超高准确率!TensorFlow猫狗识别案例

·  阅读 179
超高准确率!TensorFlow猫狗识别案例

最近更新有点慢,后台收到不少小伙伴的催更,先说声抱歉哈。最近在参加一个目标检测的比赛,时间比较紧张。这段时间我也打算调整一下思路,试着将目标检测中涉及的内容拆开来,将这些拆分的内容一点点融入到在后续的博客中。一方面兼具我现阶段的工作,一方面也不耽搁文章的更新进度。水到渠成后,就开始写目标检测方面的内容。

这篇文章中我放弃了以往的model.fit()训练方法,改用model.train_on_batch方法。两种方法的比较:

  • model.fit():用起来十分简单,对新手非常友好
  • model.train_on_batch():封装程度更低,可以玩更多花样。

此外我也引入了进度条的显示方式,更加方便我们及时查看模型训练过程中的情况,可以及时打印各项指标。

🚀 我的环境:

  • 语言环境:Python3.6.5
  • 编译器:jupyter notebook
  • 深度学习环境:TensorFlow2.4.1
  • 显卡(GPU):NVIDIA GeForce RTX 3080
  • 数据:pan.baidu.com/s/1lIY2zF_0… 提取码:71q2

🚀 来自专栏:《深度学习100例》

文章目录

  • 一、前期工作
      1. 设置GPU
      1. 导入数据
      1. 查看数据
  • 二、数据预处理
      1. 加载数据
      1. 再次检查数据
      1. 配置数据集
      1. 可视化数据
  • 三、构建VG-16网络
  • 四、编译
  • 五、训练模型
  • 六、模型评估
  • 七、保存and加载模型
  • 八、预测
  • 九、同系列作品

一、前期工作

1. 设置GPU

如果使用的是CPU可以注释掉这部分的代码。

import tensorflow as tf

gpus = tf.config.list_physical_devices("GPU")

if gpus:
    tf.config.experimental.set_memory_growth(gpus[0], True)  #设置GPU显存用量按需使用
    tf.config.set_visible_devices([gpus[0]],"GPU")

# 打印显卡信息,确认GPU可用
print(gpus)
复制代码
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
复制代码

2. 导入数据

import matplotlib.pyplot as plt
# 支持中文
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号

import os,PIL

# 设置随机种子尽可能使结果可以重现
import numpy as np
np.random.seed(1)

# 设置随机种子尽可能使结果可以重现
import tensorflow as tf
tf.random.set_seed(1)

#隐藏警告
import warnings
warnings.filterwarnings('ignore')

import pathlib
复制代码
data_dir = "./data/train"
# data_dir = "D:/jupyter notebook/DL-100-days/datasets/017_Eye_dataset"

data_dir = pathlib.Path(data_dir)
复制代码

3. 查看数据

image_count = len(list(data_dir.glob('*/*')))

print("图片总数为:",image_count)
复制代码
图片总数为: 3400
复制代码

二、数据预处理

1. 加载数据

使用image_dataset_from_directory方法将磁盘中的数据加载到tf.data.Dataset

batch_size = 8
img_height = 224
img_width = 224
复制代码

TensorFlow版本是2.2.0的同学可能会遇到module 'tensorflow.keras.preprocessing' has no attribute 'image_dataset_from_directory'的报错,升级一下TensorFlow就OK了。

"""
关于image_dataset_from_directory()的详细介绍可以参考文章:https://mtyjkh.blog.csdn.net/article/details/117018789
"""
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="training",
    seed=12,
    image_size=(img_height, img_width),
    batch_size=batch_size)
复制代码
Found 3400 files belonging to 2 classes.
Using 2720 files for training.
复制代码
"""
关于image_dataset_from_directory()的详细介绍可以参考文章:https://mtyjkh.blog.csdn.net/article/details/117018789
"""
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="validation",
    seed=12,
    image_size=(img_height, img_width),
    batch_size=batch_size)
复制代码
Found 3400 files belonging to 2 classes.
Using 680 files for validation.
复制代码

我们可以通过class_names输出数据集的标签。标签将按字母顺序对应于目录名称。

class_names = train_ds.class_names
print(class_names)
复制代码
['cat', 'dog']
复制代码

2. 再次检查数据

for image_batch, labels_batch in train_ds:
    print(image_batch.shape)
    print(labels_batch.shape)
    break
复制代码
(8, 224, 224, 3)
(8,)
复制代码
  • Image_batch是形状的张量(8, 224, 224, 3)。这是一批形状224x224x3的8张图片(最后一维指的是彩色通道RGB)。
  • Label_batch是形状(8,)的张量,这些标签对应8张图片

