Guava',
 'Kiwi',
 'Mango',
 'muskmelon',
 'Orange',
 'Peach',
 'Pear',
 'Persimmon',
 'Pitaya',
 'Plum',
 'Pomegranate',
 'Tomatoes']

2. 加载数据文件

total_datadir = './04-data/'

train_transforms = transforms.Compose([
    transforms.Resize([224, 224]),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225])
])

total_data = datasets.ImageFolder(total_datadir,transform=train_transforms)
total_data

Dataset ImageFolder
    Number of datapoints: 12000
    Root location: ./04-data/
    StandardTransform
Transform: Compose(
               Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=None)
               ToTensor()
               Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
           )

3. 划分数据

train_size = int(0.8 * len(total_data))
test_size  = len(total_data) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])
train_dataset, test_dataset

(<torch.utils.data.dataset.subset at 0x24bbdb84ac0>,
 <torch.utils.data.dataset.subset at 0x24bbdb84610>)
</torch.utils.data.dataset.subset></torch.utils.data.dataset.subset>

train_size,test_size

(9600, 2400)

train_loader = torch.utils.data.DataLoader(train_dataset,
                                           batch_size=16,
                                           shuffle=True,
                                           num_workers=1)
test_loader = torch.utils.data.DataLoader(test_dataset,
                                          batch_size=16,
                                          shuffle=True,
                                          num_workers=1)

print("The number of images in a training set is: ", len(train_loader)*16)
print("The number of images in a test set is: ", len(test_loader)*16)
print("The number of batches per epoch is: ", len(train_loader))

The number of images in a training set is:  9600
The number of images in a test set is:  2400
The number of batches per epoch is:  600

for X, y in test_loader:
    print("Shape of X [N, C, H, W]: ", X.shape)
    print("Shape of y: ", y.shape, y.dtype)
    break

Shape of X [N, C, H, W]:  torch.Size([16, 3, 224, 224])
Shape of y:  torch.Size([16]) torch.int64

二、自建模型

nn.Conv2d()函数：

• 第一个参数（in_channels）是输入的channel数量，彩色图片为3，黑白图片为1。
• 第二个参数（out_channels）是输出的channel数量
• 第三个参数（kernel_size）是卷积核大小
• 第四个参数（stride）是步长，就是卷积操作时每次移动的格子数，默认为1
• 第五个参数（padding）是填充大小，默认为0

这里大家最难理解的可能就是 nn.Linear(24*50*50, len(classeNames))这行代码了,在理解它之前你需要先补习一下👉卷积计算 的相关知识，然后可参照下面的网络结构图来进行理解

class Network_bn(nn.Module):
    def __init__(self):
        super(Network_bn, self).__init__()
"""
        nn.Conv2d()函数：
        第一个参数（in_channels）是输入的channel数量
        第二个参数（out_channels）是输出的channel数量
        第三个参数（kernel_size）是卷积核大小
        第四个参数（stride）是步长，默认为1
        第五个参数（padding）是填充大小，默认为0
"""
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=5, stride=1, padding=0)
        self.bn1 = nn.BatchNorm2d(12)
        self.conv2 = nn.Conv2d(in_channels=12, out_channels=12, kernel_size=5, stride=1, padding=0)
        self.bn2 = nn.BatchNorm2d(12)
        self.pool = nn.MaxPool2d(2,2)
        self.conv4 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=5, stride=1, padding=0)
        self.bn4 = nn.BatchNorm2d(24)
        self.conv5 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=5, stride=1, padding=0)
        self.bn5 = nn.BatchNorm2d(24)
        self.fc1 = nn.Linear(24*50*50, len(classeNames))

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn2(self.conv2(x)))
        x = self.pool(x)
        x = F.relu(self.bn4(self.conv4(x)))
        x = F.relu(self.bn5(self.conv5(x)))
        x = self.pool(x)
        x = x.view(-1, 24*50*50)
        x = self.fc1(x)

        return x

device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))

model = Network_bn().to(device)
model

Using cuda device

Network_bn(
  (conv1): Conv2d(3, 12, kernel_size=(5, 5), stride=(1, 1))
  (bn1): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (conv2): Conv2d(12, 12, kernel_size=(5, 5), stride=(1, 1))
  (bn2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv4): Conv2d(12, 24, kernel_size=(5, 5), stride=(1, 1))
  (bn4): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (conv5): Conv2d(24, 24, kernel_size=(5, 5), stride=(1, 1))
  (bn5): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (fc1): Linear(in_features=60000, out_features=15, bias=True)
)

三、模型训练

1. 优化器与损失函数

optimizer  = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=0.0001)
loss_model = nn.CrossEntropyLoss()

from torch.autograd import Variable

def test(model, test_loader, loss_model):
    size = len(test_loader.dataset)
    num_batches = len(test_loader)
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in test_loader:
            X, y = X.to(device), y.to(device)
            pred = model(X)
            test_loss += loss_model(pred, y).item()
            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
    test_loss /= num_batches
    correct /= size
    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
    return correct,test_loss

def train(model,train_loader,loss_model,optimizer):
    model=model.to(device)
    model.train()

    for i, (images, labels) in enumerate(train_loader, 0):

        images = Variable(images.to(device))
        labels = Variable(labels.to(device))

        optimizer.zero_grad()
        outputs = model(images)
        loss = loss_model(outputs, labels)
        loss.backward()
        optimizer.step()

        if i % 1000 == 0:
            print('[%5d] loss: %.3f' % (i, loss))

2. 模型的训练

test_acc_list  = []
epochs = 30

for t in range(epochs):
    print(f"Epoch {t+1}\n-------------------------------")
    train(model,train_loader,loss_model,optimizer)
    test_acc,test_loss = test(model, test_loader, loss_model)
    test_acc_list.append(test_acc)
print("Done!")

Epoch 1
[    0] loss: 0.468
Test Error:
 Accuracy: 89.2%, Avg loss: 0.377265

......

Epoch 29
[    0] loss: 0.000
Test Error:
 Accuracy: 91.8%, Avg loss: 0.660563

Done!

四、结果分析

import numpy as np
import matplotlib.pyplot as plt

x = [i for i in range(1,31)]

plt.plot(x, test_acc_list, label="Accuracy", alpha=0.8)

plt.xlabel("Epoch")
plt.ylabel("Accuracy")

plt.legend()
plt.show()

Original: https://blog.csdn.net/qq_38251616/article/details/125132612
Author: K同学啊
Title: 深度学习100例 | 第4例：水果识别 - PyTorch实现