import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import tensorflow as tf

#tensorboard --logdir="./"

def linearregression():

    with tf.variable_scope("original_data"):

        X = tf.random_normal([100,1],mean=0.0,stddev=1.0)

        y_true = tf.matmul(X,[[0.8]]) + [[0.7]]

    with tf.variable_scope("linear_model"):

        weights = tf.Variable(initial_value=tf.random_normal([1,1]))

        bias = tf.Variable(initial_value=tf.random_normal([1,1]))

        y_predict = tf.matmul(X,weights)+bias

    with tf.variable_scope("loss"):

        loss = tf.reduce_mean(tf.square(y_predict-y_true))

    with tf.variable_scope("optimizer"):

        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss)

    #收集观察张量

    tf.summary.scalar("losses",loss)

    tf.summary.histogram("weight",weights)

    tf.summary.histogram("biases",bias)

    #合并收集的张量

    merge = tf.summary.merge_all()

    init = tf.global_variables_initializer()

    saver = tf.train.Saver()

    with tf.Session() as sess:

        sess.run(init)

        print(weights.eval(),bias.eval())

        # 模型加载

        saver.restore(sess,"./model/linearregression")

        print(weights.eval(),bias.eval())

        # filewriter = tf.summary.FileWriter("./tmp",graph=sess.graph)

        # for i in range(1000):

        #     sess.run(optimizer)

        #     print("loss:", sess.run(loss))

        #     print("weight:", sess.run(weights))

        #     print("bias:", sess.run(bias))

        #     summary = sess.run(merge)

        #     filewriter.add_summary(summary,i)

        #

        # #checkpoint文件，模型保存

        # saver.save(sess,"./model/linearregression")

if __name__ == '__main__':

    linearregression()