We reported for the entire source code for the example previously described is as follows:

import tensorflow as tf 

input_value = tf.constant(0.5,name="input_value") 
weight = tf.Variable(1.0,name="weight") 
expected_output = tf.constant(0.0,name="expected_output") 
model = tf.multiply(input_value,weight,"model")loss_function = tf.pow(expected_output - model,2,name="loss_function") 
optimizer = tf.train.GradientDescentOptimizer(0.025).minimize(loss_function) 

for value in [input_value,weight,expected_output,model,loss_function]: 
    tf.summary.scalar(value.op.name,value) 
summaries = tf.summary.merge_all()sess = tf.Session() 
summary_writer = tf.summary.FileWriter('log_simple_stats',sess.graph) 
sess.run(tf.global_variables_initializer())
for i in range(100): 
    summary_writer.add_summary(sess.run(summaries),i) 
    sess.run(optimizer)