卷积神经网络（ResNet-50）鸟类识别

本文主要是介绍卷积神经网络（ResNet-50）鸟类识别，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

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卷积神经网络（VGG-19）灵笼人物识别

前期工作

1. 设置GPU（如果使用的是CPU可以忽略这步）

我的环境：

• 语言环境：Python3.6.5
• 编译器：jupyter notebook
• 深度学习环境：TensorFlow2.4.1

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")

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# 设置随机种子尽可能使结果可以重现
import numpy as np
np.random.seed(1)# 设置随机种子尽可能使结果可以重现
import tensorflow as tf
tf.random.set_seed(1)from tensorflow import keras
from tensorflow.keras import layers,modelsimport pathlib

data_dir = "weather_photos/"
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

文件夹	数量
Bananaquit	166 张
Black Throated Bushtiti	111 张
Black skimmer	122 张
Cockatoo	166张

1. 加载数据

使用image_dataset_from_directory方法将磁盘中的数据加载到tf.data.Dataset中

batch_size = 32
img_height = 224
img_width = 224

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)

Found 565 files belonging to 4 classes.
Using 452 files for training.

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)

Found 565 files belonging to 4 classes.
Using 113 files for validation.

我们可以通过class_names输出数据集的标签。标签将按字母顺序对应于目录名称。

class_names = train_ds.class_names
print(class_names)

['Bananaquit', 'Black Skimmer', 'Black Throated Bushtiti', 'Cockatoo']

2. 可视化数据

plt.figure(figsize=(10, 5))  # 图形的宽为10高为5for images, labels in train_ds.take(1):for i in range(8):ax = plt.subplot(2, 4, i + 1)  plt.imshow(images[i].numpy().astype("uint8"))plt.title(class_names[labels[i]])plt.axis("off")

plt.imshow(images[1].numpy().astype("uint8"))

3. 再次检查数据

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

(8, 224, 224, 3)
(8,)

• Image_batch是形状的张量（8, 224, 224, 3)。这是一批形状240x240x3的8张图片（最后一维指的是彩色通道RGB）。
• Label_batch是形状（8，）的张量，这些标签对应8张图片

4. 配置数据集

AUTOTUNE = tf.data.AUTOTUNEtrain_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

三、残差网络（ResNet）介绍

1. 残差网络解决了什么

残差网络是为了解决神经网络隐藏层过多时，而引起的网络退化问题。退化（degradation）问题是指：当网络隐藏层变多时，网络的准确度达到饱和然后急剧退化，而且这个退化不是由于过拟合引起的。

拓展： 深度神经网络的“两朵乌云”

