深度学习实例3_卷积神经网络Cifar10数据集分类——自学笔记

导入模块和数据

在Keras中已经内置了多种公共数据集，其中就包含CIFAR-10数据集

官网下载链接：http://www.cs.toronto.edu/\~kriz/cifar.html

python 复制代码

import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
import numpy as np

(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()

#加载数据
cifar10 = tf.keras.datasets.cifar10
(train_x,train_y),(test_x,test_y) = cifar10.load_data()
print('\n train_x:%s, train_y:%s, test_x:%s, test_y:%s'%(train_x.shape,train_y.shape,test_x.shape,test_y.shape))

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 train_x:(50000, 32, 32, 3), train_y:(50000, 1), test_x:(10000, 32, 32, 3), test_y:(10000, 1)

归一化

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#数据预处理
X_train,X_test = tf.cast(train_images/255.0,tf.float32),tf.cast(test_images/255.0,tf.float32)     #归一化
y_train,y_test = tf.cast(train_labels,tf.int16),tf.cast(test_labels,tf.int16)

# 将像素的值标准化至0到1的区间内。
train_images, test_images = train_images / 255.0, test_images / 255.0
train_images.shape,test_images.shape,train_labels.shape,test_labels.shape

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((50000, 32, 32, 3), (10000, 32, 32, 3), (50000, 1), (10000, 1))

可视化

CIFAR10数据集共有60000个样本，每个样本都是一张32*32像素的RGB图像（彩色图像），每个RGB图像又必定分为3个通道（R通道、G通道、B通道）。这60000个样本被分成了50000个训练样本和10000个测试样本。

CIFAR10数据集是用来监督学习训练的，CIFAR10中有10类物体，标签值分别按照0~9来区分,他们分别是飞机（ airplane ）、汽车（ automobile ）、鸟（ bird ）、猫（ cat ）、鹿（ deer ）、狗（ dog ）、青蛙（ frog ）、马（ horse ）、船（ ship ）和卡车（ truck ）。

python 复制代码

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer','dog', 'frog', 'horse', 'ship', 'truck']

plt.figure(figsize=(20,10))
for i in range(60):
    plt.subplot(5,12,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i][0]])
plt.show()

构建CNN网络

python 复制代码

#建立模型
model = tf.keras.Sequential()
##特征提取阶段
#第一层
model.add(tf.keras.layers.Conv2D(16,kernel_size=(3,3),padding='same',activation=tf.nn.relu,data_format='channels_last',input_shape=X_train.shape[1:]))  #卷积层，16个卷积核，大小（3，3），保持原图像大小，relu激活函数，输入形状（28，28，1）
model.add(tf.keras.layers.Conv2D(16,kernel_size=(3,3),padding='same',activation=tf.nn.relu))
model.add(tf.keras.layers.MaxPool2D(pool_size=(2,2)))   #池化层，最大值池化，卷积核（2，2）
#第二层
model.add(tf.keras.layers.Conv2D(32,kernel_size=(3,3),padding='same',activation=tf.nn.relu))
model.add(tf.keras.layers.Conv2D(32,kernel_size=(3,3),padding='same',activation=tf.nn.relu))
model.add(tf.keras.layers.MaxPool2D(pool_size=(2,2)))
##分类识别阶段
#第三层
model.add(tf.keras.layers.Flatten())    #改变输入形状
#第四层
model.add(tf.keras.layers.Dense(128,activation='relu'))     #全连接网络层，128个神经元，relu激活函数
model.add(tf.keras.layers.Dense(10,activation='softmax'))   #输出层，10个节点
print(model.summary())      #查看网络结构和参数信息