3. 配置数据集

  • shuffle() : 打乱数据,关于此函数的详细介绍可以参考:zhuanlan.zhihu.com/p/42417456
  • prefetch() :预取数据,加速运行,其详细介绍可以参考我前两篇文章,里面都有讲解。
  • cache() :将数据集缓存到内存当中,加速运行
AUTOTUNE = tf.data.AUTOTUNE

def preprocess_image(image,label):
    return (image/255.0,label)

# 归一化处理
train_ds = train_ds.map(preprocess_image, num_parallel_calls=AUTOTUNE)
val_ds   = val_ds.map(preprocess_image, num_parallel_calls=AUTOTUNE)

train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds   = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
复制代码

如果报 AttributeError: module 'tensorflow._api.v2.data' has no attribute 'AUTOTUNE' 错误,就将 AUTOTUNE = tf.data.AUTOTUNE 更换为 AUTOTUNE = tf.data.experimental.AUTOTUNE,这个错误是由于版本问题引起的。

4. 可视化数据

plt.figure(figsize=(15, 10))  # 图形的宽为15高为10

for images, labels in train_ds.take(1):
    for i in range(8):
        
        ax = plt.subplot(5, 8, i + 1) 
        plt.imshow(images[i])
        plt.title(class_names[labels[i]])
        
        plt.axis("off")
复制代码

三、构建VG-16网络

VGG优缺点分析:

  • VGG优点

VGG的结构非常简洁,整个网络都使用了同样大小的卷积核尺寸(3x3)和最大池化尺寸(2x2)。

  • VGG缺点

1)训练时间过长,调参难度大。2)需要的存储容量大,不利于部署。例如存储VGG-16权重值文件的大小为500多MB,不利于安装到嵌入式系统中。

结构说明:

  • 13个卷积层(Convolutional Layer),分别用blockX_convX表示
  • 3个全连接层(Fully connected Layer),分别用fcXpredictions表示
  • 5个池化层(Pool layer),分别用blockX_pool表示

VGG-16包含了16个隐藏层(13个卷积层和3个全连接层),故称为VGG-16

from tensorflow.keras import layers, models, Input
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout

def VGG16(nb_classes, input_shape):
    input_tensor = Input(shape=input_shape)
    # 1st block
    x = Conv2D(64, (3,3), activation='relu', padding='same',name='block1_conv1')(input_tensor)
    x = Conv2D(64, (3,3), activation='relu', padding='same',name='block1_conv2')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block1_pool')(x)
    # 2nd block
    x = Conv2D(128, (3,3), activation='relu', padding='same',name='block2_conv1')(x)
    x = Conv2D(128, (3,3), activation='relu', padding='same',name='block2_conv2')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block2_pool')(x)
    # 3rd block
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv1')(x)
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv2')(x)
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv3')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block3_pool')(x)
    # 4th block
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv1')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv2')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv3')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block4_pool')(x)
    # 5th block
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv1')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv2')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv3')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block5_pool')(x)
    # full connection
    x = Flatten()(x)
    x = Dense(4096, activation='relu',  name='fc1')(x)
    x = Dense(4096, activation='relu', name='fc2')(x)
    output_tensor = Dense(nb_classes, activation='softmax', name='predictions')(x)

    model = Model(input_tensor, output_tensor)
    return model

model=VGG16(1000, (img_width, img_height, 3))
model.summary()
复制代码
Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 25088)             0         
_________________________________________________________________
fc1 (Dense)                  (None, 4096)              102764544 
_________________________________________________________________
fc2 (Dense)                  (None, 4096)              16781312  
_________________________________________________________________
predictions (Dense)          (None, 1000)              4097000   
=================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_________________________________________________________________
复制代码

四、编译

在准备对模型进行训练之前,还需要再对其进行一些设置。以下内容是在模型的编译步骤中添加的:

  • 损失函数(loss):用于衡量模型在训练期间的准确率。
  • 优化器(optimizer):决定模型如何根据其看到的数据和自身的损失函数进行更新。
  • 评价函数(metrics):用于监控训练和测试步骤。以下示例使用了准确率,即被正确分类的图像的比率。
model.compile(optimizer="adam",
              loss     ='sparse_categorical_crossentropy',
              metrics  =['accuracy'])
复制代码

五、训练模型

from tqdm import tqdm
import tensorflow.keras.backend as K

epochs = 10
lr     = 1e-4

# 记录训练数据,方便后面的分析
history_train_loss     = []
history_train_accuracy = []
history_val_loss       = []
history_val_accuracy   = []

for epoch in range(epochs):
    train_total = len(train_ds)
    val_total   = len(val_ds)
    
