深度学习笔记11-优化器对比实验(Tensorflow)

• 🍨 本文为🔗365天深度学习训练营中的学习记录博客
• 🍖 原作者：K同学啊

目录

一、导入数据并检查

二、配置数据集

三、数据可视化

四、构建模型

五、训练模型

六、模型对比评估

七、总结

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一、导入数据并检查

import pathlib,PIL
import tensorflow as tf
from tensorflow import keras
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data_dir    = pathlib.Path("./T6")
image_count = len(list(data_dir.glob('*/*')))

batch_size = 16
img_height = 336
img_width  = 336
"""
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)
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)

class_names = train_ds.class_names
print(class_names)

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

二、配置数据集

AUTOTUNE = tf.data.AUTOTUNE
#归一化处理
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)
)

三、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")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()

四、构建模型

from tensorflow.keras.layers import Dropout,Dense,BatchNormalization
from tensorflow.keras.models import Modeldef create_model(optimizer='adam'):# 加载预训练模型vgg16_base_model = tf.keras.applications.vgg16.VGG16(weights='imagenet',include_top=False,#不包含顶层的全连接层input_shape=(img_width, img_height, 3),pooling='avg')#平均池化层替代顶层的全连接层for layer in vgg16_base_model.layers:layer.trainable = False  #将 trainable属性设置为 False 意味着在训练过程中，这些层的权重不会更新X = vgg16_base_model.outputX = Dense(170, activation='relu')(X)X = BatchNormalization()(X)X = Dropout(0.5)(X)output = Dense(len(class_names), activation='softmax')(X)#神经元数量等于类别数vgg16_model = Model(inputs=vgg16_base_model.input, outputs=output)vgg16_model.compile(optimizer=optimizer,loss='sparse_categorical_crossentropy',metrics=['accuracy'])return vgg16_modelmodel1 = create_model(optimizer=tf.keras.optimizers.Adam())
model2 = create_model(optimizer=tf.keras.optimizers.SGD())#随机梯度下降（SGD）优化器的
model2.summary()

五、训练模型

NO_EPOCHS = 20history_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)

六、模型对比评估

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()

可以看出，在这个实例中，Adam优化器的效果优于SGD优化器

七、总结

      通过本次实验，学会了比较不同优化器（Adam和SGD）在训练过程中的性能表现，可视化训练过程的损失曲线和准确率等指标。这是一项非常重要的技能，在研究论文中，可以通过这些优化方法可以提高工作量。