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Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 32, 32, 16)        448       
                                                                 
 conv2d_1 (Conv2D)           (None, 32, 32, 16)        2320      
                                                                 
 max_pooling2d (MaxPooling2  (None, 16, 16, 16)        0         
 D)                                                              
                                                                 
 conv2d_2 (Conv2D)           (None, 16, 16, 32)        4640      
                                                                 
 conv2d_3 (Conv2D)           (None, 16, 16, 32)        9248      
                                                                 
 max_pooling2d_1 (MaxPoolin  (None, 8, 8, 32)          0         
 g2D)                                                            
                                                                 
 flatten (Flatten)           (None, 2048)              0         
                                                                 
 dense (Dense)               (None, 128)               262272    
                                                                 
 dense_1 (Dense)             (None, 10)                1290      
                                                                 
=================================================================
Total params: 280218 (1.07 MB)
Trainable params: 280218 (1.07 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
None

python 复制代码

####delete


model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)), #卷积层1，卷积核3*3
    layers.MaxPooling2D((2, 2)),                   #池化层1，2*2采样
    layers.Conv2D(64, (3, 3), activation='relu'),  #卷积层2，卷积核3*3
    layers.MaxPooling2D((2, 2)),                   #池化层2，2*2采样
    layers.Conv2D(64, (3, 3), activation='relu'),  #卷积层3，卷积核3*3

    layers.Flatten(),                      #Flatten层，连接卷积层与全连接层
    layers.Dense(64, activation='relu'),   #全连接层，特征进一步提取
    layers.Dense(10)                       #输出层，输出预期结果
])

model.summary()  # 打印网络结构

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Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 30, 30, 32)        896       
                                                                 
 max_pooling2d (MaxPooling2  (None, 15, 15, 32)        0         
 D)                                                              
                                                                 
 conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     
                                                                 
 max_pooling2d_1 (MaxPoolin  (None, 6, 6, 64)          0         
 g2D)                                                            
                                                                 
 conv2d_2 (Conv2D)           (None, 4, 4, 64)          36928     
                                                                 
 flatten (Flatten)           (None, 1024)              0         
                                                                 
 dense (Dense)               (None, 64)                65600     
                                                                 
 dense_1 (Dense)             (None, 10)                650       
                                                                 
=================================================================
Total params: 122570 (478.79 KB)
Trainable params: 122570 (478.79 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________

配置模型训练方法

adam算法参数采用keras默认的公开参数，损失函数采用稀疏交叉熵损失函数，准确率采用稀疏分类准确率函数

python 复制代码

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

模型训练

python 复制代码

#训练模型
#批量训练大小为64，迭代5次，测试集比例0.2（48000条训练集数据，12000条测试集数据）

import time
print('--------------')
nowtime = time.strftime('%Y-%m-%d %H:%M:%S')
print(nowtime)

print('--------------')
nowtime = time.strftime('%Y-%m-%d %H:%M:%S')
print('训练前时刻：'+str(nowtime))

history = model.fit(X_train,y_train,batch_size=64,epochs=5,validation_split=0.2)

print('--------------')
nowtime = time.strftime('%Y-%m-%d %H:%M:%S')
print('训练后时刻：'+str(nowtime))

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--------------
2024-05-09 08:38:42
--------------
训练前时刻：2024-05-09 08:38:42
Epoch 1/5
625/625 [==============================] - 9s 6ms/step - loss: 1.5114 - sparse_categorical_accuracy: 0.4525 - val_loss: 1.2983 - val_sparse_categorical_accuracy: 0.5391
Epoch 2/5
625/625 [==============================] - 4s 6ms/step - loss: 1.1277 - sparse_categorical_accuracy: 0.6010 - val_loss: 1.0337 - val_sparse_categorical_accuracy: 0.6372
Epoch 3/5
625/625 [==============================] - 3s 6ms/step - loss: 0.9434 - sparse_categorical_accuracy: 0.6684 - val_loss: 0.9594 - val_sparse_categorical_accuracy: 0.6607
Epoch 4/5
625/625 [==============================] - 4s 7ms/step - loss: 0.8311 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.8849 - val_sparse_categorical_accuracy: 0.6873
Epoch 5/5
625/625 [==============================] - 3s 5ms/step - loss: 0.7294 - sparse_categorical_accuracy: 0.7453 - val_loss: 0.8902 - val_sparse_categorical_accuracy: 0.6915
--------------
训练后时刻：2024-05-09 08:39:25

