[人工智能] vgg16猫狗识别

from tensorflow import keras
from tensorflow.keras import layers, models
import os, PIL, pathlib
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import tensorflow as tf
import tensorflow as tf
import os,PIL
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
#os.environ['CUDA_VISIBLE_DEVICES']='0'
os.environ['CUDA_VISIBLE_DEVICES']='2'
# os.environ['TF_CPP_MIN_LOG_LEVEL']='2'#屏蔽通知和警告信息
import os,PIL
os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices'
from tensorflow import keras
keras.backend.clear_session()

设置gpu

from tensorflow       import keras
from tensorflow.keras import layers,models
import os, PIL, pathlib
import matplotlib.pyplot as plt
import tensorflow        as tf
import numpy             as np
 
gpus = tf.config.list_physical_devices("GPU")
 
if gpus:
    gpu0 = gpus[0]                                        #如果有多个GPU，仅使用第0个GPU
    tf.config.experimental.set_memory_growth(gpu0, True)  #设置GPU显存用量按需使用
    tf.config.set_visible_devices([gpu0],"GPU")
    
gpus

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,pathlib

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

data_dir = "./365-7-data"
data_dir = pathlib.Path(data_dir)

3.查看数据

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

print("图片总数为：",image_count)

二数据预处理

1.加载数据

设置数据尺寸

batch_size = 8
img_height = 224
img_width = 224

设置dataset

"""
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)

"""
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)

关于image_dataset_from_directory()的详细介绍可以参考文章：(7条消息) tf.keras.preprocessing.image_dataset_from_directory() 简介_K同学啊的博客-CSDN博客_tf.keras.preprocessing.image

???输出经过image_dataset_from_directory()分类后的标签

class_names = train_ds.class_names
print(class_names)

2.可视化数据

打印部分图片

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")

3.再次检查数据

输出数据的尺寸

for image_batch, labels_batch in train_ds:
    print(image_batch.shape)
    print(labels_batch.shape)
    break

4.配置数据集

shuffle：打乱数据集

prefetch：加速处理

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)

三搭建网络

本文神经网络为官方vgg16模型，我们需要做的是对最后一层按我们的类别进行分类即可。

调用官方

model = keras.applications.VGG16(include_top=False,weights='imagenet')
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
prediction_layer1 = tf.keras.layers.Dense(1024,activation='relu')
prediction_layer2 = tf.keras.layers.Dense(512,activation='relu')
prediction_layer3 = tf.keras.layers.Dense(len(class_names),activation='softmax')
 
model = tf.keras.Sequential([
  model,
  global_average_layer,
  prediction_layer1,
  prediction_layer2,
  prediction_layer3
])
model.summary()

?关于VGG16函数参考下面文章

(7条消息) keras 自带VGG16 net 参数分析_vola9527的博客-CSDN博客

我们对vgg16前部分卷积层没有改变，而是不选择官方的dense层，因为我们需要根据自己的数据进行输出，官方输出为1000种分类，我们只有2种，因此按照这个写法即可。?

