TensorFlow学习：使用官方模型进行图像分类、使用自己的数据对模型进行微调

前言

上一篇文章 TensorFlow案例学习：对服装图像进行分类 中我们跟随官方文档学习了如何进行预处理数据、构建模型、训练模型等。但是对于像我这样的业余玩家来说训练一个模型是非常困难的。所以为什么我们不站在巨人的肩膀上，使用已经训练好了的成熟模型呢 ？这篇文章简单介绍如何使用成熟的模型

建议按顺序观看，这是一个小系列，适合像我这样的初学者入门

配置环境：windows环境下tensorflow安装

图片分类案例学习：TensorFlow案例学习：对服装图像进行分类

使用官方模型，并进行微调：TensorFlow学习：使用官方模型进行图像分类、使用自己的数据对模型进行微调

将模型转换，在前端使用：TensorFlow学习：在web前端如何使用Keras 模型

使用成熟模型

使用

下载模型
官方为我们提供了适合 TensorFlow 的模型下载网站：TensorFlow Hub （需要科学上网）

这里我选择的模型是：mobilenet_v2，模型下载地址是 https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/5

模型简介

MobileNetV2是由Google开发的一种轻量级的卷积神经网络架构，专门设计用于在移动设备和嵌入式设备上进行图像分类和目标检测任务。MobileNetV2是MobileNet系列的第二个版本，相比于前一个版本，它在准确性和性能之间取得了更好的平衡。

MobileNetV2在ImageNet图像分类任务上取得了与更大更复杂的模型相当的准确性，但模型的大小和计算量却大大减少。这使得MobileNetV2成为在资源受限的设备上进行实时图像分类和目标检测的理想选择。

在TensorFlow中，你可以使用预训练的MobileNetV2模型，也可以根据自己的需求进行微调和训练。TensorFlow提供了相应的API和工具，使得使用和训练MobileNetV2变得更加方便。

使用
将下载后的模型解压到项目里引入就好，完整代码

# 导入tensorflow 和科学计算库
import tensorflow as tf
import numpy as np
# tensorflow-hub是一个TensorFlow库的扩展，它提供了一个简单的接口，用于重用已经训练好的机器学习模型的部分
import tensorflow_hub as hub
# 字体属性
from matplotlib.font_manager import FontProperties
# matplotlib是用于绘制图表和可视化数据的库
import matplotlib.pylab as plt
# 用于加载json文件
import json# 导入模型
# 不能直接加载模型文件，需要加载器目录
# 加载mobilenet_v2模型，这里要加载文件夹不要直接加载pb文件
# 模型如何加载要看文档，原来使用tf.keras.models.load_model加载一直失败
model = tf.keras.Sequential([hub.KerasLayer('imagenet_mobilenet_v2_140_224_classification_5')
])
print("模型信息:",model)# 预处理输入数据
# 1、mobilenet需要的图片尺寸是 224 * 224
image = tf.keras.preprocessing.image.load_img('pics/dog.png',target_size=(224,224))
# 2、将图片转为数组，既是只有一张图片
image = tf.keras.preprocessing.image.img_to_array(image)
# 3、扩展数组维度，使其符合模型的输入
image = np.expand_dims(image, axis=0)
# 4、使用mobilenet_v2提供的预处理函数对图像处理，包括图像归一化、颜色通道顺序调整、像素值标准化等操作
image = tf.keras.applications.mobilenet_v2.preprocess_input(image)# 预测
predictions = model.predict(image)
# 获取最高概率对应的类别索引
predicted_index = np.argmax(predictions)
# 概率值
confidence = np.max(predictions)
print("索引和概率值是：",predicted_index,confidence)# 加载映射文件
with open('imagenet-classes.json','r') as f:labels_dict  = json.load(f)# 类别的索引是字符串，这里要简单处理一下，这里-1是因为官方提供的多了一个0（背景），我找到的标签没有这个，因此要-1
class_name = labels_dict[str(predicted_index-1)]# 可视化显示
font = FontProperties()
font.set_family('Microsoft YaHei')
plt.figure() # 创建图像窗口
plt.xticks([])
plt.yticks([])
plt.grid(False) # 取消网格线
plt.imshow(image[0]) # 显示图片
plt.xlabel(class_name,fontproperties=font)
plt.show() # 显示图形窗口

