机器学习+神经网络+案例的使用：.ipynb代码

import tensorflow as tf from keras import datasets, layers, models import matplotlib.pyplot as plt (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() 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() train_images, test_images = train_images/255.0, test_images/255.0 model = models.Sequential() #增加一个卷积层， model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3))) #添加一个池化层 model.add(layers.MaxPool2D((2,2))) model.add(layers.Conv2D(64, (3,3), activation='relu')) model.add(layers.MaxPool2D((2,2))) model.add(layers.Conv2D(64, (3,3), activation='relu')) model.add(layers.Flatten()) model.add(layers.Dense(64, activation='relu')) model.add(layers.Dense(10)) 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)) 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') plt.show() test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)