Tensorflow实现深度学习8：猫狗识别

本文为为🔗365天深度学习训练营内部文章

原作者：K同学啊

一 导入数据

import matplotlib.pyplot as plt
import tensorflow as tf
# 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 = "./data"
data_dir = pathlib.Path(data_dir)

image_count = len(list(data_dir.glob('*/*')))

print("图片总数为：",image_count)

图片总数为： 3400

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 = 8
img_height = 224
img_width = 224

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)

Found 3400 files belonging to 2 classes.
Using 2720 files for training.

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)

Found 3400 files belonging to 2 classes.
Using 680 files for validation.

class_names = train_ds.class_names
print(class_names)

['cat', 'dog']

2. 再次检查数据

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

(8, 224, 224, 3)
(8,)

3. 配置数据集

• shuffle() ： 打乱数据

• prefetch() ：预取数据，加速运行

• 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)

在这里数据处理的过程中，比前几次稍微不同的是多加了一个归一化的处理

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VG-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

构建方法1：调用官网封装好的模型函数

model = tf.keras.applications.VGG16(weights='imagenet')
model.summary()

Model: "vgg16"
_________________________________________________________________
 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_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_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_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: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_________________________________________________________________

构建方法二：自己手动搭建模型

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

def 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()

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_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_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_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: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_________________________________________________________________

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。

# 设置初始学习率
initial_learning_rate = 1e-4

lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
        initial_learning_rate, 
        decay_steps=30,      # 敲黑板！！！这里是指 steps，不是指epochs
        decay_rate=0.92,     # lr经过一次衰减就会变成 decay_rate*lr
        staircase=True)

# 设置优化器
opt = tf.keras.optimizers.Adam(learning_rate=initial_learning_rate)

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

早停法：

from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping

epochs = 10

# 保存最佳模型参数
checkpointer = ModelCheckpoint('best_model.h5',
                                monitor='val_accuracy',
                                verbose=1,
                                save_best_only=True,
                                save_weights_only=True)

# 设置早停
earlystopper = EarlyStopping(monitor='val_accuracy', 
                             min_delta=0.001,
                             patience=20, 
                             verbose=1)

五、训练模型

history = model.fit(train_ds,
                    validation_data=val_ds,
                    epochs=epochs,
                    callbacks=[checkpointer, earlystopper])