注意点

去除掉一些不必要的代码，其实代码真的很少。但是你却可以利用这些代码实现图片分类。虽然代码不多，但是却遇到了很多问题。

加载模型
加载模型时就遇到了问题，这是当头一棒。

首先加载模型时，你需要加载整个文件夹，不要加载文件里的某个文件

model = tf.keras.Sequential([hub.KerasLayer('imagenet_mobilenet_v2_140_224_classification_5')
])

文件夹如下：

加载模型使用的函数，一开始百度查到的加载模型的函数是：tf.keras.models.load_model 。但是在这里不适用，导致模型一直加载不成功，后来看了一下官方文档，要这样加载

这里我们要下载tensorflow_hub，加载命令中的hub是其简写。tensorflow-hub是一个TensorFlow库的扩展，它提供了一个简单的接口，用于重用已经训练好的机器学习模型的部分。不同的模型有不同的加载方式，使用 TensorFlow Hub 网站提供的模型时，要注意其使用说明。

pip install tensorflow_hub

预处理输入数据
模型加载好了还不够，你还需要处理好数据。使你的图片符合模型的输入格式，也就是下面这段代码

# 预处理输入数据
# 1、mobilenet需要的图片尺寸是 224 * 224
image = tf.keras.preprocessing.image.load_img('pics/dog.png',target_size=(224,224))
# 2、将图片转为数组，既是只有一张图片
image = tf.keras.preprocessing.image.img_to_array(image)
# 3、扩展数组维度，使其符合模型的输入
image = np.expand_dims(image, axis=0)
# 4、使用mobilenet_v2提供的预处理函数对图像处理，包括图像归一化、颜色通道顺序调整、像素值标准化等操作
image = tf.keras.applications.mobilenet_v2.preprocess_input(image)

映射文件
预测完成后，你需要知道这个图片到底是什么？模型是基于Imagenet (ILSVRC-2012-CLS) 数据集训练的，因此你需要获取到对应的映射文件。这里我从网上找到了，但是有点小区别

官方用的映射文件在最前面添加了一个0，但是我找到的这个映射文件没有。如果修改序号的话，数据太多了，因此在获取类别时手动将序号进行了-1操作class_name = labels_dict[str(predicted_index-1)]

映射文件：

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对模型进行微调

MobileNetV2 文档说过可以根据自己的需求对模型进行微调，那么应该如何做呢？

官方教程：使用 TensorFlow Hub 进行迁移学习

刚好这篇教程也是使用的 MobileNetV2 模型，我们可以参照官方教程来进行模型微调。

使用

教程里介绍了如何使用MobileNetV2，这里我们可以学到一些其他知识。

预测网络图片

import PIL.Image as Image# 1、mobilenet需要的图片尺寸是 224 * 224
#image = tf.keras.preprocessing.image.load_img('pics/dog.png',target_size=(224,224))
image = tf.keras.utils.get_file('bird.jpg','https://scpic.chinaz.net/files/default/imgs/2023-08-29/7dc085b6d3291303.jpg')
image = Image.open(image).resize((224,224))

这里要注意，你更改网络图片地址后，一定要修改对应的名称，否则还会加载上一次的图片。


获取标签
之前还因为数据集不一致而犯愁，这里官方为我们提供了一种方式

labels_path = tf.keras.utils.get_file('ImageNetLabels.txt','https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')
imagenet_labels = np.array(open(labels_path).read().splitlines())

这个标签是一个纯文本，并且需要科学上网才能够访问

迁移学习

如何做呢？官方文档介绍

1、需要有数据集，文档已经为我们准备了（先用一下官方提供的数据集，后面研究一下如何自己制作数据集）
2、从 TensorFlow Hub 下载一个预训练模型，这个我们也已经有了
3、重新训练顶部（最后一个）层以识别自定义 数据集中的类

数据，花卉数据

import pathlib# 下载并解压到当前目录
data_file = tf.keras.utils.get_file('flower_photos.tgz','https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',cache_dir='.',extract=True)data_root = pathlib.Path(data_file).with_suffix('')

上面是官方文档的方式，这里我们直接通过浏览器下载并解压到当前文件夹里（需要科学上网）

文件还是比较大的


看了一下图片，都是普通的图片，这样方便我们后面自己整理训练集数据，当然最好图片也别搞得太大

加载数据并划分数据集

# 32张图片为一个批次，尺寸设置为224*224
batch_size = 32
img_height = 224
img_width = 224# 加载图像数据集，并将其分割为训练集和验证集，验证集比例为20%
train_ds = tf.keras.utils.image_dataset_from_directory('flower_photos',  # 目录validation_split=0.2, # 验证集占20%subset="training", # 将数据集划分为训练集seed= 123, # 随机种子，用于数据集随机划分image_size= (img_width,img_height) , # 调整图像大小batch_size= batch_size  # 每个批次中包含的图像数量
)
# 验证集
val_ds = tf.keras.utils.image_dataset_from_directory('flower_photos',  # 目录validation_split=0.2, # 验证集占20%subset="validation", # 将数据集划分为验证集seed= 123, # 随机种子，用于数据集随机划分image_size= (img_width,img_height) , # 调整图像大小batch_size= batch_size  # 每个批次中包含的图像数量
)

