导包

import tensorflow as tf

from tensorflow.keras import datasets, layers, models

加载Fashion-MNIST数据集

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

归一化像素值到[0, 1]区间

train_images, test_images = train_images / 255.0, test_images / 255.0

由于Fashion-MNIST的图像是灰度图像，需要增加一个颜色通道

train_images = train_images.reshape(-1, 28, 28, 1)

test_images = test_images.reshape(-1, 28, 28, 1)

对标签进行分类编码

train_labels = tf.keras.utils.to_categorical(train_labels, 10)

test_labels = tf.keras.utils.to_categorical(test_labels, 10)

定义AlexNet模型

model = models.Sequential([

    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),

    layers.MaxPooling2D((2, 2)),

    layers.Conv2D(64, (3, 3), activation='relu'),

    layers.MaxPooling2D((2, 2)),

    layers.Conv2D(128, (3, 3), activation='relu'),

    layers.MaxPooling2D((2, 2)),

    layers.Flatten(),

    layers.Dense(128, activation='relu'),

    layers.Dense(10, activation='softmax')

])

编译模型

model.compile(optimizer='adam',

              loss='categorical_crossentropy',

              metrics=['accuracy'])

训练模型

model.fit(train_images, train_labels, epochs=10, batch_size=64, validation_data=(test_images, test_labels))

评估模型

test_loss, test_acc = model.evaluate(test_images, test_labels)

print(f'Test Accuracy: {test_acc:.4f}')

训练模型保存

save_path = r'D:\\图像处理、深度学习\\训练保存\\AlexNet.h5'

model.save(save_path)