源代码地址：https://github.com/w1449550206/Linear-regression-with-tensorflow.git

import tensorflow as tf

with tf.variable_scope("lr_model"):
    def linear_regression():#定义名字
        # 1）准备好数据集：y = 0.8x + 0.7 100个样本
        # 特征值X, 目标值y_true
        with tf.variable_scope("original_data"):#定义名字
            X = tf.random_normal(shape=(100, 1), mean=2, stddev=2, name="original_data_x")
            # y_true [100, 1]
            # 矩阵运算 X（100， 1）* （1, 1）= y_true(100, 1)
            y_true = tf.matmul(X, [[0.8]], name="original_matmul") + 0.7

        # 2）建立线性模型：
        # y = W·X + b，目标：求出权重W和偏置b
        # 3）随机初始化W1和b1

        with tf.variable_scope("linear_model"):#定义名字
            weights = tf.Variable(initial_value=tf.random_normal(shape=(1, 1)), name="weights")
            bias = tf.Variable(initial_value=tf.random_normal(shape=(1, 1)), name="bias")
            y_predict = tf.matmul(X, weights, name="model_matmul") + bias

        # 4）确定损失函数（预测值与真实值之间的误差）-均方误差
        with tf.variable_scope("loss"):#定义名字
            error = tf.reduce_mean(tf.square(y_predict - y_true), name="error_op")

        # 5）梯度下降优化损失：需要指定学习率（超参数）
        # W2 = W1 - 学习率*(方向)
        # b2 = b1 - 学习率*(方向)
        with tf.variable_scope("gd_optimizer"):#定义名字
            optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01, name="optimizer").minimize(error)

        # 2）收集变量
        tf.summary.scalar("error", error)##生成准确率标量图
        tf.summary.histogram("weights", weights)#用来显示直方图信息
        tf.summary.histogram("bias", bias)#用来显示直方图信息

        # 3）合并变量
        merge = tf.summary.merge_all()

        # 初始化变量
        init = tf.global_variables_initializer()
        # 开启会话进行训练
        with tf.Session() as sess:
            # 运行初始化变量Op
            sess.run(init)
            print("随机初始化的权重为%f， 偏置为%f" % (weights.eval(), bias.eval()))
            # 1）创建事件文件【重要】
            file_writer = tf.summary.FileWriter(logdir="./summary", graph=sess.graph)
            # 训练模型
            for i in range(100):
                sess.run(optimizer)
                print("第%d步的误差为%f，权重为%f， 偏置为%f" % (i, error.eval(), weights.eval(), bias.eval()))
                # 4）运行合并变量op
                summary = sess.run(merge)
                file_writer.add_summary(summary, i)

        return None

if __name__ == '__main__':
    linear_regression()

<mark>可视化：</mark>