预测

python 复制代码

#评估模型
model.evaluate(X_test,y_test,verbose=2)     #每次迭代输出一条记录，来评价该模型是否有比较好的泛化能力

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313/313 - 1s - loss: 0.8857 - sparse_categorical_accuracy: 0.6992 - 902ms/epoch - 3ms/step





[0.8857089877128601, 0.6991999745368958]

保存模型

python 复制代码

#保存整个模型
model.save('CIFAR10_CNN_weights.h5')

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/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(

结果可视化

python 复制代码

#结果可视化
print(history.history)
loss = history.history['loss']          #训练集损失
val_loss = history.history['val_loss']  #测试集损失
acc = history.history['sparse_categorical_accuracy']            #训练集准确率
val_acc = history.history['val_sparse_categorical_accuracy']    #测试集准确率

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{'loss': [1.5113933086395264, 1.1277204751968384, 0.9433806538581848, 0.83107590675354, 0.7294103503227234], 'sparse_categorical_accuracy': [0.45252498984336853, 0.6009500026702881, 0.6684250235557556, 0.7100499868392944, 0.7452999949455261], 'val_loss': [1.2982960939407349, 1.033674955368042, 0.9594402313232422, 0.8848526477813721, 0.8902430534362793], 'val_sparse_categorical_accuracy': [0.5390999913215637, 0.6371999979019165, 0.6607000231742859, 0.6873000264167786, 0.6915000081062317]}

python 复制代码

plt.figure(figsize=(10,3))

plt.subplot(121)
plt.plot(loss,color='b',label='train')
plt.plot(val_loss,color='r',label='test')
plt.ylabel('loss')
plt.legend()

plt.subplot(122)
plt.plot(acc,color='b',label='train')
plt.plot(val_acc,color='r',label='test')
plt.ylabel('Accuracy')
plt.legend()

#暂停5秒关闭画布，否则画布一直打开的同时，会持续占用GPU内存
#根据需要自行选择
#plt.ion()       #打开交互式操作模式
#plt.show()
#plt.pause(5)
#plt.close()

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<matplotlib.legend.Legend at 0x78eb8af75d20>

使用已经保存的模型

python 复制代码

#使用模型
plt.figure()
for i in range(10):
    num = np.random.randint(1,10000)

    plt.subplot(2,5,i+1)
    plt.axis('off')
    plt.imshow(test_x[num],cmap='gray')
    demo = tf.reshape(X_test[num],(1,32,32,3))
    y_pred = np.argmax(model.predict(demo))
    plt.title('label:'+str(test_y[num])+'\n predict:'+str(y_pred))
#y_pred = np.argmax(model.predict(X_test[0:5]),axis=1)
#print('X_test[0:5]: %s'%(X_test[0:5].shape))
#print('y_pred: %s'%(y_pred))

#plt.ion()       #打开交互式操作模式
plt.show()
#plt.pause(5)
#plt.close()

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1/1 [==============================] - 0s 19ms/step
1/1 [==============================] - 0s 19ms/step
1/1 [==============================] - 0s 18ms/step
1/1 [==============================] - 0s 19ms/step
1/1 [==============================] - 0s 19ms/step
1/1 [==============================] - 0s 21ms/step
1/1 [==============================] - 0s 19ms/step
1/1 [==============================] - 0s 20ms/step
1/1 [==============================] - 0s 19ms/step
1/1 [==============================] - 0s 19ms/step

参考文献：

http://t.csdnimg.cn/dUnCD

https://blog.csdn.net/qq_38251616/article/details/116978213