• 梯度弥散/爆炸

简单来讲就是网络太深了，会导致模型训练难以收敛。这个问题可以被标准初始化和中间层正规化的方法有效控制。（现阶段知道这么一回事就好了）

• 网络退化

随着网络深度增加，网络的表现先是逐渐增加至饱和，然后迅速下降，这个退化不是由于过拟合引起的。

2. ResNet-50介绍

ResNet-50有两个基本的块，分别名为Conv Block和Identity Block

四、构建ResNet-50网络模型

下面是本文的重点，可以试着按照上面三张图自己构建一下ResNet-50

from keras import layersfrom keras.layers import Input,Activation,BatchNormalization,Flatten
from keras.layers import Dense,Conv2D,MaxPooling2D,ZeroPadding2D,AveragePooling2D
from keras.models import Modeldef identity_block(input_tensor, kernel_size, filters, stage, block):filters1, filters2, filters3 = filtersname_base = str(stage) + block + '_identity_block_'x = Conv2D(filters1, (1, 1), name=name_base + 'conv1')(input_tensor)x = BatchNormalization(name=name_base + 'bn1')(x)x = Activation('relu', name=name_base + 'relu1')(x)x = Conv2D(filters2, kernel_size,padding='same', name=name_base + 'conv2')(x)x = BatchNormalization(name=name_base + 'bn2')(x)x = Activation('relu', name=name_base + 'relu2')(x)x = Conv2D(filters3, (1, 1), name=name_base + 'conv3')(x)x = BatchNormalization(name=name_base + 'bn3')(x)x = layers.add([x, input_tensor] ,name=name_base + 'add')x = Activation('relu', name=name_base + 'relu4')(x)return xdef conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):filters1, filters2, filters3 = filtersres_name_base = str(stage) + block + '_conv_block_res_'name_base = str(stage) + block + '_conv_block_'x = Conv2D(filters1, (1, 1), strides=strides, name=name_base + 'conv1')(input_tensor)x = BatchNormalization(name=name_base + 'bn1')(x)x = Activation('relu', name=name_base + 'relu1')(x)x = Conv2D(filters2, kernel_size, padding='same', name=name_base + 'conv2')(x)x = BatchNormalization(name=name_base + 'bn2')(x)x = Activation('relu', name=name_base + 'relu2')(x)x = Conv2D(filters3, (1, 1), name=name_base + 'conv3')(x)x = BatchNormalization(name=name_base + 'bn3')(x)shortcut = Conv2D(filters3, (1, 1), strides=strides, name=res_name_base + 'conv')(input_tensor)shortcut = BatchNormalization(name=res_name_base + 'bn')(shortcut)x = layers.add([x, shortcut], name=name_base+'add')x = Activation('relu', name=name_base+'relu4')(x)return xdef ResNet50(input_shape=[224,224,3],classes=1000):img_input = Input(shape=input_shape)x = ZeroPadding2D((3, 3))(img_input)x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)x = BatchNormalization(name='bn_conv1')(x)x = Activation('relu')(x)x = MaxPooling2D((3, 3), strides=(2, 2))(x)x =     conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')x =     conv_block(x, 3, [128, 128, 512], stage=3, block='a')x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')x =     conv_block(x, 3, [256, 256, 1024], stage=4, block='a')x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')x =     conv_block(x, 3, [512, 512, 2048], stage=5, block='a')x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')x = AveragePooling2D((7, 7), name='avg_pool')(x)x = Flatten()(x)x = Dense(classes, activation='softmax', name='fc1000')(x)model = Model(img_input, x, name='resnet50')# 加载预训练模型model.load_weights("resnet50_weights_tf_dim_ordering_tf_kernels.h5")return modelmodel = ResNet50()
model.summary()

