深度学习笔记21_优化器对比实验

L10732034822024-11-02 13:38

一、我的环境

1.语言环境：Python 3.9

2.编译器：Pycharm

3.深度学习环境：TensorFlow 2.10.0

二、GPU设置

若使用的是cpu则可忽略

复制代码 
import tensorflow as tf
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")

from tensorflow          import keras
import matplotlib.pyplot as plt
import pandas            as pd
import numpy             as np
import warnings,os,PIL,pathlib

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

三**、导入数据**

python 复制代码 
data_dir    = "./data"
data_dir    = pathlib.Path(data_dir)
image_count = len(list(data_dir.glob('*/*')))
print("图片总数为：",image_count)
python 复制代码 
batch_size = 16
img_height = 336
img_width  = 336
python 复制代码 
"""
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=12,
    image_size=(img_height, img_width),
    batch_size=batch_size)
python 复制代码 
"""
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=12,
    image_size=(img_height, img_width),
    batch_size=batch_size)
python 复制代码 
class_names = train_ds.class_names
print(class_names)

运行结果：

python 复制代码 
['Angelina Jolie', 'Brad Pitt', 'Denzel Washington', 'Hugh Jackman', 'Jennifer Lawrence', 'Johnny Depp', 'Kate Winslet', 'Leonardo DiCaprio', 'Megan Fox', 'Natalie Portman', 'Nicole Kidman', 'Robert Downey Jr', 'Sandra Bullock', 'Scarlett Johansson', 'Tom Cruise', 'Tom Hanks', 'Will Smith']

四**、检查数据**

python 复制代码 
for image_batch, labels_batch in train_ds:
    print(image_batch.shape)
    print(labels_batch.shape)
    break
#(16, 336, 336, 3)
#(16,)

五**、配置数据集**

python 复制代码 
AUTOTUNE = tf.data.AUTOTUNE

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

train_ds = (
    train_ds.cache()
    .shuffle(1000)
    .map(train_preprocessing)    # 这里可以设置预处理函数
#     .batch(batch_size)           # 在image_dataset_from_directory处已经设置了batch_size
    .prefetch(buffer_size=AUTOTUNE)
)

val_ds = (
    val_ds.cache()
    .shuffle(1000)
    .map(train_preprocessing)    # 这里可以设置预处理函数
#     .batch(batch_size)         # 在image_dataset_from_directory处已经设置了batch_size
    .prefetch(buffer_size=AUTOTUNE)
)

六、数据可视化

python 复制代码 
plt.figure(figsize=(10, 8))  # 图形的宽为10高为5
plt.suptitle("数据展示")

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()

运行结果：

七、构建模型

python 复制代码 
from tensorflow.keras.layers import Dropout,Dense,BatchNormalization
from tensorflow.keras.models import Model

def create_model(optimizer='adam'):
    # 加载预训练模型
    vgg16_base_model = tf.keras.applications.vgg16.VGG16(weights='imagenet',
                                                                include_top=False,
                                                                input_shape=(img_width, img_height, 3),
                                                                pooling='avg')
    for layer in vgg16_base_model.layers:
        layer.trainable = False

    X = vgg16_base_model.output
    
    X = Dense(170, activation='relu')(X)
    X = BatchNormalization()(X)
    X = Dropout(0.5)(X)

    output = Dense(len(class_names), activation='softmax')(X)
    vgg16_model = Model(inputs=vgg16_base_model.input, outputs=output)

    vgg16_model.compile(optimizer=optimizer,
                        loss='sparse_categorical_crossentropy',
                        metrics=['accuracy'])
    return vgg16_model

model1 = create_model(optimizer=tf.keras.optimizers.Adam())
model2 = create_model(optimizer=tf.keras.optimizers.SGD())
model2.summary()