执行后可以看到控制台输出如下信息，发现了3670张图片，将这些图片归为5类，其中2936张图片用于训练。这里归为5类刚好对于目录下有5中不同的图片

获取花卉种类

# 花卉种类
class_names = np.array(train_ds.class_names)
print("花卉种类：",class_names)

刚好对应那5个文件夹的名称

对训练集和验证集的图像数据进行归一化处理
归一化的目的，在另一篇文章已经解释过了，这里不再说了

# 归一化
normalization_layer = tf.keras.layers.Rescaling(1./255) # 创建了一个Rescaling层，将像素值缩放到0到1之间 。 1./255是 1/255保留小数，差点没看懂
train_ds = train_ds.map(lambda x,y:(normalization_layer(x),y))
val_ds = val_ds.map(lambda x, y: (normalization_layer(x), y))

对数据集进行缓存和预取操作

# 使用缓冲预取，避免产生I/O阻塞
AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)# 验证数据是否成功加载和处理
for image_batch, labels_batch in train_ds:print(image_batch.shape)print(labels_batch.shape)break

输出结果与官方文档一致，可以继续

(32, 224, 224, 3)表示一个一个批次有32张图片，尺寸是224*224，图像的通道数是3（RGB通道）

对一批图像运行分类器，进行预测

# 对一批图片运行分类器，进行预测
result_batch = model.predict(train_ds)
# 加载映射文件，这里我将其下载到了本地
# 文件下载地址：https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt
imagenet_labels = np.array(open('labels.txt').read().splitlines())
# 在给定的张量中找到沿指定轴的最大值的索引
predict_class_names = imagenet_labels[tf.math.argmax(result_batch, axis=-1)]
print("预测类别：",predict_class_names)# 绘制出预测与图片
plt.figure(figsize=(10,9))
plt.subplots_adjust(hspace=0.5)
for n in range(30):plt.subplot(6,5,n+1)plt.imshow(image_batch[n])plt.title(predict_class_names[n])plt.axis('off')
_ = plt.suptitle("ImageNet predictions")
plt.show()

与官方运行效果一致，可以继续往下进行。

下载无头模式
之前使用的模型是用来进行分类的，现在按照官方教程还需要下载对于的 特征提取模型。分类模型 imagenet_mobilenet_v2_140_224_classification_5 对应的特征提取模型下载地址是：https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/feature_vector/5

下载并解压到项目里，进行使用

# 加载特征提取器
feature_extractor_layer = hub.KerasLayer('imagenet_mobilenet_v2_140_224_feature_vector_5', # 预训练模型input_shape=(224,224,3), # 指定图像输入的高度、宽度和通道数trainable=False  #训练过程中不更新特征提取器的权重
)
# 特征提取器为每个图像返回一个 1280 长的向量（在此示例中，图像批大小仍为 32）
feature_batch = feature_extractor_layer(image_batch)
print("特征批次形状：",feature_batch.shape)

问题不大，是因为使用的模型不同引起的。

附加分类头

# 附加分类头
new_model = tf.keras.Sequential([feature_extractor_layer,tf.keras.layers.Dense(len(class_names)) # 指定输出分类，这里的花是5类
])

可以正常执行，继续

训练模型
现在已经提取出了特征，接下来是训练模型。

# 开始训练，暂时只训练10轮。history记录了训练过程中的各项指标，便于后续分析和可视化
history = new_model.fit(train_ds, # 训练数据集validation_data=val_ds, # 验证数据集，用于在训练过程中监控模型的性能epochs=10, # 训练的总轮次callbacks=tensorboard_callback # 回调函数，用于在训练过程中执行特定操作，比如记录日志
)

从下图可以看到，经过10轮的训练，准确率越来越高

文件夹下也可以看见训练日志：

另外在控制台运行：tensorboard --logdir logs/fit 启动 TensorBoard
控制台会返回一个网址，可以查看指标如何随每个纪元而变化，并跟踪其他标量值

