>- **🍨 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](K同学啊)**
一、前期工作
1. 设置GPU
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")
# 打印显卡信息，确认GPU可用
print(gpus)2.导入数据
import numpy as np
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,pathlib
#隐藏警告
import warnings
warnings.filterwarnings('ignore')
data_dir = "./365-9-data"
data_dir = pathlib.Path(data_dir)
image_count = len(list(data_dir.glob('*/*')))
print("图片总数为：",image_count)
二、数据预处理
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 = 64
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=12,
image_size=(img_height, img_width),
batch_size=batch_size)"""
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)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)
每批有64张图象，长宽都是224的，彩色3通道
2. 再次检查数据
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
breakImage_batch是形状的张量（64, 224, 224, 3)。这是一批形状224x224x3的64张图片（最后一维指的是彩色通道RGB）。Label_batch是形状（64，）的张量，这些标签对应8张图片
3. 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 def preprocess_image(image,label): return (image/255.0,label) # 归一化处理 train_ds = train_ds.map(preprocess_image, num_parallel_calls=AUTOTUNE) val_ds = val_ds.map(preprocess_image, num_parallel_calls=AUTOTUNE) train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE) val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)如果报
AttributeError: module 'tensorflow._api.v2.data' has no attribute 'AUTOTUNE'错误，就将AUTOTUNE = tf.data.AUTOTUNE更换为AUTOTUNE = tf.data.experimental.AUTOTUNE，这个错误是由于版本问题引起的 -
4. 可视化数据
plt.figure(figsize=(15, 10)) # 图形的宽为15高为10 for images, labels in train_ds.take(1): for i in range(8): ax = plt.subplot(5, 8, i + 1) plt.imshow(images[i]) plt.title(class_names[labels[i]]) plt.axis("off")
-
三、构建VGG-16网络
VGG优缺点分析：
- VGG优点
-
VGG的结构非常简洁，整个网络都使用了同样大小的卷积核尺寸（3x3）和最大池化尺寸（2x2）。
- VGG缺点
-
1)训练时间过长，调参难度大。2)需要的存储容量大，不利于部署。例如存储VGG-16权重值文件的大小为500多MB，不利于安装到嵌入式系统中。
结构说明：
- 13个卷积层（Convolutional Layer），分别用
blockX_convX表示 - 3个全连接层（Fully connected Layer），分别用
fcX与predictions表示 - 5个池化层（Pool layer），分别用
blockX_pool表示 -
VGG-16包含了16个隐藏层（13个卷积层和3个全连接层），故称为VGG-16
from tensorflow.keras import layers, models, Input
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout
def VGG16(nb_classes, input_shape):
input_tensor = Input(shape=input_shape)
# 1st block
x = Conv2D(64, (3,3), activation='relu', padding='same',name='block1_conv1')(input_tensor)
x = Conv2D(64, (3,3), activation='relu', padding='same',name='block1_conv2')(x)
x = MaxPooling2D((2,2), strides=(2,2), name = 'block1_pool')(x)
# 2nd block
x = Conv2D(128, (3,3), activation='relu', padding='same',name='block2_conv1')(x)
x = Conv2D(128, (3,3), activation='relu', padding='same',name='block2_conv2')(x)
x = MaxPooling2D((2,2), strides=(2,2), name = 'block2_pool')(x)
# 3rd block
x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv1')(x)
x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv2')(x)
x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv3')(x)
x = MaxPooling2D((2,2), strides=(2,2), name = 'block3_pool')(x)
# 4th block
x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv1')(x)
x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv2')(x)
x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv3')(x)
x = MaxPooling2D((2,2), strides=(2,2), name = 'block4_pool')(x)
# 5th block
x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv1')(x)
x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv2')(x)
x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv3')(x)
x = MaxPooling2D((2,2), strides=(2,2), name = 'block5_pool')(x)
# full connection
x = Flatten()(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dense(4096, activation='relu', name='fc2')(x)
output_tensor = Dense(nb_classes, activation='softmax', name='predictions')(x)
model = Model(input_tensor, output_tensor)
return model
model=VGG16(1000, (img_width, img_height, 3))
model.summary()
四、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。
