improved graph

image.png

#coding=utf-8
import tensorflow as tf
import numpy as np

graph = tf.Graph()
with graph.as_default():
    with tf.name_scope("variables"):
        global_step = tf.Variable(0, dtype = tf.int32, trainable = False, name = "global_step")
        total_output = tf.Variable(0.0, dtype = tf.float32, trainable = False, name = "total_output")

    with tf.name_scope("transfromation"):
        with tf.name_scope("input"):
            a = tf.placeholder(tf.float32, shape = [None], name = "input_placeholder_a")
        with tf.name_scope("intermediate_layer"):
            b = tf.reduce_prod(a, name = "product_b")
            c = tf.reduce_sum(a, name = "sum_c")
        with tf.name_scope("output"):
            output = tf.add(b, c, name = "output")

    with tf.name_scope("update"):
        update_total = total_output.assign_add(output)
        increment_step = global_step.assign_add(1)

        with tf.name_scope("summaries"):
            avg = tf.div(update_total, tf.cast(increment_step, tf.float32), name = "average")
            tf.scalar_summary(b'Output', output, name = "output_summary")
            tf.scalar_summary(b'Sum of outputs over time', update_total, name = "total_summary")
            tf.scalar_summary(b'Average of outputs over time', avg, name = "average_summary")
        with tf.name_scope("global_ops"):
            init = tf.initialize_all_variables()
            merged_summaries = tf.merge_all_summaries()

sess = tf.Session(graph = graph)
writer = tf.train.SummaryWriter('./improved_graph', graph)
sess.run(init)

def run_graph(input_tensor):
    feed_dict = {a: input_tensor}
    _, step, summary = sess.run([output, increment_step, merged_summaries], feed_dict = feed_dict)
    writer.add_summary(summary, global_step = step)

run_graph([2, 8])
run_graph([3, 1, 3, 3])
run_graph([8])
run_graph([1, 2, 3])
run_graph([11, 4])
run_graph([4, 1])
run_graph([7, 3, 1])
run_graph([6, 3])
run_graph([0, 2])
run_graph([4, 5, 6])

writer.flush()
writer.close()
sess.close()