CODEWITHSUNDEEP
pythontensorflow
Jabez
Jabez

Reputation: 377

Tensorflow Basics - Computing cumulative moving average

I've just started with Tensorflow and wanted to know if the below code is right approach to compute cumulative rolling average

import tensorflow as tf
import numpy as np

x = tf.Variable(0, name = "x")
x_pld = tf.placeholder(tf.int32)
update_x = tf.assign(x, x_pld)

curr_avg = tf.Variable(tf.constant(0.), name="curr_avg")
avg_pld = tf.placeholder("float")
update_avg = tf.assign(curr_avg, avg_pld)

# Initalize 
init_op = tf.initialize_all_variables()

with tf.Session() as session:
    session.run(init_op)

    for i in range(5):  
        temp_avg = session.run(curr_avg)
        session.run(update_x, feed_dict={x_pld: np.random.randint(1000)})
        new_x = session.run(x)
        print(new_x) 
        session.run(update_avg, feed_dict={avg_pld: ((temp_avg * (i)) + new_x)/(i+1)})

    print(session.run(curr_avg))

Upvotes: 2

Views: 2337

Answers (1)

rrao
rrao

Reputation: 641

import numpy as np
import tensorflow as tf

# Set Variables
# only need single placeholder because only feeding in one value to graph
next_val = tf.placeholder(shape=(), dtype=tf.float32) 
cumulative = tf.Variable(0, dtype=tf.float32)
divisor = tf.Variable(0, dtype=tf.float32)

#Calculations
cumulative = cumulative.assign_add(next_val)
divisor = divisor.assign_add(1)
avg = tf.div(cumulative, divisor)

with tf.Session() as session:
    tf.initialize_all_variables().run() # run initialization of variables in graph

    for i in range(5):
        new_val = np.random.randint(1000)
        # run graph ops once, and return the result of avg
        curr_avg = session.run([avg], feed_dict={next_val: new_val})

        print "next value added: {}".format(new_val) # allows you to verify output
        print "rolling average: {}".format(curr_avg)

Upvotes: 2

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