运行结果：

python 复制代码 
Model: "model_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #
=================================================================
 input_2 (InputLayer)        [(None, 336, 336, 3)]     0

 block1_conv1 (Conv2D)       (None, 336, 336, 64)      1792

 block1_conv2 (Conv2D)       (None, 336, 336, 64)      36928

 block1_pool (MaxPooling2D)  (None, 168, 168, 64)      0

 block2_conv1 (Conv2D)       (None, 168, 168, 128)     73856

 block2_conv2 (Conv2D)       (None, 168, 168, 128)     147584

 block2_pool (MaxPooling2D)  (None, 84, 84, 128)       0

 block3_conv1 (Conv2D)       (None, 84, 84, 256)       295168

 block3_conv2 (Conv2D)       (None, 84, 84, 256)       590080

 block3_conv3 (Conv2D)       (None, 84, 84, 256)       590080

 block3_pool (MaxPooling2D)  (None, 42, 42, 256)       0

 block4_conv1 (Conv2D)       (None, 42, 42, 512)       1180160

 block4_conv2 (Conv2D)       (None, 42, 42, 512)       2359808

 block4_conv3 (Conv2D)       (None, 42, 42, 512)       2359808

 block4_pool (MaxPooling2D)  (None, 21, 21, 512)       0

 block5_conv1 (Conv2D)       (None, 21, 21, 512)       2359808

 block5_conv2 (Conv2D)       (None, 21, 21, 512)       2359808

 block5_conv3 (Conv2D)       (None, 21, 21, 512)       2359808

 block5_pool (MaxPooling2D)  (None, 10, 10, 512)       0

 global_average_pooling2d_1   (None, 512)              0
 (GlobalAveragePooling2D)

 dense_2 (Dense)             (None, 170)               87210

 batch_normalization_1 (Batc  (None, 170)              680
 hNormalization)

 dropout_1 (Dropout)         (None, 170)               0

 dense_3 (Dense)             (None, 17)                2907

=================================================================
Total params: 14,805,485
Trainable params: 90,457
Non-trainable params: 14,715,028
_________________________________________________________________

八、训练模型

python 复制代码 
NO_EPOCHS = 50

history_model1  = model1.fit(train_ds, epochs=NO_EPOCHS, verbose=1, validation_data=val_ds)
history_model2  = model2.fit(train_ds, epochs=NO_EPOCHS, verbose=1, validation_data=val_ds)NO_EPOCHS = 50

history_model1  = model1.fit(train_ds, epochs=NO_EPOCHS, verbose=1, validation_data=val_ds)
history_model2  = model2.fit(train_ds, epochs=NO_EPOCHS, verbose=1, validation_data=val_ds)