- 评价函数（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。
model.compile(optimizer="adam", loss ='sparse_categorical_crossentropy', metrics =['accuracy'])五、训练模型
-
from tqdm import tqdm import tensorflow.keras.backend as K epochs = 10 lr = 1e-4 # 记录训练数据，方便后面的分析 history_train_loss = [] history_train_accuracy = [] history_val_loss = [] history_val_accuracy = [] for epoch in range(epochs): train_total = len(train_ds) val_total = len(val_ds) """ total：预期的迭代数目 ncols：控制进度条宽度 mininterval：进度更新最小间隔，以秒为单位（默认值：0.1） """ with tqdm(total=train_total, desc=f'Epoch {epoch + 1}/{epochs}',mininterval=1,ncols=100) as pbar: lr = lr*0.92 K.set_value(model.optimizer.lr, lr) train_loss = [] train_accuracy = [] for image,label in train_ds: """ 训练模型，简单理解train_on_batch就是：它是比model.fit()更高级的一个用法 想详细了解 train_on_batch 的同学， 可以看看我的这篇文章：https://www.yuque.com/mingtian-fkmxf/hv4lcq/ztt4gy """ # 这里生成的是每一个batch的acc与loss history = model.train_on_batch(image,label) train_loss.append(history[0]) train_accuracy.append(history[1]) pbar.set_postfix({"train_loss": "%.4f"%history[0], "train_acc":"%.4f"%history[1], "lr": K.get_value(model.optimizer.lr)}) pbar.update(1) history_train_loss.append(np.mean(train_loss)) history_train_accuracy.append(np.mean(train_accuracy)) print('开始验证！') with tqdm(total=val_total, desc=f'Epoch {epoch + 1}/{epochs}',mininterval=0.3,ncols=100) as pbar: val_loss = [] val_accuracy = [] for image,label in val_ds: # 这里生成的是每一个batch的acc与loss history = model.test_on_batch(image,label) val_loss.append(history[0]) val_accuracy.append(history[1]) pbar.set_postfix({"val_loss": "%.4f"%history[0], "val_acc":"%.4f"%history[1]}) pbar.update(1) history_val_loss.append(np.mean(val_loss)) history_val_accuracy.append(np.mean(val_accuracy)) print('结束验证！') print("验证loss为：%.4f"%np.mean(val_loss)) print("验证准确率为：%.4f"%np.mean(val_accuracy))
-
六、模型评估
-
epochs_range = range(epochs) plt.figure(figsize=(14, 4)) plt.subplot(1, 2, 1) plt.plot(epochs_range, history_train_accuracy, label='Training Accuracy') plt.plot(epochs_range, history_val_accuracy, label='Validation Accuracy') plt.legend(loc='lower right') plt.title('Training and Validation Accuracy') plt.subplot(1, 2, 2) plt.plot(epochs_range, history_train_loss, label='Training Loss') plt.plot(epochs_range, history_val_loss, label='Validation Loss') plt.legend(loc='upper right') plt.title('Training and Validation Loss') plt.show()七、预测
-
import numpy as np # 采用加载的模型（new_model）来看预测结果 plt.figure(figsize=(18, 3)) # 图形的宽为18高为5 plt.suptitle("预测结果展示") for images, labels in val_ds.take(1): for i in range(8): ax = plt.subplot(1,8, i + 1) # 显示图片 plt.imshow(images[i].numpy()) # 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 = model.predict(img_array) plt.title(class_names[np.argmax(predictions)]) plt.axis("off")
-
八、数据增强
-
我们使用tf.keras.layers.experimental.preprocessing.RandomFlip：水平和垂直随机翻转每个图像来增强数据，来生成大量的不同但相关的图像。这些变换使模型在训练过程中能够看到更多的变化，从而增强其对不同情况下的泛化能力，同时可以学习到更为普遍的特征，从而降低过拟合的风险
data_augmentation = tf.keras.Sequential(tf.keras.layers.experimental.preprocessing.RandomFlip("horizontal_and_vertical")) # Add the image to a batch. image = tf.expand_dims(images[i], 0) plt.figure(figsize=(8, 8)) for i in range(9): augmented_image = data_augmentation(image) ax = plt.subplot(3, 3, i + 1) plt.imshow(augmented_image[0]) plt.axis("off")
batch_size = 32
AUTOTUNE = tf.data.AUTOTUNE
def prepare(ds):
ds = ds.map(lambda x, y: (data_augmentation(x, training=True), y), num_parallel_calls=AUTOTUNE)
return ds
train_ds = prepare(train_ds)
from tensorflow.keras import layers, models, Input
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout
model = tf.keras.Sequential([
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(len(class_names))
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
epochs=20
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs
)
可见，数据增强后，准确率有所上升
![[数据集][目标检测]电力场景输电线导线散股检测数据集VOC+YOLO格式3890张1类别](https://i-blog.csdnimg.cn/direct/77806ebcef9646e68d1ba8c2989e6e60.png)
