def linear_regression():
    """
    自实现一个线性回归
    :return:
    """
    with tf.compat.v1.variable_scope("prepare_data"):
        # 1）准备数据
        X = tf.compat.v1.random_normal(shape=[100, 1], name="feature")
        y_true = tf.matmul(X, [[0.8]]) + 0.7

    with tf.compat.v1.variable_scope("create_model"):
        # 2）构造模型
        # 定义模型参数 用 变量
        weights = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1, 1]), name="Weights")
        bias = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1, 1]), name="Bias")
        y_predict = tf.matmul(X, weights) + bias

    with tf.compat.v1.variable_scope("loss_function"):
        # 3）构造损失函数
        error = tf.reduce_mean(tf.square(y_predict - y_true))

    with tf.compat.v1.variable_scope("optimizer"):
        # 4）优化损失
        optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.01).minimize(error)

    # 2_收集变量
    tf.summary.scalar("error", error)
    tf.summary.histogram("weights", weights)
    tf.summary.histogram("bias", bias)

    # 3_合并变量
    merged = tf.compat.v1.summary.merge_all()

    # 创建Saver对象
    saver = tf.compat.v1.train.Saver()

    # 显式地初始化变量
    init = tf.compat.v1.global_variables_initializer()

    # 开启会话
    with tf.compat.v1.Session() as sess:
        # 初始化变量
        sess.run(init)

        # 1_创建事件文件
        file_writer = tf.compat.v1.summary.FileWriter("./tmp/linear", graph=sess.graph)

        # 查看初始化模型参数之后的值
        print("训练前模型参数为：权重%f，偏置%f，损失为%f" % (weights.eval(), bias.eval(), error.eval()))

        # 开始训练
        # for i in range(100):
        #     sess.run(optimizer)
        #     print("第%d次训练后模型参数为：权重%f，偏置%f，损失为%f" % (i+1, weights.eval(), bias.eval(), error.eval()))
        #
        #     # 运行合并变量操作
        #     summary = sess.run(merged)
        #     # 将每次迭代后的变量写入事件文件
        #     file_writer.add_summary(summary, i)
        #
        #     # 保存模型
        #     if i % 10 ==0:
        #         saver.save(sess, "./tmp/model/my_linear.ckpt")
        # 加载模型
        if os.path.exists("./tmp/model/checkpoint"):
            saver.restore(sess, "./tmp/model/my_linear.ckpt")

        print("训练后模型参数为：权重%f，偏置%f，损失为%f" % (weights.eval(), bias.eval(), error.eval()))


    return None

# 1）定义命令行参数
tf.compat.v1.app.flags.DEFINE_integer("max_step", 100, "训练模型的步数")
tf.compat.v1.app.flags.DEFINE_string("model_dir", "Unknown", "模型保存的路径+模型名字")

# 2）简化变量名
FLAGS = tf.compat.v1.app.flags.FLAGS

def command_demo():
    """
    命令行参数演示
    :return:
    """
    print("max_step:\n", FLAGS.max_step)
    print("model_dir:\n", FLAGS.model_dir)

    return None