tensorflow 自己手动实现的线性回归

tensorflow 自己手动实现的线性回归

#!/usr/bin/python
# -*- coding:utf-8 -*-

import tensorflow as tf
import os

# 第二个参数是默认值
tf.app.flags.DEFINE_integer(\"max_iter\", 100, \"迭代次数\")
tf.app.flags.DEFINE_string(\"model_dir\", \"./tmp/ckpt/model\", \"模型路径\")
tf.app.flags.DEFINE_string(\"summary_dir\", \"./tmp/test/\", \"graph路径\")
tf.app.flags.DEFINE_string(\"checkpoint_dir\", \"./tmp/ckpt/checkpoint\", \"模型路径\")

FLAGS=tf.app.flags.FLAGS
def mylineregression():
    with tf.variable_scope(\"data\"):
        x=tf.random_normal([100,1],0.0,1.0)
        y=tf.multiply(x,[[0.7]])+0.8
    with tf.variable_scope(\"model\"):
        weight=tf.Variable(tf.random_normal([1,1],0.0,1.0))
        bias=tf.Variable(0.0)
        y_predict=tf.multiply(x,weight)+bias
    with tf.variable_scope(\"loss\"):
        loss=tf.reduce_mean(tf.square(y-y_predict))
    with tf.variable_scope(\"optimizer\"):
        train_op=tf.train.GradientDescentOptimizer(0.1).minimize(loss)


    init_value=tf.global_variables_initializer()

    saver=tf.train.Saver()

    tf.summary.scalar(\"losses\",loss)
    tf.summary.histogram(\"weight\",weight)
    tf.summary.histogram(\"bias\",bias)

    merged=tf.summary.merge_all()

    with tf.Session() as sess:
        sess.run(init_value)
        filwriter=tf.summary.FileWriter(FLAGS.summary_dir, graph=sess.graph)
        # print(sess.run([weight,bias]))
        # 加载模型，覆盖变量的值
        if os.path.exists(FLAGS.checkpoint_dir):
            saver.restore(sess,FLAGS.model_dir)
        for i in range(FLAGS.max_iter):
            print(\"第%d次训练参数weight:%f,bias:%f\"%(i,weight.eval(),bias.eval()))
            # print(y_predict.eval())
            summary=sess.run(merged)
            filwriter.add_summary(summary,i)
            sess.run(train_op)

        tf.summary.FileWriter(FLAGS.summary_dir,graph=sess.graph)
        # 保存模型
        # saver.save(sess,\"./tmp/ckpt/model\")


    return None
if __name__ == \'__main__\':
    print(\"hello\")
    mylineregression()