Model: "resnet50"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            
__________________________________________________________________________________________________
zero_padding2d (ZeroPadding2D)  (None, 230, 230, 3)  0           input_1[0][0]                    
__________________________________________________________________________________________________
conv1 (Conv2D)                  (None, 112, 112, 64) 9472        zero_padding2d[0][0]             
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      
__________________________________________________________________________________________________
activation (Activation)         (None, 112, 112, 64) 0           bn_conv1[0][0]                   
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D)    (None, 55, 55, 64)   0           activation[0][0]                 
__________________________________________________________________________________________________
2a_conv_block_conv1 (Conv2D)    (None, 55, 55, 64)   4160        max_pooling2d[0][0]              
__________________________________________________________________________________________________
2a_conv_block_bn1 (BatchNormali (None, 55, 55, 64)   256         2a_conv_block_conv1[0][0]        
__________________________________________________________________________________________________
2a_conv_block_relu1 (Activation (None, 55, 55, 64)   0           2a_conv_block_bn1[0][0]          
__________________________________________________________________________________________________
2a_conv_block_conv2 (Conv2D)    (None, 55, 55, 64)   36928       2a_conv_block_relu1[0][0]        
__________________________________________________________________________________________________
2a_conv_block_bn2 (BatchNormali (None, 55, 55, 64)   256         2a_conv_block_conv2[0][0]        
__________________________________________________________________________________________________
2a_conv_block_relu2 (Activation (None, 55, 55, 64)   0           2a_conv_block_bn2[0][0]          
__________________________________________________________________________________________________
2a_conv_block_conv3 (Conv2D)    (None, 55, 55, 256)  16640       2a_conv_block_relu2[0][0]        
__________________________________________________________________________________________________
2a_conv_block_res_conv (Conv2D) (None, 55, 55, 256)  16640       max_pooling2d[0][0]              
__________________________________________________________________________________________________
2a_conv_block_bn3 (BatchNormali (None, 55, 55, 256)  1024        2a_conv_block_conv3[0][0]        
__________________________________________________________________________________________________
2a_conv_block_res_bn (BatchNorm (None, 55, 55, 256)  1024        2a_conv_block_res_conv[0][0]     
__________________________________________________________________________________________________
2a_conv_block_add (Add)         (None, 55, 55, 256)  0           2a_conv_block_bn3[0][0]          2a_conv_block_res_bn[0][0]       
__________________________________________________________________________________________________
2a_conv_block_relu4 (Activation (None, 55, 55, 256)  0           2a_conv_block_add[0][0]          
__________________________________________________________________________________________________
2b_identity_block_conv1 (Conv2D (None, 55, 55, 64)   16448       2a_conv_block_relu4[0][0]        
__________________________________________________________________________________________________
2b_identity_block_bn1 (BatchNor (None, 55, 55, 64)   256         2b_identity_block_conv1[0][0]    
__________________________________________________________________________________________________
2b_identity_block_relu1 (Activa (None, 55, 55, 64)   0           2b_identity_block_bn1[0][0]      
__________________________________________________________________________________________________
2b_identity_block_conv2 (Conv2D (None, 55, 55, 64)   36928       2b_identity_block_relu1[0][0]    
__________________________________________________________________________________________________
2b_identity_block_bn2 (BatchNor (None, 55, 55, 64)   256         2b_identity_block_conv2[0][0]    
__________________________________________________________________________________________________
2b_identity_block_relu2 (Activa (None, 55, 55, 64)   0           2b_identity_block_bn2[0][0]      
__________________________________________________________________________________________________
2b_identity_block_conv3 (Conv2D (None, 55, 55, 256)  16640       2b_identity_block_relu2[0][0]    
__________________________________________________________________________________________________
2b_identity_block_bn3 (BatchNor (None, 55, 55, 256)  1024        2b_identity_block_conv3[0][0]    
__________________________________________________________________________________________________
2b_identity_block_add (Add)     (None, 55, 55, 256)  0           2b_identity_block_bn3[0][0]      2a_conv_block_relu4[0][0]        
__________________________________________________________________________________________________
2b_identity_block_relu4 (Activa (None, 55, 55, 256)  0           2b_identity_block_add[0][0]      
__________________________________________________________________________________________________
2c_identity_block_conv1 (Conv2D (None, 55, 55, 64)   16448       2b_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
2c_identity_block_bn1 (BatchNor (None, 55, 55, 64)   256         2c_identity_block_conv1[0][0]    
__________________________________________________________________________________________________
2c_identity_block_relu1 (Activa (None, 55, 55, 64)   0           2c_identity_block_bn1[0][0]      
__________________________________________________________________________________________________
2c_identity_block_conv2 (Conv2D (None, 55, 55, 64)   36928       2c_identity_block_relu1[0][0]    
__________________________________________________________________________________________________
2c_identity_block_bn2 (BatchNor (None, 55, 55, 64)   256         2c_identity_block_conv2[0][0]    
__________________________________________________________________________________________________
2c_identity_block_relu2 (Activa (None, 55, 55, 64)   0           2c_identity_block_bn2[0][0]      
__________________________________________________________________________________________________
2c_identity_block_conv3 (Conv2D (None, 55, 55, 256)  16640       2c_identity_block_relu2[0][0]    
__________________________________________________________________________________________________
2c_identity_block_bn3 (BatchNor (None, 55, 55, 256)  1024        2c_identity_block_conv3[0][0]    
