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()