CNN模板

该代码使用CNN，实现一个简单的10分类问题

代码如下：

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

#准备数据CIFAR10
(train_images, train_labels),(test_images, test_labels)=datasets.cifar10.load_data()
#将像素的值标准化
train_images=train_images/255.0
test_images=test_images/255.0

#验证数据，将前25张图片打印出来
'''
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
               'dog', 'frog', 'horse', 'ship', 'truck']

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,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()
'''

#构建神经网络模型CNN
model=models.Sequential()
model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D(2,2))
model.add(layers.Conv2D(64, (3,3), activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64,(3,3), activation='relu'))

#构建全联接层
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))

#查看整个CNN结构
model.summary()

#训练并编译模型
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy']
              )
history=model.fit(train_images, train_labels, epochs=10,
                   validation_data=(test_images, test_labels))

#模型评估
plt.rcParams['font.sans-serif']=['SimHei']
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.xlabel('组')
plt.ylabel('准确率：')
plt.ylim([0.5, 1])
plt.legend(loc='best')
plt.show()