检查预测

到这里基本上差不多了，现在进行简单的预测

predicted_batch = new_model.predict(image_batch)
predicted_id = tf.math.argmax(predicted_batch, axis=-1)
predicted_label_batch = class_names[predicted_id]
print("花卉种类：",predicted_label_batch)plt.figure(figsize=(10,9))
plt.subplots_adjust(hspace=0.5)for n in range(30):plt.subplot(6,5,n+1)plt.imshow(image_batch[n])plt.title(predicted_label_batch[n].title())plt.axis('off')
_ = plt.suptitle("Model predictions")
plt.show()

运行结果与官方文档一致

导出并重新加载模型
模型训练好后，可以将其导出为SavedModel，以便以后重复使用。

SavedModel是TensorFlow中的一种模型保存格式。它是一种用于将训练好的模型保存到磁盘并能够方便地加载和使用的标准化格式。SavedModel可以保存模型的架构、权重参数、优化器状态以及其他与模型相关的设置。

SavedModel提供了一种跨平台、跨语言的模型保存和加载方式，可以在不同的TensorFlow版本和不同的编程语言中使用。这使得我们可以将模型从一种环境迁移到另一种环境，或者与其他TensorFlow应用程序共享模型。

在导出模型时，你可以使用tf.saved_model.save()函数将模型保存为SavedModel格式。这个函数接受模型对象和保存路径作为参数，并将模型的相关信息保存到指定路径下的文件夹中。例如：

tf.saved_model.save(model, export_path)