Epoch 1/10
340/340 [==============================] - ETA: 0s - loss: 0.2396 - accuracy: 0.8930
Epoch 1: val_accuracy improved from -inf to 0.99412, saving model to best_model.h5
340/340 [==============================] - 1376s 4s/step - loss: 0.2396 - accuracy: 0.8930 - val_loss: 0.0210 - val_accuracy: 0.9941
Epoch 2/10
340/340 [==============================] - ETA: 0s - loss: 0.0276 - accuracy: 0.9908
Epoch 2: val_accuracy did not improve from 0.99412
340/340 [==============================] - 1345s 4s/step - loss: 0.0276 - accuracy: 0.9908 - val_loss: 0.0465 - val_accuracy: 0.9853
Epoch 3/10
340/340 [==============================] - ETA: 0s - loss: 0.1150 - accuracy: 0.9717
Epoch 3: val_accuracy did not improve from 0.99412
340/340 [==============================] - 1316s 4s/step - loss: 0.1150 - accuracy: 0.9717 - val_loss: 0.0704 - val_accuracy: 0.9750
Epoch 4/10
340/340 [==============================] - ETA: 0s - loss: 0.0192 - accuracy: 0.9949
Epoch 4: val_accuracy improved from 0.99412 to 0.99853, saving model to best_model.h5
340/340 [==============================] - 1336s 4s/step - loss: 0.0192 - accuracy: 0.9949 - val_loss: 0.0083 - val_accuracy: 0.9985
Epoch 5/10
340/340 [==============================] - ETA: 0s - loss: 0.0248 - accuracy: 0.9930
Epoch 5: val_accuracy did not improve from 0.99853
340/340 [==============================] - 1321s 4s/step - loss: 0.0248 - accuracy: 0.9930 - val_loss: 0.0036 - val_accuracy: 0.9985
Epoch 6/10
340/340 [==============================] - ETA: 0s - loss: 0.0240 - accuracy: 0.9937
Epoch 6: val_accuracy did not improve from 0.99853
340/340 [==============================] - 1323s 4s/step - loss: 0.0240 - accuracy: 0.9937 - val_loss: 0.0074 - val_accuracy: 0.9956
Epoch 7/10
340/340 [==============================] - ETA: 0s - loss: 0.0039 - accuracy: 0.9982
Epoch 7: val_accuracy did not improve from 0.99853
340/340 [==============================] - 1324s 4s/step - loss: 0.0039 - accuracy: 0.9982 - val_loss: 0.0069 - val_accuracy: 0.9971
Epoch 8/10
340/340 [==============================] - ETA: 0s - loss: 8.3202e-04 - accuracy: 1.0000
Epoch 8: val_accuracy did not improve from 0.99853
340/340 [==============================] - 1318s 4s/step - loss: 8.3202e-04 - accuracy: 1.0000 - val_loss: 0.0205 - val_accuracy: 0.9956
Epoch 9/10
340/340 [==============================] - ETA: 0s - loss: 0.0759 - accuracy: 0.9801
Epoch 9: val_accuracy did not improve from 0.99853
340/340 [==============================] - 1326s 4s/step - loss: 0.0759 - accuracy: 0.9801 - val_loss: 0.0372 - val_accuracy: 0.9882
Epoch 10/10
340/340 [==============================] - ETA: 0s - loss: 0.0242 - accuracy: 0.9934
Epoch 10: val_accuracy did not improve from 0.99853
340/340 [==============================] - 1328s 4s/step - loss: 0.0242 - accuracy: 0.9934 - val_loss: 0.0072 - val_accuracy: 0.9985

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()

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")

1/1 [==============================] - 1s 609ms/step
1/1 [==============================] - 0s 123ms/step
1/1 [==============================] - 0s 140ms/step
1/1 [==============================] - 0s 134ms/step
1/1 [==============================] - 0s 129ms/step
1/1 [==============================] - 0s 126ms/step
1/1 [==============================] - 0s 124ms/step
1/1 [==============================] - 0s 123ms/step

在训练模型的时候，除了用上述的代码之外，还可以用另一种方式。

改用model.train_on_batch方法。两种方法的比较：

• model.fit()：用起来十分简单，对新手非常友好

• model.train_on_batch()：封装程度更低，可以玩更多花样。

此外我也引入了进度条的显示方式，更加方便我们及时查看模型训练过程中的情况，可以及时打印各项指标

model.compile(optimizer="adam",
              loss     ='sparse_categorical_crossentropy',
              metrics  =['accuracy'])

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)

        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
            """
            history = model.train_on_batch(image,label)

            train_loss     = history[0]
            train_accuracy = history[1]
            
            pbar.set_postfix({"loss": "%.4f"%train_loss,
                              "accuracy":"%.4f"%train_accuracy,
                              "lr": K.get_value(model.optimizer.lr)})
            pbar.update(1)
        history_train_loss.append(train_loss)
        history_train_accuracy.append(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:

        for image,label in val_ds:      
            
            history = model.test_on_batch(image,label)
            
            val_loss     = history[0]
            val_accuracy = history[1]
            
            pbar.set_postfix({"loss": "%.4f"%val_loss,
                              "accuracy":"%.4f"%val_accuracy})
            pbar.update(1)
        history_val_loss.append(val_loss)
        history_val_accuracy.append(val_accuracy)
            
    print('结束验证！')
    print("验证loss为：%.4f"%val_loss)
    print("验证准确率为：%.4f"%val_accuracy)

​​​​​

对比之前的model.fit()方法，这次还引用了更详细的进度条。后续的操作和上述方法一样