深度学习—利用TensorFlow2实现狗狗品种品种（DenseNet121实现）

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原创最新推荐文章于 2026-02-23 00:48:05 发布 1.1k 阅读 AI 写同款· GEO 优化 ›

使用深度学习识别狗的品种作者|Nouman 编译|VK 来源|Towards Data Science 在这篇文章中，我将教你建立你自己的网页应用程序，它将接受你的狗的图片，并输出其品种。准确率超过80%！我们将使用深度学习来训练一个模型的数据集的狗图像与他们的品种，以学习的特征来区分每一个品种。数据分析数据集可以从这里下载（https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/dogImages.zip）。在成功加载和浏览数据集后，以下是关于数据的一些介阅读详情

全是代码和效果图，具体注解，回看利用resnet50的那篇博客就行

# 引入相关的库
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import pathlib
import os
import numpy as np
import random
import glob
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pandas as pd
import matplotlib.pyplot as plt

gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
tf.config.experimental.set_virtual_device_configuration(
    gpus[0],
    [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=7000)])
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True

# 获取图片路径
train_root = '/home/hyb/狗狗识别/dogImages/train'
test_root = '/home/hyb/狗狗识别/dogImages/test'
valid_root = '/home/hyb/狗狗识别/dogImages/valid'

# 定义超参数，避免在编译，训练的代码中进行硬编码，方便调整超参数
weigth = 224
heigth = 224
channels = 3
batch_size = 28
num_classes = 133

# 数据增强
train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
    preprocessing_function = tf.keras.applications.densenet.preprocess_input,
    rotation_range= 40,
    width_shift_range= 0.2,
    height_shift_range= 0.2,
    shear_range= 0.2,
    zoom_range= 0.2,
    horizontal_flip= True,
    fill_mode= 'nearest'
)
# 主要是读取到图片目录下的所以狗狗种类和图片，对图片进行预处理
train_generator = train_datagen.flow_from_directory(
    train_root,
    target_size=(weigth,heigth),
    batch_size=batch_size,
    seed = 7,
    shuffle= True,
    class_mode = "categorical"
)


valid_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
    preprocessing_function = tf.keras.applications.densenet.preprocess_input,
)
# 主要是读取到图片目录下的所以狗狗种类和图片，对图片进行预处理
valid_generator = train_datagen.flow_from_directory(
    valid_root,
    target_size=(weigth,heigth),
    batch_size=batch_size,
    seed = 7,
    shuffle= False,
    class_mode = "categorical"
)

train_num = train_generator.samples
valid_num = valid_generator.samples
print("训练集中的图片数量：",train_num)
print("验证集中的图片数量：",valid_num)

test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
    preprocessing_function = tf.keras.applications.densenet.preprocess_input,
)
test_generator = train_datagen.flow_from_directory(
    test_root,
    target_size=(weigth,heigth),
    batch_size=batch_size,
#     seed = 7,
#     shuffle= False,
#     class_mode = "categorical"
)
test_num = test_generator.samples
print("训练集中的图片数量：",test_num)

# 查看训练数据的维度保证和（batch_size,weight,height,channels）设置的超参数是一致的
## tip：如果（28,133）打印出来的维度不是预期的，可能是 '/home/hyb/狗狗识别/dogImages/train'这个路径下缺少了train，没有完全读到狗狗种类文件
for i in range(2):
    x,y = train_generator.next()
    print(x.shape,y.shape)
    print(y)

densenet_fine_tune = tf.keras.models.Sequential()
densenet_fine_tune.add(tf.keras.applications.DenseNet121(include_top = False,pooling = 'avg',weights = 'imagenet'))
# 修改输出神经元对应狗狗种类
densenet_fine_tune.add(tf.keras.layers.Dense(num_classes, activation = 'softmax'))
densenet_fine_tune.layers[0].trainable = False

# 对模型进行编译
densenet_fine_tune.compile(loss="categorical_crossentropy",
                           optimizer="Adam", metrics=['accuracy'])
densenet_fine_tune.summary()

# 设置哨兵，用来存储quan# 设置哨兵，用来存储权重模型，和数据收敛，模型再也学习不到新内容的时候及时收敛
checkpoint_path = "/home/hyb/狗狗识别/dogImages/saved_models/checkpoint_densenet/cp.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)
checkpoint_filepath = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
                                                 verbose= 1,
                                                 save_weights_only= True,
                                                  save_best_only=True)
callback_dog=tf.keras.callbacks.EarlyStopping(patience=5,min_delta=1e-3)
history = densenet_fine_tune.fit(train_generator,
                              steps_per_epoch=train_num // batch_size,
                             epochs=50,
                             validation_data=valid_generator,
                             validation_steps= valid_num // batch_size,
                             callbacks=[checkpoint_filepath,callback_dog])

# 加载权重
densenet_fine_tune.load_weights(checkpoint_path)

# 评估模型
loss,acc = densenet_fine_tune.evaluate(test_generator,verbose=1)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))

在这里插入图片描述

# 存储模型
densenet_fine_tune.save('/home/hyb/狗狗识别/dogImages/saved_models/model_h5/checkpoint_densenet/my_model.h5') 
# 重新创建完全相同的模型，包括其权重和优化程序
new_model = tf.keras.models.load_model('/home/hyb/狗狗识别/dogImages/saved_models/model_h5/checkpoint_densenet/my_model.h5')

