第41步深度学习图像识别：Efficientnet建模（Tensorflow）

一、写在前面

（1）Efficientnet

EfficientNet是Google在2019年提出的一种新的卷积神经网络架构，主要目标是提高模型的效率，即在保持或提高模型性能的同时，尽可能地降低模型的复杂性和计算需求。

EfficientNet的关键思想是均衡网络的深度（层数）、宽度（每层的通道数）和分辨率（输入的图像尺寸）。这是通过一种称为复合缩放（Compound Scaling）的方法实现的。在复合缩放中，深度、宽度和分辨率的缩放是同时进行的，而不是分别进行。这种方法能够有效地利用模型的参数，提高模型的性能。

EfficientNet包括多个变体，从EfficientNet-B0到EfficientNet-B7，其中"B"后面的数字越大，网络的深度和宽度越大，需要的计算资源也越多，但同时能够达到更高的性能。

（2）Efficientnet的预训练版本

Keras有Efficientnet的各种变体预训练模型，省事：

二、Efficientnet迁移学习代码实战

我们继续胸片的数据集：肺结核病人和健康人的胸片的识别。其中，肺结核病人700张，健康人900张，分别存入单独的文件夹中。使用的是EfficientnetB0这个网络。

（a）导入包

from tensorflow import keras
import tensorflow as tf
from tensorflow.python.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropout, Activation, Reshape, Softmax, GlobalAveragePooling2D, BatchNormalization
from tensorflow.python.keras.layers.convolutional import Convolution2D, MaxPooling2D
from tensorflow.python.keras import Sequential, initializers
from tensorflow.python.keras import Model
from tensorflow.python.keras.optimizers import adam_v2
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator, image_dataset_from_directory
from tensorflow.python.keras.layers.preprocessing.image_preprocessing import RandomFlip, RandomRotation, RandomContrast, RandomZoom, RandomTranslation
import os,PIL,pathlib
import warnings
#设置GPU
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")
    
warnings.filterwarnings("ignore")             #忽略警告信息
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

（b）导入数据集

#1.导入数据
data_dir = "./MTB"
data_dir = pathlib.Path(data_dir)
image_count = len(list(data_dir.glob('*/*')))
print("图片总数为：",image_count)

batch_size = 32
img_height = 100
img_width  = 100


train_ds = image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="training",
    seed=12,
    image_size=(img_height, img_width),
    batch_size=batch_size)

val_ds = image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="validation",
    seed=12,
    image_size=(img_height, img_width),
    batch_size=batch_size)

class_names = train_ds.class_names
print(class_names)
print(train_ds)


#2.检查数据
for image_batch, labels_batch in train_ds:
    print(image_batch.shape)
    print(labels_batch.shape)
    break

#3.配置数据
AUTOTUNE = tf.data.AUTOTUNE

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

train_ds = (
    train_ds.cache()
    .shuffle(800)
    .map(train_preprocessing)    

    .prefetch(buffer_size=AUTOTUNE)
)

val_ds = (
    val_ds.cache()
    .map(train_preprocessing)   

    .prefetch(buffer_size=AUTOTUNE)
)

#4. 数据可视化
plt.figure(figsize=(10, 8))  # 图形的宽为10高为5
plt.suptitle("数据展示")

class_names = ["Tuberculosis","Normal"]
for images, labels in train_ds.take(1):
    for i in range(15):
        plt.subplot(4, 5, i + 1)
        plt.xticks([])
        plt.yticks([])
        plt.grid(False)

        # 显示图片
        plt.imshow(images[i])
        # 显示标签
        plt.xlabel(class_names[labels[i]-1])

plt.show()

（c）数据增强

data_augmentation = Sequential([
  RandomFlip("horizontal_and_vertical"),
  RandomRotation(0.2),
  RandomContrast(1.0),
  RandomZoom(0.5,0.2),
  RandomTranslation(0.3,0.5),
])

def prepare(ds):
    ds = ds.map(lambda x, y: (data_augmentation(x, training=True), y), num_parallel_calls=AUTOTUNE)
    return ds
train_ds = prepare(train_ds)

（d）导入EfficientNetB0

#获取预训练模型对输入的预处理方法
from tensorflow.python.keras.applications.efficientnet import EfficientNetB0
from tensorflow.python.keras import Input, regularizers
IMG_SIZE = (img_height, img_width, 3)

base_model = EfficientNetB0(include_top=False, #是否包含顶层的全连接层
                         weights='imagenet')

inputs = Input(shape=IMG_SIZE)
#模型
x = base_model(inputs, training=False) #参数不变化
#全局池化
x = GlobalAveragePooling2D()(x)
#BatchNormalization
x = BatchNormalization()(x)
#Dropout
x = Dropout(0.8)(x)
#Dense
x = Dense(128, kernel_regularizer=regularizers.l2(0.1))(x)  # 添加 L2 正则化
#BatchNormalization
x = BatchNormalization()(x)
#激活函数
x = Activation('relu')(x)
#输出层
outputs = Dense(2, kernel_regularizer=regularizers.l2(0.1))(x)  # 添加 L2 正则化
#BatchNormalization
outputs = BatchNormalization()(outputs)
#激活函数
outputs = Activation('sigmoid')(outputs)
#整体封装
model = Model(inputs, outputs)
#打印模型结构
print(model.summary())

