卷积神经网络（VGG-19）灵笼人物识别

文章目录

前期工作
- 1. 设置GPU（如果使用的是CPU可以忽略这步）
- - 我的环境：
- 2. 导入数据
- 3. 查看数据
二、数据预处理
- 1. 加载数据
- 2. 可视化数据
- 3. 再次检查数据
- 4. 配置数据集
- 5. 归一化
三、构建VGG-19网络
- 1. 官方模型（已打包好）
- 2. 自建模型
- 3. 网络结构图
四、编译
五、训练模型
六、模型评估
七、保存and加载模型
八、预测

前期工作

1. 设置GPU（如果使用的是CPU可以忽略这步）

我的环境：

语言环境：Python3.6.5
编译器：jupyter notebook
深度学习环境：TensorFlow2.4.1

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")

2. 导入数据

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

# 设置随机种子尽可能使结果可以重现
import numpy as np
np.random.seed(1)

# 设置随机种子尽可能使结果可以重现
import tensorflow as tf
tf.random.set_seed(1)

from tensorflow import keras
from tensorflow.keras import layers,models

import pathlib

data_dir = "weather_photos/"
data_dir = pathlib.Path(data_dir)

3. 查看数据

数据集中一共有白月魁、查尔斯、红蔻、马克、摩根、冉冰等6个人物角色。

文件夹	含义	数量
baiyuekui	白月魁	40 张
chaersi	查尔斯	76 张
hongkou	红蔻	36 张
make	马克	38张
mogen	摩根	30 张
ranbing	冉冰	60张

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 = 32
img_height = 224
img_width = 224

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.1,
    subset="training",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

Found 280 files belonging to 6 classes.
Using 252 files for training.

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.1,
    subset="validation",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

Found 280 files belonging to 6 classes.
Using 28 files for validation.

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)

['baiyuekui', 'chaersi', 'hongkou', 'make', 'mogen', 'ranbing']

2. 可视化数据

plt.figure(figsize=(10, 5))  # 图形的宽为10高为5

for images, labels in train_ds.take(1):
    for i in range(8):
        
        ax = plt.subplot(2, 4, i + 1)  

        plt.imshow(images[i].numpy().astype("uint8"))
        plt.title(class_names[labels[i]])
        
        plt.axis("off")

在这里插入图片描述

plt.imshow(images[1].numpy().astype("uint8"))

在这里插入图片描述

3. 再次检查数据

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

(16, 224, 224, 3)
(16,)

Image_batch是形状的张量（16,180,180,3）。这是一批形状180x180x3的16张图片（最后一维指的是彩色通道RGB）。
Label_batch是形状（16，）的张量，这些标签对应16张图片

4. 配置数据集

AUTOTUNE = tf.data.AUTOTUNE

train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

5. 归一化

normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)
normalization_train_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
val_ds = val_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(val_ds))
first_image = image_batch[0]
# 查看归一化后的数据
print(np.min(first_image), np.max(first_image))

0.0 0.9928046

三、构建VGG-19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。

1. 官方模型（已打包好）

官网模型调用这块我放到后面几篇文章中，下面主要讲一下VGG-19

# model = keras.applications.VGG19(weights='imagenet')
# model.summary()

2. 自建模型

from tensorflow.keras import layers, models, Input
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout

def VGG19(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 = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv4')(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 = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv4')(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 = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv4')(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=VGG19(1000, (img_width, img_height, 3))
model.summary()

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv4 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv4 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv4 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 25088)             0         
_________________________________________________________________
fc1 (Dense)                  (None, 4096)              102764544 
_________________________________________________________________
fc2 (Dense)                  (None, 4096)              16781312  
_________________________________________________________________
predictions (Dense)          (None, 1000)              4097000   
=================================================================
Total params: 143,667,240
Trainable params: 143,667,240
Non-trainable params: 0
_________________________________________________________________

3. 网络结构图

结构说明：

16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-19包含了19个隐藏层（16个卷积层和3个全连接层），故称为VGG-19

** 在这里插入图片描述
**

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。
指标（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。

# 设置优化器
opt = tf.keras.optimizers.Adam(learning_rate=1e-4)

model.compile(optimizer=opt,
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

五、训练模型

epochs = 10

history = model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=epochs
)

Epoch 1/10
16/16 [==============================] - 21s 274ms/step - loss: 5.4494 - accuracy: 0.1508 - val_loss: 6.8600 - val_accuracy: 0.0714
Epoch 2/10
16/16 [==============================] - 2s 130ms/step - loss: 1.7976 - accuracy: 0.3174 - val_loss: 6.8402 - val_accuracy: 0.3929
Epoch 3/10
16/16 [==============================] - 2s 139ms/step - loss: 1.4882 - accuracy: 0.4201 - val_loss: 6.8453 - val_accuracy: 0.5357
Epoch 4/10
16/16 [==============================] - 2s 135ms/step - loss: 1.1548 - accuracy: 0.5917 - val_loss: 6.8551 - val_accuracy: 0.3571
Epoch 5/10
16/16 [==============================] - 2s 139ms/step - loss: 1.0376 - accuracy: 0.6267 - val_loss: 6.8421 - val_accuracy: 0.4286
Epoch 6/10
16/16 [==============================] - 2s 136ms/step - loss: 1.0189 - accuracy: 0.5942 - val_loss: 6.8277 - val_accuracy: 0.5714
Epoch 7/10
16/16 [==============================] - 2s 133ms/step - loss: 0.6873 - accuracy: 0.7761 - val_loss: 6.8382 - val_accuracy: 0.6429
Epoch 8/10
16/16 [==============================] - 2s 128ms/step - loss: 0.3739 - accuracy: 0.9019 - val_loss: 6.8109 - val_accuracy: 0.5357
Epoch 9/10
16/16 [==============================] - 2s 128ms/step - loss: 0.3761 - accuracy: 0.8547 - val_loss: 6.8101 - val_accuracy: 0.6429
Epoch 10/10
16/16 [==============================] - 2s 129ms/step - loss: 0.1258 - accuracy: 0.9713 - val_loss: 6.7796 - val_accuracy: 0.8929

六、模型评估

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs_range = range(epochs)

plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

在这里插入图片描述

七、保存and加载模型

# 保存模型
model.save('model/my_model.h5')
# 加载模型
new_model = keras.models.load_model('model/my_model.h5')

八、预测

# 采用加载的模型（new_model）来看预测结果

plt.figure(figsize=(10, 5))  # 图形的宽为10高为5

for images, labels in val_ds.take(1):
    for i in range(8):
        ax = plt.subplot(2, 4, i + 1)  
        
        # 显示图片
        plt.imshow(images[i])
        
        # 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 = new_model.predict(img_array)
        plt.title(class_names[np.argmax(predictions)])

        plt.axis("off")