tf_summary_demo_1.py

import numpy as np
import tensorflow as tf

def add_values(num_list):
    # making a list of tensors.
    tensor_list = tf.constant(num_list, shape=[1, 3], name='tensor_list')
    tensor_ten = tf.constant([10] * len(num_list), \
                shape=[len(num_list), 1], name='tensor_ten')
    # the below statement declares a scope under which the 
    # variables will be used.
    with tf.name_scope('number_layer1'):
        tensor_mean = tf.reduce_mean(tensor_list)
        tensor_val = tf.matmul(tensor_list, tensor_ten)
        tf.summary.scalar('tensor_mean', tensor_mean)
        # the below layer is like a nested layer in the above one,
        # and hence will also be the same way in the graph.
        with tf.name_scope('number_layer2'):
            result_tensor = tf.reduce_sum(tensor_val)
            tf.summary.scalar('result_tensor', result_tensor)
    return result_tensor


# initializing our graph
with tf.Graph().as_default():
    init = tf.global_variables_initializer()
    # file writer initialization.
    writer = tf.summary.FileWriter('./tf_summary/1')
    with tf.Session() as sess:
        sess.run(init)
        sess.run(add_values([10, 20, 30]))
        writer.add_graph(sess.graph)
        # this function saves from writing all summaries individually to
        # the file writer.
        merged = tf.summary.merge_all()
        # calculate the summaries.
        s = sess.run(merged)
        # write them.
        writer.add_summary(s)