打印出模型的结构：

（e）编译模型

#定义优化器
from tensorflow.python.keras.optimizers import adam_v2, rmsprop_v2
#from tensorflow.python.keras.optimizer_v2.gradient_descent import SGD
optimizer = adam_v2.Adam()
#optimizer = SGD(learning_rate=0.001)
#optimizer = rmsprop_v2.RMSprop()
#编译模型
model.compile(optimizer=optimizer,
                loss='sparse_categorical_crossentropy',
                metrics=['accuracy'])

#训练模型
from tensorflow.python.keras.callbacks import ModelCheckpoint, Callback, EarlyStopping, ReduceLROnPlateau, LearningRateScheduler

NO_EPOCHS = 21
PATIENCE  = 10
VERBOSE   = 1

# 设置动态学习率
annealer = LearningRateScheduler(lambda x: 1e-5 * 0.99 ** (x+NO_EPOCHS))

#性能不提升时，减少学习率
#reduce = ReduceLROnPlateau(monitor='val_accuracy', 
#                           patience=PATIENCE,
#                           verbose=1,
#                           factor=0.8,
#                           min_lr=1e-6)

# 设置早停
earlystopper = EarlyStopping(monitor='loss', patience=PATIENCE, verbose=VERBOSE)

# 
checkpointer = ModelCheckpoint('mtb_jet_best_model_EfficientNetB0.h5',
                                monitor='val_accuracy',
                                verbose=VERBOSE,
                                save_best_only=True,
                                save_weights_only=True,
                                mode='max')

train_model  = model.fit(train_ds,
                  epochs=NO_EPOCHS,
                  verbose=1,
                  validation_data=val_ds,
                  callbacks=[earlystopper, checkpointer, annealer])

#保存模型
model.save('mtb_jet_best_model_EfficientNetB0.h5')
print("The trained model has been saved.")

模型训练速度也挺快的：

（f）Accuracy和Loss可视化

import matplotlib.pyplot as plt

loss = train_model.history['loss']
acc = train_model.history['accuracy']
val_loss = train_model.history['val_loss']
val_acc = train_model.history['val_accuracy']
epoch = range(1, len(loss)+1)

fig, ax = plt.subplots(1, 2, figsize=(10,4))
ax[0].plot(epoch, loss, label='Train loss')
ax[0].plot(epoch, val_loss, label='Validation loss')
ax[0].set_xlabel('Epochs')
ax[0].set_ylabel('Loss')
ax[0].legend()
ax[1].plot(epoch, acc, label='Train acc')
ax[1].plot(epoch, val_acc, label='Validation acc')
ax[1].set_xlabel('Epochs')
ax[1].set_ylabel('Accuracy')
ax[1].legend()
plt.show()

观察模型训练情况：

蓝色为训练集，橙色为验证集。可以看到，验证集的loss在中后期抖动的非常厉害，而准确率一直随缘波动。存在过拟合咯。

（g）混淆矩阵可视化以及模型参数

没啥好说的，都跟之前的ML模型类似：

import numpy as np
import matplotlib.pyplot as plt
from tensorflow.python.keras.models import load_model
from matplotlib.pyplot import imshow
from sklearn.metrics import classification_report, confusion_matrix
import seaborn as sns
import pandas as pd
import math
# 定义一个绘制混淆矩阵图的函数
def plot_cm(labels, predictions):
    
    # 生成混淆矩阵
    conf_numpy = confusion_matrix(labels, predictions)
    # 将矩阵转化为 DataFrame
    conf_df = pd.DataFrame(conf_numpy, index=class_names ,columns=class_names)  
    
    plt.figure(figsize=(8,7))
    
    sns.heatmap(conf_df, annot=True, fmt="d", cmap="BuPu")
    
    plt.title('混淆矩阵',fontsize=15)
    plt.ylabel('真实值',fontsize=14)
    plt.xlabel('预测值',fontsize=14)

val_pre   = []
val_label = []

for images, labels in val_ds:#这里可以取部分验证数据（.take(1)）生成混淆矩阵
    for image, label in zip(images, labels):
        # 需要给图片增加一个维度
        img_array = tf.expand_dims(image, 0) 
        # 使用模型预测图片中的人物
        prediction = model.predict(img_array)
        val_pre.append(np.argmax(prediction))
        val_label.append(label)

plot_cm(val_label, val_pre)

