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 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")

from tensorflow.keras import layers
import numpy             as np
import matplotlib.pyplot as plt
import pathlib

data_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5

for 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)
    break

AUTOTUNE = tf.data.AUTOTUNE

train_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, Flatten


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(len(class_names), (img_width, img_height, 3))
model.summary()

# 设置初始学习率
initial_learning_rate = 1e-4

lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
        initial_learning_rate,
        decay_steps=30,      # 敲黑板！！！这里是指 steps，不是指epochs
        decay_rate=0.92,     # lr经过一次衰减就会变成 decay_rate*lr
        staircase=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 = 20

history = 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 block
    x = 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 connection
    x = 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 model

model=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()