tensorflow实现mlp的代码

可以参考以下代码：

import tensorflow as tf

# 定义 MLP 模型
class MLP(tf.keras.Model):
    def __init__(self):
        super(MLP, self).__init__()
        self.flatten = tf.keras.layers.Flatten() # 将输入数据展平
        self.dense1 = tf.keras.layers.Dense(units=128, activation=tf.nn.relu)
        self.dense2 = tf.keras.layers.Dense(units=10)

    def call(self, inputs):
        x = self.flatten(inputs)
        x = self.dense1(x)
        x = self.dense2(x)
        return x

# 加载数据集，并进行预处理
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()
train_images, test_images = train_images / 255.0, test_images / 255.0

# 训练模型
model = MLP()
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))

# 在测试集上评估模型
test_loss, test_acc = model.evaluate(test_images, test_labels)
print("Test accuracy:", test_acc)