第T9周：猫狗识别2

本文主要是介绍第T9周：猫狗识别2，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

>- **🍨 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 tfgpus = 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)break

Image_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.AUTOTUNEdef 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10for 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, Dropoutdef VGG16(nb_classes, input_shape):input_tensor = Input(shape=input_shape)# 1st blockx = 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 blockx = 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 blockx = 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 blockx = 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 blockx = 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 connectionx = 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 modelmodel=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 Kepochs = 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*0.92K.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losshistory = 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losshistory = 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.AUTOTUNEdef prepare(ds):ds = ds.map(lambda x, y: (data_augmentation(x, training=True), y), num_parallel_calls=AUTOTUNE)return dstrain_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
)