__________________________________________________________________________________________________
2c_identity_block_add (Add)     (None, 55, 55, 256)  0           2c_identity_block_bn3[0][0]      2b_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
2c_identity_block_relu4 (Activa (None, 55, 55, 256)  0           2c_identity_block_add[0][0]      
__________________________________________________________________________________________________
3a_conv_block_conv1 (Conv2D)    (None, 28, 28, 128)  32896       2c_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
3a_conv_block_bn1 (BatchNormali (None, 28, 28, 128)  512         3a_conv_block_conv1[0][0]        
__________________________________________________________________________________________________
3a_conv_block_relu1 (Activation (None, 28, 28, 128)  0           3a_conv_block_bn1[0][0]          
__________________________________________________________________________________________________
3a_conv_block_conv2 (Conv2D)    (None, 28, 28, 128)  147584      3a_conv_block_relu1[0][0]        
__________________________________________________________________________________________________
3a_conv_block_bn2 (BatchNormali (None, 28, 28, 128)  512         3a_conv_block_conv2[0][0]        
__________________________________________________________________________________________________
3a_conv_block_relu2 (Activation (None, 28, 28, 128)  0           3a_conv_block_bn2[0][0]          
__________________________________________________________________________________________________
3a_conv_block_conv3 (Conv2D)    (None, 28, 28, 512)  66048       3a_conv_block_relu2[0][0]        
__________________________________________________________________________________________________
3a_conv_block_res_conv (Conv2D) (None, 28, 28, 512)  131584      2c_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
3a_conv_block_bn3 (BatchNormali (None, 28, 28, 512)  2048        3a_conv_block_conv3[0][0]        
__________________________________________________________________________________________________
3a_conv_block_res_bn (BatchNorm (None, 28, 28, 512)  2048        3a_conv_block_res_conv[0][0]     
__________________________________________________________________________________________________
3a_conv_block_add (Add)         (None, 28, 28, 512)  0           3a_conv_block_bn3[0][0]          3a_conv_block_res_bn[0][0]       
__________________________________________________________________________________________________
3a_conv_block_relu4 (Activation (None, 28, 28, 512)  0           3a_conv_block_add[0][0]          
__________________________________________________________________________________________________
3b_identity_block_conv1 (Conv2D (None, 28, 28, 128)  65664       3a_conv_block_relu4[0][0]        
__________________________________________________________________________________________________
3b_identity_block_bn1 (BatchNor (None, 28, 28, 128)  512         3b_identity_block_conv1[0][0]    
__________________________________________________________________________________________________
3b_identity_block_relu1 (Activa (None, 28, 28, 128)  0           3b_identity_block_bn1[0][0]      
__________________________________________________________________________________________________
3b_identity_block_conv2 (Conv2D (None, 28, 28, 128)  147584      3b_identity_block_relu1[0][0]    
__________________________________________________________________________________________________
3b_identity_block_bn2 (BatchNor (None, 28, 28, 128)  512         3b_identity_block_conv2[0][0]    
__________________________________________________________________________________________________
3b_identity_block_relu2 (Activa (None, 28, 28, 128)  0           3b_identity_block_bn2[0][0]      
__________________________________________________________________________________________________
3b_identity_block_conv3 (Conv2D (None, 28, 28, 512)  66048       3b_identity_block_relu2[0][0]    
__________________________________________________________________________________________________
3b_identity_block_bn3 (BatchNor (None, 28, 28, 512)  2048        3b_identity_block_conv3[0][0]    
__________________________________________________________________________________________________
3b_identity_block_add (Add)     (None, 28, 28, 512)  0           3b_identity_block_bn3[0][0]      3a_conv_block_relu4[0][0]        
__________________________________________________________________________________________________
3b_identity_block_relu4 (Activa (None, 28, 28, 512)  0           3b_identity_block_add[0][0]      
__________________________________________________________________________________________________
3c_identity_block_conv1 (Conv2D (None, 28, 28, 128)  65664       3b_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
3c_identity_block_bn1 (BatchNor (None, 28, 28, 128)  512         3c_identity_block_conv1[0][0]    
__________________________________________________________________________________________________
3c_identity_block_relu1 (Activa (None, 28, 28, 128)  0           3c_identity_block_bn1[0][0]      
__________________________________________________________________________________________________
3c_identity_block_conv2 (Conv2D (None, 28, 28, 128)  147584      3c_identity_block_relu1[0][0]    
__________________________________________________________________________________________________
3c_identity_block_bn2 (BatchNor (None, 28, 28, 128)  512         3c_identity_block_conv2[0][0]    
__________________________________________________________________________________________________
3c_identity_block_relu2 (Activa (None, 28, 28, 128)  0           3c_identity_block_bn2[0][0]      
__________________________________________________________________________________________________
3c_identity_block_conv3 (Conv2D (None, 28, 28, 512)  66048       3c_identity_block_relu2[0][0]    
__________________________________________________________________________________________________
3c_identity_block_bn3 (BatchNor (None, 28, 28, 512)  2048        3c_identity_block_conv3[0][0]    
__________________________________________________________________________________________________
3c_identity_block_add (Add)     (None, 28, 28, 512)  0           3c_identity_block_bn3[0][0]      3b_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
3c_identity_block_relu4 (Activa (None, 28, 28, 512)  0           3c_identity_block_add[0][0]      
__________________________________________________________________________________________________
3d_identity_block_conv1 (Conv2D (None, 28, 28, 128)  65664       3c_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
