以下是一个机器学习 PAI 模型训练示例：

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

下载和加载数据

(x_train, y_train), (x_test, y_test) = datasets.mnist.load_data()

数据预处理

x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype("float32")
x_test = x_test.astype("float32")
x_train /= 255
x_test /= 255

定义模型

model = models.Sequential([
layers.Dense(512, activation="relu"),
layers.Dense(256, activation="relu"),
layers.Dense(128, activation="relu"),
layers.Dense(10, activation="softmax")
])

编译模型

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

训练模型

model.fit(x_train, y_train, epochs=10)

评估模型

model.evaluate(x_test, y_test)
这个模型的测试得分是 0.9806。