# 显示网络结构
new_model.summary()

# 获取狗狗种类，以便后续对狗狗进行识别
files = sorted(os.listdir('./dogImages/test/'))
print(files)
# 狗狗的133个种类
files=['001.Affenpinscher', '002.Afghan_hound', '003.Airedale_terrier', '004.Akita', '005.Alaskan_malamute', '006.American_eskimo_dog', '007.American_foxhound', '008.American_staffordshire_terrier', '009.American_water_spaniel', '010.Anatolian_shepherd_dog', '011.Australian_cattle_dog', '012.Australian_shepherd', '013.Australian_terrier', '014.Basenji', '015.Basset_hound', '016.Beagle', '017.Bearded_collie', '018.Beauceron', '019.Bedlington_terrier', '020.Belgian_malinois', '021.Belgian_sheepdog', '022.Belgian_tervuren', '023.Bernese_mountain_dog', '024.Bichon_frise', '025.Black_and_tan_coonhound', '026.Black_russian_terrier', '027.Bloodhound', '028.Bluetick_coonhound', '029.Border_collie', '030.Border_terrier', '031.Borzoi', '032.Boston_terrier', '033.Bouvier_des_flandres', '034.Boxer', '035.Boykin_spaniel', '036.Briard', '037.Brittany', '038.Brussels_griffon', '039.Bull_terrier', '040.Bulldog', '041.Bullmastiff', '042.Cairn_terrier', '043.Canaan_dog', '044.Cane_corso', '045.Cardigan_welsh_corgi', '046.Cavalier_king_charles_spaniel', '047.Chesapeake_bay_retriever', '048.Chihuahua', '049.Chinese_crested', '050.Chinese_shar-pei', '051.Chow_chow', '052.Clumber_spaniel', '053.Cocker_spaniel', '054.Collie', '055.Curly-coated_retriever', '056.Dachshund', '057.Dalmatian', '058.Dandie_dinmont_terrier', '059.Doberman_pinscher', '060.Dogue_de_bordeaux', '061.English_cocker_spaniel', '062.English_setter', '063.English_springer_spaniel', '064.English_toy_spaniel', '065.Entlebucher_mountain_dog', '066.Field_spaniel', '067.Finnish_spitz', '068.Flat-coated_retriever', '069.French_bulldog', '070.German_pinscher', '071.German_shepherd_dog', '072.German_shorthaired_pointer', '073.German_wirehaired_pointer', '074.Giant_schnauzer', '075.Glen_of_imaal_terrier', '076.Golden_retriever', '077.Gordon_setter', '078.Great_dane', '079.Great_pyrenees', '080.Greater_swiss_mountain_dog', '081.Greyhound', '082.Havanese', '083.Ibizan_hound', '084.Icelandic_sheepdog', '085.Irish_red_and_white_setter', '086.Irish_setter', '087.Irish_terrier', '088.Irish_water_spaniel', '089.Irish_wolfhound', '090.Italian_greyhound', '091.Japanese_chin', '092.Keeshond', '093.Kerry_blue_terrier', '094.Komondor', '095.Kuvasz', '096.Labrador_retriever', '097.Lakeland_terrier', '098.Leonberger', '099.Lhasa_apso', '100.Lowchen', '101.Maltese', '102.Manchester_terrier', '103.Mastiff', '104.Miniature_schnauzer', '105.Neapolitan_mastiff', '106.Newfoundland', '107.Norfolk_terrier', '108.Norwegian_buhund', '109.Norwegian_elkhound', '110.Norwegian_lundehund', '111.Norwich_terrier', '112.Nova_scotia_duck_tolling_retriever', '113.Old_english_sheepdog', '114.Otterhound', '115.Papillon', '116.Parson_russell_terrier', '117.Pekingese', '118.Pembroke_welsh_corgi', '119.Petit_basset_griffon_vendeen', '120.Pharaoh_hound', '121.Plott', '122.Pointer', '123.Pomeranian', '124.Poodle', '125.Portuguese_water_dog', '126.Saint_bernard', '127.Silky_terrier', '128.Smooth_fox_terrier', '129.Tibetan_mastiff', '130.Welsh_springer_spaniel', '131.Wirehaired_pointing_griffon', '132.Xoloitzcuintli', '133.Yorkshire_terrier']

# 对狗狗种类进行预测：我这张是哈士奇，对应的是5号，虽然只有3%，但都比绝大多数的评分要多
from keras.preprocessing.image import load_img, img_to_array
pic_dog = '/home/hyb/下载/6d97155392b885da3b55dd249c337433.jpg'
pic_dog = load_img(pic_dog,target_size=(224,224))
pic_dog = img_to_array(pic_dog)
pic_dog = pic_dog/255
pic_dog = pic_dog.reshape(1,224,224,3)
result = densenet_fine_tune.predict(pic_dog)
# print(result)
count = 0
for i in result[0]:
    percent = '%.3f%%'%(i*100)
    print("{}的概率：{}".format(files[count],percent))
    count += 1

在这里插入图片描述

# 模型学习曲线
def plot_learning_curves(history, label, epcohs, min_value, max_value):
    data = {}
    data[label] = history.history[label]
    data['val_'+label] = history.history['val_'+label]
    pd.DataFrame(data).plot(figsize=(8, 5))
    plt.grid(True)
    plt.axis([0, 20, min_value, max_value])
    plt.show()
    
plot_learning_curves(history, 'accuracy', 20, 0, 1)
plot_learning_curves(history, 'loss', 20, 0, 2)