3d_identity_block_bn1 (BatchNor (None, 28, 28, 128)  512         3d_identity_block_conv1[0][0]    
__________________________________________________________________________________________________
3d_identity_block_relu1 (Activa (None, 28, 28, 128)  0           3d_identity_block_bn1[0][0]      
__________________________________________________________________________________________________
3d_identity_block_conv2 (Conv2D (None, 28, 28, 128)  147584      3d_identity_block_relu1[0][0]    
__________________________________________________________________________________________________
3d_identity_block_bn2 (BatchNor (None, 28, 28, 128)  512         3d_identity_block_conv2[0][0]    
__________________________________________________________________________________________________
3d_identity_block_relu2 (Activa (None, 28, 28, 128)  0           3d_identity_block_bn2[0][0]      
__________________________________________________________________________________________________
3d_identity_block_conv3 (Conv2D (None, 28, 28, 512)  66048       3d_identity_block_relu2[0][0]    
__________________________________________________________________________________________________
3d_identity_block_bn3 (BatchNor (None, 28, 28, 512)  2048        3d_identity_block_conv3[0][0]    
__________________________________________________________________________________________________
3d_identity_block_add (Add)     (None, 28, 28, 512)  0           3d_identity_block_bn3[0][0]      3c_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
3d_identity_block_relu4 (Activa (None, 28, 28, 512)  0           3d_identity_block_add[0][0]      
__________________________________________________________________________________________________
4a_conv_block_conv1 (Conv2D)    (None, 14, 14, 256)  131328      3d_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
4a_conv_block_bn1 (BatchNormali (None, 14, 14, 256)  1024        4a_conv_block_conv1[0][0]        
__________________________________________________________________________________________________
4a_conv_block_relu1 (Activation (None, 14, 14, 256)  0           4a_conv_block_bn1[0][0]          
__________________________________________________________________________________________________
4a_conv_block_conv2 (Conv2D)    (None, 14, 14, 256)  590080      4a_conv_block_relu1[0][0]        
__________________________________________________________________________________________________
4a_conv_block_bn2 (BatchNormali (None, 14, 14, 256)  1024        4a_conv_block_conv2[0][0]        
__________________________________________________________________________________________________
4a_conv_block_relu2 (Activation (None, 14, 14, 256)  0           4a_conv_block_bn2[0][0]          
__________________________________________________________________________________________________
4a_conv_block_conv3 (Conv2D)    (None, 14, 14, 1024) 263168      4a_conv_block_relu2[0][0]        
__________________________________________________________________________________________________
4a_conv_block_res_conv (Conv2D) (None, 14, 14, 1024) 525312      3d_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
4a_conv_block_bn3 (BatchNormali (None, 14, 14, 1024) 4096        4a_conv_block_conv3[0][0]        
__________________________________________________________________________________________________
4a_conv_block_res_bn (BatchNorm (None, 14, 14, 1024) 4096        4a_conv_block_res_conv[0][0]     
__________________________________________________________________________________________________
4a_conv_block_add (Add)         (None, 14, 14, 1024) 0           4a_conv_block_bn3[0][0]          4a_conv_block_res_bn[0][0]       
__________________________________________________________________________________________________
4a_conv_block_relu4 (Activation (None, 14, 14, 1024) 0           4a_conv_block_add[0][0]          
__________________________________________________________________________________________________
4b_identity_block_conv1 (Conv2D (None, 14, 14, 256)  262400      4a_conv_block_relu4[0][0]        
__________________________________________________________________________________________________
4b_identity_block_bn1 (BatchNor (None, 14, 14, 256)  1024        4b_identity_block_conv1[0][0]    
__________________________________________________________________________________________________
4b_identity_block_relu1 (Activa (None, 14, 14, 256)  0           4b_identity_block_bn1[0][0]      
__________________________________________________________________________________________________
4b_identity_block_conv2 (Conv2D (None, 14, 14, 256)  590080      4b_identity_block_relu1[0][0]    
__________________________________________________________________________________________________
4b_identity_block_bn2 (BatchNor (None, 14, 14, 256)  1024        4b_identity_block_conv2[0][0]    
__________________________________________________________________________________________________
4b_identity_block_relu2 (Activa (None, 14, 14, 256)  0           4b_identity_block_bn2[0][0]      
__________________________________________________________________________________________________
4b_identity_block_conv3 (Conv2D (None, 14, 14, 1024) 263168      4b_identity_block_relu2[0][0]    
__________________________________________________________________________________________________
4b_identity_block_bn3 (BatchNor (None, 14, 14, 1024) 4096        4b_identity_block_conv3[0][0]    
__________________________________________________________________________________________________
4b_identity_block_add (Add)     (None, 14, 14, 1024) 0           4b_identity_block_bn3[0][0]      4a_conv_block_relu4[0][0]        
__________________________________________________________________________________________________
4b_identity_block_relu4 (Activa (None, 14, 14, 1024) 0           4b_identity_block_add[0][0]      
__________________________________________________________________________________________________
4c_identity_block_conv1 (Conv2D (None, 14, 14, 256)  262400      4b_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
4c_identity_block_bn1 (BatchNor (None, 14, 14, 256)  1024        4c_identity_block_conv1[0][0]    
__________________________________________________________________________________________________
4c_identity_block_relu1 (Activa (None, 14, 14, 256)  0           4c_identity_block_bn1[0][0]      
__________________________________________________________________________________________________
4c_identity_block_conv2 (Conv2D (None, 14, 14, 256)  590080      4c_identity_block_relu1[0][0]    
__________________________________________________________________________________________________
4c_identity_block_bn2 (BatchNor (None, 14, 14, 256)  1024        4c_identity_block_conv2[0][0]    