运行结果：

python 复制代码 
Epoch 1/50
90/90 [==============================] - 14s 140ms/step - loss: 3.1176 - accuracy: 0.1035 - val_loss: 2.7743 - val_accuracy: 0.1972
Epoch 2/50
90/90 [==============================] - 12s 137ms/step - loss: 2.5185 - accuracy: 0.2181 - val_loss: 2.6079 - val_accuracy: 0.2056
Epoch 3/50
90/90 [==============================] - 12s 137ms/step - loss: 2.2428 - accuracy: 0.2757 - val_loss: 2.4016 - val_accuracy: 0.3000
Epoch 4/50
90/90 [==============================] - 12s 137ms/step - loss: 2.0429 - accuracy: 0.3458 - val_loss: 2.2346 - val_accuracy: 0.3083
Epoch 5/50
90/90 [==============================] - 12s 137ms/step - loss: 1.9355 - accuracy: 0.3917 - val_loss: 2.0221 - val_accuracy: 0.3778
Epoch 6/50
90/90 [==============================] - 12s 137ms/step - loss: 1.8070 - accuracy: 0.3965 - val_loss: 1.8872 - val_accuracy: 0.4250
Epoch 7/50
90/90 [==============================] - 12s 137ms/step - loss: 1.7000 - accuracy: 0.4500 - val_loss: 1.8404 - val_accuracy: 0.4167
Epoch 8/50
90/90 [==============================] - 12s 137ms/step - loss: 1.6306 - accuracy: 0.4757 - val_loss: 1.8395 - val_accuracy: 0.3972
Epoch 9/50
90/90 [==============================] - 12s 137ms/step - loss: 1.5473 - accuracy: 0.4979 - val_loss: 1.6730 - val_accuracy: 0.4806
Epoch 10/50
90/90 [==============================] - 12s 137ms/step - loss: 1.4987 - accuracy: 0.5299 - val_loss: 1.6955 - val_accuracy: 0.4500
Epoch 11/50
90/90 [==============================] - 12s 138ms/step - loss: 1.4504 - accuracy: 0.5507 - val_loss: 1.6891 - val_accuracy: 0.4806
Epoch 12/50
90/90 [==============================] - 12s 137ms/step - loss: 1.3725 - accuracy: 0.5722 - val_loss: 1.7054 - val_accuracy: 0.4500
Epoch 13/50
90/90 [==============================] - 12s 137ms/step - loss: 1.3657 - accuracy: 0.5528 - val_loss: 1.5714 - val_accuracy: 0.5028
Epoch 14/50
90/90 [==============================] - 12s 137ms/step - loss: 1.3056 - accuracy: 0.5972 - val_loss: 1.5478 - val_accuracy: 0.5139
Epoch 15/50
90/90 [==============================] - 12s 138ms/step - loss: 1.2539 - accuracy: 0.6028 - val_loss: 1.5626 - val_accuracy: 0.5000
Epoch 16/50
90/90 [==============================] - 12s 138ms/step - loss: 1.2368 - accuracy: 0.6153 - val_loss: 1.5329 - val_accuracy: 0.5028
Epoch 17/50
90/90 [==============================] - 12s 136ms/step - loss: 1.1918 - accuracy: 0.6132 - val_loss: 1.5597 - val_accuracy: 0.5000
Epoch 18/50
90/90 [==============================] - 12s 137ms/step - loss: 1.1848 - accuracy: 0.6208 - val_loss: 1.4749 - val_accuracy: 0.4972
Epoch 19/50
90/90 [==============================] - 12s 135ms/step - loss: 1.1555 - accuracy: 0.6465 - val_loss: 1.7383 - val_accuracy: 0.4167
Epoch 20/50
90/90 [==============================] - 12s 137ms/step - loss: 1.1110 - accuracy: 0.6507 - val_loss: 1.6035 - val_accuracy: 0.5000
Epoch 21/50
90/90 [==============================] - 12s 137ms/step - loss: 1.0790 - accuracy: 0.6743 - val_loss: 1.4571 - val_accuracy: 0.5333
Epoch 22/50
90/90 [==============================] - 12s 132ms/step - loss: 1.0522 - accuracy: 0.6687 - val_loss: 1.3678 - val_accuracy: 0.5750
Epoch 23/50
90/90 [==============================] - 12s 136ms/step - loss: 1.0175 - accuracy: 0.6861 - val_loss: 1.4401 - val_accuracy: 0.5056
Epoch 24/50
90/90 [==============================] - 12s 138ms/step - loss: 0.9935 - accuracy: 0.6944 - val_loss: 1.6090 - val_accuracy: 0.5056
Epoch 25/50
90/90 [==============================] - 12s 138ms/step - loss: 0.9755 - accuracy: 0.6944 - val_loss: 1.5200 - val_accuracy: 0.5000