cm_val = confusion_matrix(val_label, val_pre)    
a_val = cm_val[0,0]
b_val = cm_val[0,1]
c_val = cm_val[1,0]
d_val = cm_val[1,1]
acc_val = (a_val+d_val)/(a_val+b_val+c_val+d_val) #准确率：就是被分对的样本数除以所有的样本数
error_rate_val = 1 - acc_val #错误率：与准确率相反，描述被分类器错分的比例
sen_val = d_val/(d_val+c_val) #灵敏度：表示的是所有正例中被分对的比例，衡量了分类器对正例的识别能力
sep_val = a_val/(a_val+b_val) #特异度：表示的是所有负例中被分对的比例，衡量了分类器对负例的识别能力
precision_val = d_val/(b_val+d_val) #精确度：表示被分为正例的示例中实际为正例的比例
F1_val = (2*precision_val*sen_val)/(precision_val+sen_val) #F1值：P和R指标有时候会出现的矛盾的情况，这样就需要综合考虑他们，最常见的方法就是F-Measure（又称为F-Score）
MCC_val = (d_val*a_val-b_val*c_val) / (math.sqrt((d_val+b_val)*(d_val+c_val)*(a_val+b_val)*(a_val+c_val))) #马修斯相关系数（Matthews correlation coefficient）：当两个类别具有非常不同的大小时，可以使用MCC
print("验证集的灵敏度为：",sen_val, 
      "验证集的特异度为：",sep_val,
      "验证集的准确率为：",acc_val, 
      "验证集的错误率为：",error_rate_val,
      "验证集的精确度为：",precision_val, 
      "验证集的F1为：",F1_val,
      "验证集的MCC为：",MCC_val)
    
train_pre   = []
train_label = []
for images, labels in train_ds:#这里可以取部分验证数据（.take(1)）生成混淆矩阵
    for image, label in zip(images, labels):
        # 需要给图片增加一个维度
        img_array = tf.expand_dims(image, 0)
        # 使用模型预测图片中的人物
        prediction = model.predict(img_array)

        train_pre.append(np.argmax(prediction))
        train_label.append(label)
        
plot_cm(train_label, train_pre)

cm_train = confusion_matrix(train_label, train_pre)  
a_train = cm_train[0,0]
b_train = cm_train[0,1]
c_train = cm_train[1,0]
d_train = cm_train[1,1]
acc_train = (a_train+d_train)/(a_train+b_train+c_train+d_train)
error_rate_train = 1 - acc_train
sen_train = d_train/(d_train+c_train)
sep_train = a_train/(a_train+b_train)
precision_train = d_train/(b_train+d_train)
F1_train = (2*precision_train*sen_train)/(precision_train+sen_train)
MCC_train = (d_train*a_train-b_train*c_train) / (math.sqrt((d_train+b_train)*(d_train+c_train)*(a_train+b_train)*(a_train+c_train))) 
print("训练集的灵敏度为：",sen_train, 
      "训练集的特异度为：",sep_train,
      "训练集的准确率为：",acc_train, 
      "训练集的错误率为：",error_rate_train,
      "训练集的精确度为：",precision_train, 
      "训练集的F1为：",F1_train,
      "训练集的MCC为：",MCC_train)

加载最优的那一次模型：

可以发现灵敏度远远低于特异度，需要调整阈值，这里就不调整了。参照之前的代码即可。

（h）AUC曲线绘制

from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.python.keras.models import load_model
from matplotlib.pyplot import imshow
from sklearn.metrics import classification_report, confusion_matrix
import seaborn as sns
import pandas as pd
import math

def plot_roc(name, labels, predictions, **kwargs):
    fp, tp, _ = metrics.roc_curve(labels, predictions)

    plt.plot(fp, tp, label=name, linewidth=2, **kwargs)
    plt.plot([0, 1], [0, 1], color='orange', linestyle='--')
    plt.xlabel('False positives rate')
    plt.ylabel('True positives rate')
    ax = plt.gca()
    ax.set_aspect('equal')


val_pre_auc   = []
val_label_auc = []

for images, labels in val_ds:
    for image, label in zip(images, labels):      
        img_array = tf.expand_dims(image, 0) 
        prediction_auc = model.predict(img_array)
        val_pre_auc.append((prediction_auc)[:,1])
        val_label_auc.append(label)
auc_score_val = metrics.roc_auc_score(val_label_auc, val_pre_auc)


train_pre_auc   = []
train_label_auc = []

for images, labels in train_ds:
    for image, label in zip(images, labels):
        img_array_train = tf.expand_dims(image, 0) 
        prediction_auc = model.predict(img_array_train)
        train_pre_auc.append((prediction_auc)[:,1])#输出概率而不是标签！
        train_label_auc.append(label)
auc_score_train = metrics.roc_auc_score(train_label_auc, train_pre_auc)

plot_roc('validation AUC: {0:.4f}'.format(auc_score_val), val_label_auc , val_pre_auc , color="red", linestyle='--')
plot_roc('training AUC: {0:.4f}'.format(auc_score_train), train_label_auc, train_pre_auc, color="blue", linestyle='--')
plt.legend(loc='lower right')
#plt.savefig("roc.pdf", dpi=300,format="pdf")

print("训练集的AUC值为：",auc_score_train, "验证集的AUC值为：",auc_score_val)

ROC曲线：