深度学习100例 -卷积神经网络（ResNet-50）鸟类识别 | 第8天

Posted 2021-06-25 K同学啊

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一、前期工作

本文将采用ResNet-50实现鸟类图片的识别分类

我的环境：

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

往期精彩内容：

来自专栏： 【深度学习100例】

转载请通过左侧联系方式（电脑端可看）、或者站内私信的方式联系我

1. 设置GPU

如果使用的是CPU可以注释掉这部分的代码。

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")

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,models

import pathlib

data_dir = "D:/jupyter notebook/DL-100-days/datasets/bird_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)

图片总数为： 565

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 = 8
img_height = 224
img_width = 224

TensorFlow版本是2.2.0的同学可能会遇到module 'tensorflow.keras.preprocessing' has no attribute 'image_dataset_from_directory'的报错，升级一下TensorFlow就OK了。

"""
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)

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

"""
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)

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 Throated Bushtiti', 'Black skimmer', 'Cockatoo']

2. 可视化数据

plt.figure(figsize=(10, 5))  # 图形的宽为10高为5
plt.suptitle("微信公众号：K同学啊")

for 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")

在这里插入图片描述

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

在这里插入图片描述

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

shuffle() ：打乱数据，关于此函数的详细介绍可以参考：https://zhuanlan.zhihu.com/p/42417456
prefetch() ：预取数据，加速运行，其详细介绍可以参考我前两篇文章，里面都有讲解。
cache() ：将数据集缓存到内存当中，加速运行

AUTOTUNE = tf.data.AUTOTUNE

train_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

Conv Block结构：

在这里插入图片描述

Identity Block结构：

在这里插入图片描述

ResNet-50总体结构：

在这里插入图片描述

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 layers

from keras.layers import Input,Activation,BatchNormalization,Flatten
from keras.layers import Dense,Conv2D,MaxPooling2D,ZeroPadding2D,AveragePooling2D
from keras.models import Model

def identity_block(input_tensor, kernel_size, filters, stage, block):

    filters1, filters2, filters3 = filters

    name_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 x


def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):

    filters1, filters2, filters3 = filters

    res_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 x

def 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 model

model = 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]    

     =============================================================
            此处省略了若干行，此处省略了若干行，此处省略了若干行
     =============================================================
__________________________________________________________________________________________________
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 = 10

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

Epoch 1/10
57/57 [==============================] - 12s 86ms/step - loss: 2.4313 - accuracy: 0.6548 - val_loss: 213.7383 - val_accuracy: 0.3186
Epoch 2/10
57/57 [==============================] - 3s 52ms/step - loss: 0.4293 - accuracy: 0.8557 - val_loss: 9.0470 - val_accuracy: 0.2566
Epoch 3/10
57/57 [==============================] - 3s 52ms/step - loss: 0.2309 - accuracy: 0.9183 - val_loss: 1.4181 - val_accuracy: 0.7080
Epoch 4/10
57/57 [==============================] - 3s 53ms/step - loss: 0.1721 - accuracy: 0.9535 - val_loss: 2.5627 - val_accuracy: 0.6726
Epoch 5/10
57/57 [==============================] - 3s 53ms/step - loss: 0.0795 - accuracy: 0.9701 - val_loss: 0.2747 - val_accuracy: 0.8938
Epoch 6/10
57/57 [==============================] - 3s 52ms/step - loss: 0.0435 - accuracy: 0.9899 - val_loss: 0.1483 - val_accuracy: 0.9381
Epoch 7/10
57/57 [==============================] - 3s 52ms/step - loss: 0.0308 - accuracy: 0.9970 - val_loss: 0.1705 - val_accuracy: 0.9381
Epoch 8/10
57/57 [==============================] - 3s 52ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0674 - val_accuracy: 0.9735
Epoch 9/10
57/57 [==============================] - 3s 52ms/step - loss: 8.2391e-04 - accuracy: 1.0000 - val_loss: 0.0720 - val_accuracy: 0.9以上是关于深度学习100例 -卷积神经网络（ResNet-50）鸟类识别 | 第8天的主要内容，如果未能解决你的问题，请参考以下文章 
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