Epoch 26/50
90/90 [==============================] - 12s 137ms/step - loss: 0.9349 - accuracy: 0.7243 - val_loss: 1.5443 - val_accuracy: 0.5000
Epoch 27/50
90/90 [==============================] - 12s 137ms/step - loss: 0.9127 - accuracy: 0.7139 - val_loss: 1.3795 - val_accuracy: 0.5639
Epoch 28/50
90/90 [==============================] - 12s 137ms/step - loss: 0.8983 - accuracy: 0.7139 - val_loss: 1.3948 - val_accuracy: 0.5778
Epoch 29/50
90/90 [==============================] - 12s 138ms/step - loss: 0.8580 - accuracy: 0.7271 - val_loss: 1.4376 - val_accuracy: 0.5389
Epoch 30/50
90/90 [==============================] - 12s 138ms/step - loss: 0.8537 - accuracy: 0.7312 - val_loss: 1.3611 - val_accuracy: 0.5833
Epoch 31/50
90/90 [==============================] - 12s 138ms/step - loss: 0.8291 - accuracy: 0.7306 - val_loss: 1.4406 - val_accuracy: 0.5417
Epoch 32/50
90/90 [==============================] - 12s 138ms/step - loss: 0.8300 - accuracy: 0.7340 - val_loss: 1.3366 - val_accuracy: 0.5833
Epoch 33/50
90/90 [==============================] - 13s 139ms/step - loss: 0.7823 - accuracy: 0.7736 - val_loss: 1.3799 - val_accuracy: 0.5556
Epoch 34/50
90/90 [==============================] - 12s 139ms/step - loss: 0.7909 - accuracy: 0.7646 - val_loss: 1.3132 - val_accuracy: 0.5917
Epoch 35/50
90/90 [==============================] - 12s 139ms/step - loss: 0.7966 - accuracy: 0.7583 - val_loss: 1.4601 - val_accuracy: 0.5361
Epoch 36/50
90/90 [==============================] - 12s 138ms/step - loss: 0.7554 - accuracy: 0.7514 - val_loss: 1.3837 - val_accuracy: 0.5528
Epoch 37/50
90/90 [==============================] - 12s 138ms/step - loss: 0.7400 - accuracy: 0.7715 - val_loss: 1.4580 - val_accuracy: 0.5667
Epoch 38/50
90/90 [==============================] - 12s 138ms/step - loss: 0.6991 - accuracy: 0.7889 - val_loss: 1.5139 - val_accuracy: 0.5083
Epoch 39/50
90/90 [==============================] - 12s 132ms/step - loss: 0.6953 - accuracy: 0.7875 - val_loss: 1.5710 - val_accuracy: 0.5083
Epoch 40/50
90/90 [==============================] - 12s 138ms/step - loss: 0.6927 - accuracy: 0.7868 - val_loss: 1.4386 - val_accuracy: 0.5194
Epoch 41/50
90/90 [==============================] - 12s 139ms/step - loss: 0.6569 - accuracy: 0.7910 - val_loss: 1.3502 - val_accuracy: 0.5583
Epoch 42/50
90/90 [==============================] - 13s 139ms/step - loss: 0.6406 - accuracy: 0.8076 - val_loss: 1.4855 - val_accuracy: 0.5639
Epoch 43/50
90/90 [==============================] - 12s 137ms/step - loss: 0.6585 - accuracy: 0.7979 - val_loss: 1.6144 - val_accuracy: 0.5194
Epoch 44/50
90/90 [==============================] - 12s 137ms/step - loss: 0.6393 - accuracy: 0.7944 - val_loss: 1.4049 - val_accuracy: 0.6056
Epoch 45/50
90/90 [==============================] - 12s 137ms/step - loss: 0.6111 - accuracy: 0.8146 - val_loss: 1.4132 - val_accuracy: 0.5611
Epoch 46/50
90/90 [==============================] - 12s 137ms/step - loss: 0.6233 - accuracy: 0.7993 - val_loss: 1.5067 - val_accuracy: 0.5417
Epoch 47/50
90/90 [==============================] - 12s 137ms/step - loss: 0.5802 - accuracy: 0.8208 - val_loss: 1.5511 - val_accuracy: 0.5250
Epoch 48/50
90/90 [==============================] - 12s 137ms/step - loss: 0.5785 - accuracy: 0.8104 - val_loss: 1.3737 - val_accuracy: 0.5583
Epoch 49/50
90/90 [==============================] - 12s 137ms/step - loss: 0.5717 - accuracy: 0.8333 - val_loss: 1.3962 - val_accuracy: 0.5833
Epoch 50/50
90/90 [==============================] - 12s 137ms/step - loss: 0.5532 - accuracy: 0.8271 - val_loss: 1.5394 - val_accuracy: 0.5417

