python实现对MNIST数据集的手写数字分类

以下是使用Python实现对MNIST数据集的手写数字分类的步骤：

1. 导入必要的库和数据集

import tensorflow as tf
from tensorflow import keras

mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

2. 数据预处理

train_images = train_images / 255.0
test_images = test_images / 255.0

3. 构建模型

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

4. 编译模型

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

5. 训练模型

model.fit(train_images, train_labels, epochs=10)

6. 评估模型

test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy:', test_acc)

7. 进行预测

predictions = model.predict(test_images)