学习tensorflow2.0官方文档记录（一）：https://tensorflow.google.cn/tutorials/images/cnn?hl=zh_cn
下载并准备 CIFAR10 数据集，有VPN的下的快，需要的私信我免费发。
直接搬代码了：
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()# 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 / 255.0class_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)# 由于 CIFAR 的标签是 array，# 因此您需要额外的索引（index）。plt.xlabel(class_names[train_labels[i][0]])
plt.show()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'))
#model.summary()model.add(layers.Flatten())
model.add(layers.Dense(64,activation='relu'))
model.add(layers.Dense(10))#model.summary()
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))
#print(history.history)plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right') #图例
plt.show()test_loss,test_acc=model.evaluate(test_images,test_labels,verbose = 2)#verbose = 显#示在屏幕上
print(test_loss,test_acc)