九、评估模型

python 复制代码 
from matplotlib.ticker import MultipleLocator
plt.rcParams['savefig.dpi'] = 300 #图片像素
plt.rcParams['figure.dpi']  = 300 #分辨率

acc1     = history_model1.history['accuracy']
acc2     = history_model2.history['accuracy']
val_acc1 = history_model1.history['val_accuracy']
val_acc2 = history_model2.history['val_accuracy']

loss1     = history_model1.history['loss']
loss2     = history_model2.history['loss']
val_loss1 = history_model1.history['val_loss']
val_loss2 = history_model2.history['val_loss']

epochs_range = range(len(acc1))

plt.figure(figsize=(16, 4))
plt.subplot(1, 2, 1)

plt.plot(epochs_range, acc1, label='Training Accuracy-Adam')
plt.plot(epochs_range, acc2, label='Training Accuracy-SGD')
plt.plot(epochs_range, val_acc1, label='Validation Accuracy-Adam')
plt.plot(epochs_range, val_acc2, label='Validation Accuracy-SGD')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
# 设置刻度间隔，x轴每1一个刻度
ax = plt.gca()
ax.xaxis.set_major_locator(MultipleLocator(1))

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss1, label='Training Loss-Adam')
plt.plot(epochs_range, loss2, label='Training Loss-SGD')
plt.plot(epochs_range, val_loss1, label='Validation Loss-Adam')
plt.plot(epochs_range, val_loss2, label='Validation Loss-SGD')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
   
# 设置刻度间隔，x轴每1一个刻度
ax = plt.gca()
ax.xaxis.set_major_locator(MultipleLocator(1))

plt.show()

运行结果：

python 复制代码 
def test_accuracy_report(model):
    score = model.evaluate(val_ds, verbose=0)
    print('Loss function: %s, accuracy:' % score[0], score[1])
    
test_accuracy_report(model2)

运行结果：

python 复制代码 
Loss function: 1.5393909215927124, accuracy: 0.5416666865348816

十、总结

本周学习使用VGG16模型进行人脸识别，通过改变优化器得到不一样的效果；一般来说，Adam 是最好的选择，可以通过实验改变优化器来寻找最佳。

相关推荐
强盛小灵通专卖员5 分钟前
DL00291-联邦学习以去中心化锂离子电池健康预测模型完整实现
人工智能·机器学习·深度强化学习·核心期刊·导师·小论文·大论文
Hello123网站14 分钟前
多墨智能-AI一键生成工作文档/流程图/思维导图
人工智能·流程图·ai工具
有Li1 小时前
CLIK-Diffusion：用于牙齿矫正的临床知识感知扩散模型|文献速递-深度学习人工智能医疗图像
人工智能·深度学习·文献·医学生
大唐荣华1 小时前
视觉语言模型（VLA）分类方法体系
人工智能·分类·机器人·具身智能
即兴小索奇1 小时前
AI应用商业化加速落地 2025智能体爆发与端侧创新成增长引擎
人工智能·搜索引擎·ai·商业·ai商业洞察·即兴小索奇
yiqiqukanhaiba1 小时前
STM32学习笔记13-通信协议I2C&MPU6050&I2C软件控制
笔记·stm32·学习
code bean1 小时前
【halcon】Halcon 开发笔记： gray_histo_abs 报错陷阱
笔记
NeilNiu1 小时前
开源AI工具Midscene.js
javascript·人工智能·开源
nju_spy2 小时前
机器学习 - Kaggle项目实践（4）Toxic Comment Classification Challenge 垃圾评论分类问题
人工智能·深度学习·自然语言处理·tf-idf·南京大学·glove词嵌入·双头gru
计算机sci论文精选2 小时前
CVPR 2025 | 具身智能 | HOLODECK：一句话召唤3D世界，智能体的“元宇宙练功房”来了
人工智能·深度学习·机器学习·计算机视觉·机器人·cvpr·具身智能
热门推荐
01UV安装并设置国内源02DeepSeek更新！速览DeepSeek V3.1新特性03Qwen3-Coder 快速上手教程 | Qwen Code + Claude Code04KGG转MP3工具|非KGM文件|解密音频05蜘蛛磁力 搜索引擎大全，如何使用蜘蛛磁力查找磁力链接06【2025.08.06最新版】Android Studio下载、安装及配置记录（自动下载sdk）07阿里开源首个图像生成基础模型——Qwen-Image本地部署教程，超强中文渲染能力刷新SOTA！082025最新国内服务器可用docker源仓库地址大全（2025年8月更新）09NVIDIA显卡驱动、CUDA、cuDNN 和 TensorRT 版本匹配指南10TRAE 规则（Rules）配置指南：个人习惯、团队规范与最佳实践