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导包
- import tensorflow as tf
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- 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)
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- 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)
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- test_labels = tf.keras.utils.to_categorical(test_labels, 10)
定义AlexNet模型
- model = models.Sequential([
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- layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
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- layers.MaxPooling2D((2, 2)),
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- layers.Conv2D(64, (3, 3), activation='relu'),
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- layers.MaxPooling2D((2, 2)),
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- layers.Conv2D(128, (3, 3), activation='relu'),
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- layers.MaxPooling2D((2, 2)),
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- layers.Flatten(),
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- layers.Dense(128, activation='relu'),
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- layers.Dense(10, activation='softmax')
-
- ])
编译模型
- model.compile(optimizer='adam',
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- loss='categorical_crossentropy',
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- 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)
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