?自己搭建

model = models.Sequential([
    layers.experimental.preprocessing.Rescaling( 1. ,input_shape=(img_height, img_width, 3)),
    layers.Conv2D(filters=64, kernel_size=(3, 3), padding='same'),  # 卷积层1
    #layers.BatchNormalization(),  # BN层1
    layers.Activation('relu'),  # 激活层1
    layers.Conv2D(filters=64, kernel_size=(3, 3), padding='same', ),
    #layers.BatchNormalization(),  # BN层1
    layers.Activation('relu') , # 激活层1
    layers.MaxPool2D(pool_size=(2, 2), strides=2, padding='same'),
    #layers.Dropout(0.2),  # dropout层
    #
    layers.Conv2D(filters=128, kernel_size=(3, 3), padding='same'),
    #layers.BatchNormalization(),  # BN层1
    layers.Activation('relu'),  # 激活层1
    layers.Conv2D(filters=128, kernel_size=(3, 3), padding='same'),
    #layers.BatchNormalization(),  # BN层1
    layers.Activation('relu'),  # 激活层1
    layers.MaxPool2D(pool_size=(2, 2), strides=2, padding='same'),
    #layers.Dropout(0.2),  # dropout层
    #
    layers.Conv2D(filters=256, kernel_size=(3, 3), padding='same'),
    #layers.BatchNormalization() , # BN层1
    layers.Activation('relu'),  # 激活层1
    layers.Conv2D(filters=256, kernel_size=(3, 3), padding='same'),
    #layers.BatchNormalization() , # BN层1
    layers.Activation('relu') , # 激活层1
    layers.Conv2D(filters=256, kernel_size=(3, 3), padding='same'),
   # layers.BatchNormalization(),
    layers.Activation('relu'),
    layers.MaxPool2D(pool_size=(2, 2), strides=2, padding='same'),
    #layers.Dropout(0.2),
    #
    layers.Conv2D(filters=512, kernel_size=(3, 3), padding='same'),
   # layers.BatchNormalization() , # BN层1
    layers.Activation('relu') , # 激活层1
    layers.Conv2D(filters=512, kernel_size=(3, 3), padding='same'),
    #layers.BatchNormalization() , # BN层1
    layers.Activation('relu'),  # 激活层1
    layers.Conv2D(filters=512, kernel_size=(3, 3), padding='same'),
    #layers.BatchNormalization(),
    layers.Activation('relu'),
    layers.MaxPool2D(pool_size=(2, 2), strides=2, padding='same'),
    #layers.Dropout(0.2),
    #
    layers.Conv2D(filters=512, kernel_size=(3, 3), padding='same'),
   # layers.BatchNormalization() , # BN层1
    layers.Activation('relu'),  # 激活层1
    layers.Conv2D(filters=512, kernel_size=(3, 3), padding='same'),
    #layers.BatchNormalization(),  # BN层1
    layers.Activation('relu'),  # 激活层1
    layers.Conv2D(filters=512, kernel_size=(3, 3), padding='same'),
   # layers.BatchNormalization(),
    layers.Activation('relu'),
    layers.MaxPool2D(pool_size=(2, 2), strides=2, padding='same'),
    #layers.Dropout(0.2),
 
 
    layers.Flatten(),  # Flatten层，连接卷积层与全连接层
    layers.Dense(4096, activation='relu'),  # 全连接层，特征进一步提取
    layers.Dense(4096, activation='relu'),  # 全连接层，特征进一步提取
    layers.Dense(len(class_names),activation='softmax')  # 输出层，输出预期结果
])
model.summary()

四训练模型

模型训练时，需要完成如下设置

损失函数（loss）：衡量模型准确率

优化器（optimizer）：根据损失函数进行优化更新

指标（metrics）：监控训练过程，保存最优模型

1.设置学习率

model.compile(optimizer="adam",
              loss     ='sparse_categorical_crossentropy',
              metrics  =['accuracy'])

注意损失函数不要用错了！

对于损失函数可以参考下面文章

(7条消息) tensorflow损失函数详解_重邮研究森的博客-CSDN博客

此外，需要注意在dataset设置中label_model的设置会影响loss

2.早期与保存最佳模型参数
关于ModelCheckpoint参考下面文章

(7条消息) ModelCheckpoint 讲解【TensorFlow2入门手册】_K同学啊的博客-CSDN博客_modelcheckpoint的filepath

2.模型训练

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)
        
        train_loss     = []
        train_accuracy = []
        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://www.yuque.com/mingtian-fkmxf/hv4lcq/ztt4gy
            """
             # 这里生成的是每一个batch的acc与loss
            history = model.train_on_batch(image,label)
            
            train_loss.append(history[0])
            train_accuracy.append(history[1])
            
            pbar.set_postfix({"train_loss": "%.4f"%history[0],
                              "train_acc":"%.4f"%history[1],
                              "lr": K.get_value(model.optimizer.lr)})
            pbar.update(1)
            
        history_train_loss.append(np.mean(train_loss))
        history_train_accuracy.append(np.mean(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:

        val_loss     = []
        val_accuracy = []
        for image,label in val_ds:      
            # 这里生成的是每一个batch的acc与loss
            history = model.test_on_batch(image,label)
            
            val_loss.append(history[0])
            val_accuracy.append(history[1])
            
            pbar.set_postfix({"val_loss": "%.4f"%history[0],
                              "val_acc":"%.4f"%history[1]})
            pbar.update(1)
        history_val_loss.append(np.mean(val_loss))
        history_val_accuracy.append(np.mean(val_accuracy))
            
    print('结束验证！')
    print("验证loss为：%.4f"%np.mean(val_loss))
    print("验证准确率为：%.4f"%np.mean(val_accuracy))

?五模型评估

1.Loss和Accuracy图

?2.指定图片进行预测

import numpy as np

# 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 = model.predict(img_array)
        plt.title(class_names[np.argmax(predictions)])

        plt.axis("off")