__________________________________________________________________________________________________
4c_identity_block_relu2 (Activa (None, 14, 14, 256)  0           4c_identity_block_bn2[0][0]      
__________________________________________________________________________________________________
4c_identity_block_conv3 (Conv2D (None, 14, 14, 1024) 263168      4c_identity_block_relu2[0][0]    
__________________________________________________________________________________________________
4c_identity_block_bn3 (BatchNor (None, 14, 14, 1024) 4096        4c_identity_block_conv3[0][0]    
__________________________________________________________________________________________________
4c_identity_block_add (Add)     (None, 14, 14, 1024) 0           4c_identity_block_bn3[0][0]      4b_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
4c_identity_block_relu4 (Activa (None, 14, 14, 1024) 0           4c_identity_block_add[0][0]      
__________________________________________________________________________________________________
4d_identity_block_conv1 (Conv2D (None, 14, 14, 256)  262400      4c_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
4d_identity_block_bn1 (BatchNor (None, 14, 14, 256)  1024        4d_identity_block_conv1[0][0]    
__________________________________________________________________________________________________
4d_identity_block_relu1 (Activa (None, 14, 14, 256)  0           4d_identity_block_bn1[0][0]      
__________________________________________________________________________________________________
4d_identity_block_conv2 (Conv2D (None, 14, 14, 256)  590080      4d_identity_block_relu1[0][0]    
__________________________________________________________________________________________________
4d_identity_block_bn2 (BatchNor (None, 14, 14, 256)  1024        4d_identity_block_conv2[0][0]    
__________________________________________________________________________________________________
4d_identity_block_relu2 (Activa (None, 14, 14, 256)  0           4d_identity_block_bn2[0][0]      
__________________________________________________________________________________________________
4d_identity_block_conv3 (Conv2D (None, 14, 14, 1024) 263168      4d_identity_block_relu2[0][0]    
__________________________________________________________________________________________________
4d_identity_block_bn3 (BatchNor (None, 14, 14, 1024) 4096        4d_identity_block_conv3[0][0]    
__________________________________________________________________________________________________
4d_identity_block_add (Add)     (None, 14, 14, 1024) 0           4d_identity_block_bn3[0][0]      4c_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
4d_identity_block_relu4 (Activa (None, 14, 14, 1024) 0           4d_identity_block_add[0][0]      
__________________________________________________________________________________________________
4e_identity_block_conv1 (Conv2D (None, 14, 14, 256)  262400      4d_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
4e_identity_block_bn1 (BatchNor (None, 14, 14, 256)  1024        4e_identity_block_conv1[0][0]    
__________________________________________________________________________________________________
4e_identity_block_relu1 (Activa (None, 14, 14, 256)  0           4e_identity_block_bn1[0][0]      
__________________________________________________________________________________________________
4e_identity_block_conv2 (Conv2D (None, 14, 14, 256)  590080      4e_identity_block_relu1[0][0]    
__________________________________________________________________________________________________
4e_identity_block_bn2 (BatchNor (None, 14, 14, 256)  1024        4e_identity_block_conv2[0][0]    
__________________________________________________________________________________________________
4e_identity_block_relu2 (Activa (None, 14, 14, 256)  0           4e_identity_block_bn2[0][0]      
__________________________________________________________________________________________________
4e_identity_block_conv3 (Conv2D (None, 14, 14, 1024) 263168      4e_identity_block_relu2[0][0]    
__________________________________________________________________________________________________
4e_identity_block_bn3 (BatchNor (None, 14, 14, 1024) 4096        4e_identity_block_conv3[0][0]    
__________________________________________________________________________________________________
4e_identity_block_add (Add)     (None, 14, 14, 1024) 0           4e_identity_block_bn3[0][0]      4d_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
4e_identity_block_relu4 (Activa (None, 14, 14, 1024) 0           4e_identity_block_add[0][0]      
__________________________________________________________________________________________________
4f_identity_block_conv1 (Conv2D (None, 14, 14, 256)  262400      4e_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
4f_identity_block_bn1 (BatchNor (None, 14, 14, 256)  1024        4f_identity_block_conv1[0][0]    
__________________________________________________________________________________________________
4f_identity_block_relu1 (Activa (None, 14, 14, 256)  0           4f_identity_block_bn1[0][0]      
__________________________________________________________________________________________________
4f_identity_block_conv2 (Conv2D (None, 14, 14, 256)  590080      4f_identity_block_relu1[0][0]    
__________________________________________________________________________________________________
4f_identity_block_bn2 (BatchNor (None, 14, 14, 256)  1024        4f_identity_block_conv2[0][0]    
__________________________________________________________________________________________________
4f_identity_block_relu2 (Activa (None, 14, 14, 256)  0           4f_identity_block_bn2[0][0]      
__________________________________________________________________________________________________
4f_identity_block_conv3 (Conv2D (None, 14, 14, 1024) 263168      4f_identity_block_relu2[0][0]    
__________________________________________________________________________________________________
4f_identity_block_bn3 (BatchNor (None, 14, 14, 1024) 4096        4f_identity_block_conv3[0][0]    
__________________________________________________________________________________________________
4f_identity_block_add (Add)     (None, 14, 14, 1024) 0           4f_identity_block_bn3[0][0]      4e_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
4f_identity_block_relu4 (Activa (None, 14, 14, 1024) 0           4f_identity_block_add[0][0]      
__________________________________________________________________________________________________
5a_conv_block_conv1 (Conv2D)    (None, 7, 7, 512)    524800      4f_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