    """
    total:预期的迭代数目
    ncols:控制进度条宽度
    mininterval:进度更新最小间隔,以秒为单位(默认值:0.1)
    """
    with tqdm(total=train_total, desc=f'Epoch {epoch + 1}/{epochs}',mininterval=1,ncols=100) as pbar:
        
        lr = lr*0.92
        K.set_value(model.optimizer.lr, lr)
        
        for image,label in train_ds:      
            """
            训练模型,简单理解train_on_batch就是:它是比model.fit()更高级的一个用法
            
            想详细了解 train_on_batch 的同学,
            可以看看我的这篇文章:https://mtyjkh.blog.csdn.net/article/details/119506151
            """
            history = model.train_on_batch(image,label)
            
            train_loss     = history[0]
            train_accuracy = history[1]
            
            pbar.set_postfix({"loss": "%.4f"%train_loss,
                              "accuracy":"%.4f"%train_accuracy,
                              "lr": K.get_value(model.optimizer.lr)})
            pbar.update(1)
        history_train_loss.append(train_loss)
        history_train_accuracy.append(train_accuracy)
            
    print('开始验证!')
    
    with tqdm(total=val_total, desc=f'Epoch {epoch + 1}/{epochs}',mininterval=0.3,ncols=100) as pbar:

        for image,label in val_ds:      
            
            history = model.test_on_batch(image,label)
            
            val_loss     = history[0]
            val_accuracy = history[1]
            
            pbar.set_postfix({"loss": "%.4f"%val_loss,
                              "accuracy":"%.4f"%val_accuracy})
            pbar.update(1)
        history_val_loss.append(val_loss)
        history_val_accuracy.append(val_accuracy)
            
    print('结束验证!')
    print("验证loss为:%.4f"%val_loss)
    print("验证准确率为:%.4f"%val_accuracy)
复制代码
Epoch 1/10: 100%|████████| 340/340 [00:23<00:00, 14.36it/s, loss=1.1077, accuracy=0.6250, lr=9.2e-5]
开始验证!
Epoch 1/10: 100%|█████████████████████| 85/85 [00:02<00:00, 36.55it/s, loss=0.9331, accuracy=0.6250]
结束验证!
验证loss为:0.9331
验证准确率为:0.6250
Epoch 2/10: 100%|███████| 340/340 [00:19<00:00, 17.49it/s, loss=0.4633, accuracy=0.6250, lr=8.46e-5]

......

Epoch 9/10: 100%|███████| 340/340 [00:19<00:00, 17.36it/s, loss=0.0112, accuracy=1.0000, lr=4.72e-5]
开始验证!
Epoch 9/10: 100%|█████████████████████| 85/85 [00:01<00:00, 43.46it/s, loss=0.0302, accuracy=1.0000]
结束验证!
验证loss为:0.0302
验证准确率为:1.0000
Epoch 10/10: 100%|██████| 340/340 [00:19<00:00, 17.22it/s, loss=0.0000, accuracy=1.0000, lr=4.34e-5]
开始验证!
Epoch 10/10: 100%|████████████████████| 85/85 [00:02<00:00, 42.15it/s, loss=0.0231, accuracy=1.0000]
结束验证!
验证loss为:0.0231
验证准确率为:1.0000
复制代码
# 这是我们之前的训练方法。
# history = model.fit(
#     train_ds,
#     validation_data=val_ds,
#     epochs=epochs
# )
复制代码

六、模型评估

epochs_range = range(epochs)

plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)

plt.plot(epochs_range, history_train_accuracy, label='Training Accuracy')
plt.plot(epochs_range, history_val_accuracy, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, history_train_loss, label='Training Loss')
plt.plot(epochs_range, history_val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
复制代码

七、保存and加载模型

这是最简单的模型保存与加载方法哈

# 保存模型
model.save('model/21_model.h5')
复制代码
# 加载模型
new_model = tf.keras.models.load_model('model/21_model.h5')
复制代码

八、预测

# 采用加载的模型(new_model)来看预测结果

plt.figure(figsize=(18, 3))  # 图形的宽为18高为5
plt.suptitle("预测结果展示")

for images, labels in val_ds.take(1):
    for i in range(8):
        ax = plt.subplot(1,8, i + 1)  
        
        # 显示图片
        plt.imshow(images[i].numpy())
        
        # 需要给图片增加一个维度
        img_array = tf.expand_dims(images[i], 0) 
        
        # 使用模型预测图片中的人物
        predictions = new_model.predict(img_array)
        plt.title(class_names[np.argmax(predictions)])

        plt.axis("off")
复制代码

🚀 本文选自专栏:《深度学习100例》