神经网络多层感知分类器实现手写数字识别？

以下是一个使用神经网络多层感知分类器实现手写数字识别的示例：

1. 导入所需的库和模块

import tensorflow as tf
from tensorflow.keras import layers, models

2. 加载MNIST数据集并进行预处理

mnist = tf.keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images, test_images = train_images / 255.0, test_images / 255.0

3. 构建多层感知分类器模型

model = models.Sequential([
    layers.Flatten(input_shape=(28, 28)),
    layers.Dense(128, activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(10, activation='softmax')
])

4. 编译模型并训练

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5)

5. 评估模型性能

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
print('\nTest accuracy:', test_acc)