5a_conv_block_bn1 (BatchNormali (None, 7, 7, 512)    2048        5a_conv_block_conv1[0][0]        
__________________________________________________________________________________________________
5a_conv_block_relu1 (Activation (None, 7, 7, 512)    0           5a_conv_block_bn1[0][0]          
__________________________________________________________________________________________________
5a_conv_block_conv2 (Conv2D)    (None, 7, 7, 512)    2359808     5a_conv_block_relu1[0][0]        
__________________________________________________________________________________________________
5a_conv_block_bn2 (BatchNormali (None, 7, 7, 512)    2048        5a_conv_block_conv2[0][0]        
__________________________________________________________________________________________________
5a_conv_block_relu2 (Activation (None, 7, 7, 512)    0           5a_conv_block_bn2[0][0]          
__________________________________________________________________________________________________
5a_conv_block_conv3 (Conv2D)    (None, 7, 7, 2048)   1050624     5a_conv_block_relu2[0][0]        
__________________________________________________________________________________________________
5a_conv_block_res_conv (Conv2D) (None, 7, 7, 2048)   2099200     4f_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
5a_conv_block_bn3 (BatchNormali (None, 7, 7, 2048)   8192        5a_conv_block_conv3[0][0]        
__________________________________________________________________________________________________
5a_conv_block_res_bn (BatchNorm (None, 7, 7, 2048)   8192        5a_conv_block_res_conv[0][0]     
__________________________________________________________________________________________________
5a_conv_block_add (Add)         (None, 7, 7, 2048)   0           5a_conv_block_bn3[0][0]          5a_conv_block_res_bn[0][0]       
__________________________________________________________________________________________________
5a_conv_block_relu4 (Activation (None, 7, 7, 2048)   0           5a_conv_block_add[0][0]          
__________________________________________________________________________________________________
5b_identity_block_conv1 (Conv2D (None, 7, 7, 512)    1049088     5a_conv_block_relu4[0][0]        
__________________________________________________________________________________________________
5b_identity_block_bn1 (BatchNor (None, 7, 7, 512)    2048        5b_identity_block_conv1[0][0]    
__________________________________________________________________________________________________
5b_identity_block_relu1 (Activa (None, 7, 7, 512)    0           5b_identity_block_bn1[0][0]      
__________________________________________________________________________________________________
5b_identity_block_conv2 (Conv2D (None, 7, 7, 512)    2359808     5b_identity_block_relu1[0][0]    
__________________________________________________________________________________________________
5b_identity_block_bn2 (BatchNor (None, 7, 7, 512)    2048        5b_identity_block_conv2[0][0]    
__________________________________________________________________________________________________
5b_identity_block_relu2 (Activa (None, 7, 7, 512)    0           5b_identity_block_bn2[0][0]      
__________________________________________________________________________________________________
5b_identity_block_conv3 (Conv2D (None, 7, 7, 2048)   1050624     5b_identity_block_relu2[0][0]    
__________________________________________________________________________________________________
5b_identity_block_bn3 (BatchNor (None, 7, 7, 2048)   8192        5b_identity_block_conv3[0][0]    
__________________________________________________________________________________________________
5b_identity_block_add (Add)     (None, 7, 7, 2048)   0           5b_identity_block_bn3[0][0]      5a_conv_block_relu4[0][0]        
__________________________________________________________________________________________________
5b_identity_block_relu4 (Activa (None, 7, 7, 2048)   0           5b_identity_block_add[0][0]      
__________________________________________________________________________________________________
5c_identity_block_conv1 (Conv2D (None, 7, 7, 512)    1049088     5b_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
5c_identity_block_bn1 (BatchNor (None, 7, 7, 512)    2048        5c_identity_block_conv1[0][0]    
__________________________________________________________________________________________________
5c_identity_block_relu1 (Activa (None, 7, 7, 512)    0           5c_identity_block_bn1[0][0]      
__________________________________________________________________________________________________
5c_identity_block_conv2 (Conv2D (None, 7, 7, 512)    2359808     5c_identity_block_relu1[0][0]    
__________________________________________________________________________________________________
5c_identity_block_bn2 (BatchNor (None, 7, 7, 512)    2048        5c_identity_block_conv2[0][0]    
__________________________________________________________________________________________________
5c_identity_block_relu2 (Activa (None, 7, 7, 512)    0           5c_identity_block_bn2[0][0]      
__________________________________________________________________________________________________
5c_identity_block_conv3 (Conv2D (None, 7, 7, 2048)   1050624     5c_identity_block_relu2[0][0]    
__________________________________________________________________________________________________
5c_identity_block_bn3 (BatchNor (None, 7, 7, 2048)   8192        5c_identity_block_conv3[0][0]    
__________________________________________________________________________________________________
5c_identity_block_add (Add)     (None, 7, 7, 2048)   0           5c_identity_block_bn3[0][0]      5b_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
5c_identity_block_relu4 (Activa (None, 7, 7, 2048)   0           5c_identity_block_add[0][0]      
__________________________________________________________________________________________________
avg_pool (AveragePooling2D)     (None, 1, 1, 2048)   0           5c_identity_block_relu4[0][0]    
__________________________________________________________________________________________________
flatten (Flatten)               (None, 2048)         0           avg_pool[0][0]                   
__________________________________________________________________________________________________
fc1000 (Dense)                  (None, 1000)         2049000     flatten[0][0]                    
==================================================================================================
Total params: 25,636,712
Trainable params: 25,583,592
Non-trainable params: 53,120
__________________________________________________________________________________________________