其中，model是你要保存的模型对象，export_path是保存路径。

导出模型后，你可以在其他地方使用tf.saved_model.load()函数加载SavedModel，并使用加载的模型进行预测或其他操作。

导出

# 导出训练好的模型
export_path = '/tmp/saved_models/flower_model'
new_model.save(export_path)

完整代码

# 导入tensorflow 和科学计算库
import tensorflow as tf
import numpy as np
# tensorflow-hub是一个TensorFlow库的扩展，它提供了一个简单的接口，用于重用已经训练好的机器学习模型的部分
import tensorflow_hub as hub
# 字体属性
from matplotlib.font_manager import FontProperties
# matplotlib是用于绘制图表和可视化数据的库
import matplotlib.pylab as pltimport datetime# 导入模型
# 不能直接加载模型文件，需要加载器目录
model = tf.keras.Sequential([hub.KerasLayer('imagenet_mobilenet_v2_140_224_classification_5')
])# 32张图片为一个批次，尺寸设置为224*224
batch_size = 32
img_height = 224
img_width = 224# 加载图像数据集，并将其分割为训练集和验证集，验证集比例为20%
train_ds = tf.keras.utils.image_dataset_from_directory('flower_photos',  # 目录validation_split=0.2, # 验证集占20%subset="training", # 将数据集划分为训练集seed= 123, # 随机种子，用于数据集随机划分image_size= (img_width,img_height) , # 调整图像大小batch_size= batch_size  # 每个批次中包含的图像数量
)
# 验证集
val_ds = tf.keras.utils.image_dataset_from_directory('flower_photos',  # 目录validation_split=0.2, # 验证集占20%subset="validation", # 将数据集划分为验证集seed= 123, # 随机种子，用于数据集随机划分image_size= (img_width,img_height) , # 调整图像大小batch_size= batch_size  # 每个批次中包含的图像数量
)# 花卉种类
class_names = np.array(train_ds.class_names)
print("花卉种类：",class_names)# 归一化
normalization_layer = tf.keras.layers.Rescaling(1./255) # 创建了一个Rescaling层，将像素值缩放到0到1之间 。 1./255是 1/255保留小数，差点没看懂
train_ds = train_ds.map(lambda x,y:(normalization_layer(x),y))
val_ds = val_ds.map(lambda x, y: (normalization_layer(x), y))# 使用缓冲预取，避免产生I/O阻塞
AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)# 验证数据是否成功加载和处理
for image_batch, labels_batch in train_ds:print(image_batch.shape)print(labels_batch.shape)break# 对一批图片运行分类器，进行预测
result_batch = model.predict(train_ds)
# 加载映射文件，这里我将其下载到了本地
imagenet_labels = np.array(open('labels.txt').read().splitlines())
# 在给定的张量中找到沿指定轴的最大值的索引
predict_class_names = imagenet_labels[tf.math.argmax(result_batch, axis=-1)]
print("预测类别：",predict_class_names)# 绘制出预测与图片
# plt.figure(figsize=(10,9))
# plt.subplots_adjust(hspace=0.5)
# for n in range(30):
#   plt.subplot(6,5,n+1)
#   plt.imshow(image_batch[n])
#   plt.title(predict_class_names[n])
#   plt.axis('off')
# _ = plt.suptitle("ImageNet predictions")
# plt.show()# 加载特征提取器
feature_extractor_layer = hub.KerasLayer('imagenet_mobilenet_v2_140_224_feature_vector_5', # 预训练模型input_shape=(224,224,3), # 指定图像输入的高度、宽度和通道数trainable=False  #训练过程中不更新特征提取器的权重
)
# 特征提取器为每个图像返回一个 1280 长的向量（在此示例中，图像批大小仍为 32）
feature_batch = feature_extractor_layer(image_batch)
print("特征批次形状：",feature_batch.shape)# 附加分类头
new_model = tf.keras.Sequential([feature_extractor_layer,tf.keras.layers.Dense(len(class_names)) # 指定输出分类，这里的花是5类
])# 训练模型
new_model.compile(optimizer=tf.keras.optimizers.Adam(),  # 使用Adam优化器作为优化算法loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), # 使用SparseCategoricalCrossentropy作为损失函数metrics=['acc'] # 使用准确率作为评估指标
)
# 训练日志
log_dir = "logs/fit/" + datetime.datetime.now().strftime('%Y%m%d-%H%M%S')
# 用于在训练过程中收集模型指标和摘要数据，并将其写入TensorBoard日志文件中
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir= log_dir,histogram_freq=1
)# 开始训练，暂时只训练10轮。history记录了训练过程中的各项指标，便于后续分析和可视化
history = new_model.fit(train_ds, # 训练数据集validation_data=val_ds, # 验证数据集，用于在训练过程中监控模型的性能epochs=10, # 训练的总轮次callbacks=tensorboard_callback # 回调函数，用于在训练过程中执行特定操作，比如记录日志
)# 简单预测
# predicted_batch = new_model.predict(image_batch)
# predicted_id = tf.math.argmax(predicted_batch, axis=-1)
# predicted_label_batch = class_names[predicted_id]
# print("花卉种类：",predicted_label_batch)# plt.figure(figsize=(10,9))
# plt.subplots_adjust(hspace=0.5)# for n in range(30):
#   plt.subplot(6,5,n+1)
#   plt.imshow(image_batch[n])
#   plt.title(predicted_label_batch[n].title())
#   plt.axis('off')
# _ = plt.suptitle("Model predictions")
# plt.show()# 导出训练好的模型
export_path = 'tmp/saved_models/flower_model'
new_model.save(export_path)

使用微调后的模型

模型已经保存到本地，现在我们来使用一下

# 导入tensorflow 和科学计算库
import tensorflow as tf
import numpy as np
# matplotlib是用于绘制图表和可视化数据的库
import matplotlib.pylab as plt
import PIL.Image as Image
from matplotlib.font_manager import FontProperties# 导入模型
model = tf.keras.models.load_model('tmp/saved_models/flower_model')# 预处理输入数据 
image = tf.keras.utils.get_file('sunflower.jpg','https://scpic.chinaz.net/files/pic/pic9/202006/bpic20492.jpg')
image = Image.open(image).resize((224,224))
image = tf.keras.preprocessing.image.img_to_array(image)
image = np.expand_dims(image, axis=0)
image = tf.keras.applications.mobilenet_v2.preprocess_input(image)# 预测
predictions = model.predict(image)
predicted_index = np.argmax(predictions)print("索引是：",predicted_index)class_names= ['雏菊','蒲公英' ,'玫瑰' ,'向日葵' ,'郁金香']# 可视化显示
font = FontProperties()
font.set_family('Microsoft YaHei')
plt.figure() # 创建图像窗口
plt.xticks([])
plt.yticks([])
plt.grid(False) # 取消网格线
plt.imshow(image[0]) # 显示图片
plt.xlabel(class_names[predicted_index] ,fontproperties=font)
plt.show() # 显示图形窗口

非常成功

使用自己的数据训练模型

上面的代码已经非常成功了，但是使用的数据是官方提供的，这里我们基于上面的教程，将数据集换成自己的。
我这里简单准备了45张猫咪的图片，15张白猫、15张黑猫、15张黑白猫。数据量不够，不够可以简单试一下效果

数据

训练导出模型

准确率有点高，感觉有点慌

使用

还能接受吧