目录

一、前言

二、我的环境

三、代码实现

 四、VGG-16框架

 五、LeNet5模型

六、模型改进

一、前言

>- **🍨 本文为[🔗365天深度学习训练营](https://mp.weixin.qq.com/s/xLjALoOD8HPZcH563En8bQ) 中的学习记录博客**
>- **🍦 参考文章：365天深度学习训练营-第7周：咖啡豆识别（训练营内部成员可读）**
>- **🍖 原作者：[K同学啊|接辅导、项目定制](https://mtyjkh.blog.csdn.net/)**

● 难度：夯实基础⭐⭐
● 语言：Python3、TensorFlow2
● 时间：9月5-9月9日🍺 要求：
1. 自己搭建VGG-16网络框架
2. 调用官方的VGG-16网络框架🍻 拔高（可选）：
1. 验证集准确率达到100%
2. 使用PPT画出VGG-16算法框架图（发论文需要这项技能）🔎 探索（难度有点大）
1. 在不影响准确率的前提下轻量化模型
○ 目前VGG16的Total params是134,276,932

二、我的环境

语言环境：Python3.7

编译器：jupyter notebook

深度学习环境：TensorFlow2

三、代码实现

import tensorflow as tfgpus = 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")from tensorflow.keras import layers
import numpy             as np
import matplotlib.pyplot as plt
import pathlibdata_dir = "./49-data/"
data_dir = pathlib.Path(data_dir)image_count = len(list(data_dir.glob('*/*.png')))print("图片总数为：",image_count)batch_size = 32
img_height = 224
img_width = 224"""
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=123,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=123,image_size=(img_height, img_width),batch_size=batch_size)class_names = train_ds.class_names
print(class_names)plt.figure(figsize=(10, 4))  # 图形的宽为10高为5for images, labels in train_ds.take(1):for i in range(10):ax = plt.subplot(2, 5, i + 1)plt.imshow(images[i].numpy().astype("uint8"))plt.title(class_names[labels[i]])plt.axis("off")for image_batch, labels_batch in train_ds:print(image_batch.shape)print(labels_batch.shape)breakAUTOTUNE = tf.data.AUTOTUNEtrain_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds   = val_ds.cache().prefetch(buffer_size=AUTOTUNE)normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)train_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
val_ds   = val_ds.map(lambda x, y: (normalization_layer(x), y))image_batch, labels_batch = next(iter(val_ds))
first_image = image_batch[0]# 查看归一化后的数据
print(np.min(first_image), np.max(first_image))# model = tf.keras.applications.VGG16(weights='imagenet')
# model.summary()from tensorflow.keras import Input
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flattendef VGG16(nb_classes, input_shape):input_tensor = Input(shape=input_shape)# 1st blockx = 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 blockx = 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 blockx = 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 blockx = 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 blockx = 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 connectionx = 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 modelmodel=VGG16(len(class_names), (img_width, img_height, 3))
model.summary()# 设置初始学习率
initial_learning_rate = 1e-4lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(initial_learning_rate,decay_steps=30,      # 敲黑板！！！这里是指 steps，不是指epochsdecay_rate=0.92,     # lr经过一次衰减就会变成 decay_rate*lrstaircase=True)# 设置优化器
opt = tf.keras.optimizers.Adam(learning_rate=initial_learning_rate)model.compile(optimizer=opt,loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])epochs = 20history = model.fit(train_ds,validation_data=val_ds,epochs=epochs
)acc = history.history['accuracy']
val_acc = history.history['val_accuracy']loss = history.history['loss']
val_loss = history.history['val_loss']epochs_range = range(epochs)plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

 四、VGG-16框架

 五、LeNet5模型

def LeNet5(nb_classes, input_shape):input_tensor = Input(shape=input_shape)# 1st blockx = Conv2D(6, (5,5), activation='sigmoid', 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)x = Conv2D(16, (5,5), activation='sigmoid', padding='same',name='block2_conv1')(x)x =MaxPooling2D((2,2),strides=(2,2),name = 'block2_pool')(x)# full connectionx = Flatten()(x)x = Dense(120, activation='sigmoid',  name='fc1')(x)x = Dense(84, activation='sigmoid', name='fc2')(x)output_tensor = Dense(nb_classes, activation='softmax', name='predictions')(x)model = Model(input_tensor, output_tensor)return modelmodel=LeNet5(len(class_names), (img_width, img_height, 3))
model.summary()

效果极差，下次一定不用 

六、模型改进

1、调低学习率（或按迭代次数衰减）
2、调整参数的初始化方法
3、调整输入数据的标准化方法
4、修改Loss函数
5、增加正则化
6、使用BN/GN层（中间层数据的标准化）
7、使用dropout

优化1

model = keras.models.Sequential()# 优化 增加L2正则化
model.add(keras.layers.Conv2D(64, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay)))
model.add(keras.layers.Activation('relu'))
# 优化 添加BN层和Dropout
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Dropout(0.3))model.add(keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2))model.add(keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'))
model.add(keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'))
model.add(keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2))model.add(keras.layers.Conv2D(256, (3, 3), padding='same', activation='relu'))
model.add(keras.layers.Conv2D(256, (3, 3), padding='same', activation='relu'))
model.add(keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2))model.add(keras.layers.Conv2D(512, (3, 3), padding='same', activation='relu'))
model.add(keras.layers.Conv2D(512, (3, 3), padding='same', activation='relu'))
model.add(keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2))model.add(keras.layers.Conv2D(512, (3, 3), padding='same', activation='relu'))
model.add(keras.layers.Conv2D(512, (3, 3), padding='same', activation='relu'))
model.add(keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2))model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(256, activation='relu'))  # VGG16为4096
model.add(keras.layers.Dense(128, activation='relu'))  # VGG16为4096
model.add(keras.layers.Dense(num_classes, activation='softmax'))  # VGG16为1000

优化2

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1layers.Activation('relu'),  # 激活层1layers.Conv2D(filters=64, kernel_size=(3, 3), padding='same', ),#layers.BatchNormalization(),  # BN层1layers.Activation('relu') , # 激活层1layers.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1layers.Activation('relu'),  # 激活层1layers.Conv2D(filters=128, kernel_size=(3, 3), padding='same'),#layers.BatchNormalization(),  # BN层1layers.Activation('relu'),  # 激活层1layers.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1layers.Activation('relu'),  # 激活层1layers.Conv2D(filters=256, kernel_size=(3, 3), padding='same'),#layers.BatchNormalization() , # BN层1layers.Activation('relu') , # 激活层1layers.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1layers.Activation('relu') , # 激活层1layers.Conv2D(filters=512, kernel_size=(3, 3), padding='same'),#layers.BatchNormalization() , # BN层1layers.Activation('relu'),  # 激活层1layers.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1layers.Activation('relu'),  # 激活层1layers.Conv2D(filters=512, kernel_size=(3, 3), padding='same'),#layers.BatchNormalization(),  # BN层1layers.Activation('relu'),  # 激活层1layers.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()