五、编译

在准备对模型进行训练之前，还需要再对其进行一些设置。以下内容是在模型的编译步骤中添加的：

• 损失函数（loss）：用于衡量模型在训练期间的准确率。
• 优化器（optimizer）：决定模型如何根据其看到的数据和自身的损失函数进行更新。
• 指标（metrics）：用于监控训练和测试步骤。以下示例使用了准确率，即被正确分类的图像的比率。

---

# 设置优化器，我这里改变了学习率。
opt = tf.keras.optimizers.Adam(learning_rate=1e-7)model.compile(optimizer="adam",loss='sparse_categorical_crossentropy',metrics=['accuracy'])

六、训练模型

epochs = 10history = model.fit(train_ds,validation_data=val_ds,epochs=epochs
)

Epoch 1/10
57/57 [==============================] - 12s 87ms/step - loss: 2.4394 - accuracy: 0.6620 - val_loss: 484.4415 - val_accuracy: 0.1858
Epoch 2/10
57/57 [==============================] - 3s 55ms/step - loss: 0.3268 - accuracy: 0.8904 - val_loss: 5.5874 - val_accuracy: 0.4513
Epoch 3/10
57/57 [==============================] - 3s 52ms/step - loss: 0.1593 - accuracy: 0.9558 - val_loss: 2.0023 - val_accuracy: 0.6726
Epoch 4/10
57/57 [==============================] - 3s 52ms/step - loss: 0.0546 - accuracy: 0.9869 - val_loss: 1.4410 - val_accuracy: 0.7788
Epoch 5/10
57/57 [==============================] - 3s 51ms/step - loss: 0.1656 - accuracy: 0.9567 - val_loss: 2.1653 - val_accuracy: 0.5487
Epoch 6/10
57/57 [==============================] - 3s 51ms/step - loss: 0.2403 - accuracy: 0.9221 - val_loss: 1.0641 - val_accuracy: 0.7257
Epoch 7/10
57/57 [==============================] - 3s 52ms/step - loss: 0.1774 - accuracy: 0.9377 - val_loss: 0.4922 - val_accuracy: 0.8673
Epoch 8/10
57/57 [==============================] - 3s 51ms/step - loss: 0.0733 - accuracy: 0.9760 - val_loss: 0.2036 - val_accuracy: 0.9381
Epoch 9/10
57/57 [==============================] - 3s 51ms/step - loss: 0.0185 - accuracy: 0.9992 - val_loss: 0.1091 - val_accuracy: 0.9735
Epoch 10/10
57/57 [==============================] - 3s 51ms/step - loss: 0.0086 - accuracy: 1.0000 - val_loss: 0.1246 - val_accuracy: 0.9735

六、模型评估

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()

八、保存and加载模型

# 保存模型
model.save('model/my_model.h5')
# 加载模型
new_model = keras.models.load_model('model/my_model.h5')

九、预测

# 采用加载的模型（new_model）来看预测结果plt.figure(figsize=(10, 5))  # 图形的宽为10高为5for images, labels in val_ds.take(1):for i in range(8):ax = plt.subplot(2, 4, i + 1)  # 显示图片plt.imshow(images[i])# 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 = new_model.predict(img_array)plt.title(class_names[np.argmax(predictions)])plt.axis("off")

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