Simple linear regression structure in TensorFlow with Python

From WikiOD

A model widely used in traditional statistics is the linear regression model. In this article, the objective is to follow the step-by-step implementation of this type of models. We are going to represent a simple linear regression structure.

For our study, we will analyze the age of the children on the x axis and the height of the children on the y axis. We will try to predict the height of the children, using their age, applying simple linear regression.[in TF finding the best W and b]

Parameters[edit | edit source]

Parameter Description
train_X np array with x dimension of information
train_Y np array with y dimension of information

Remarks[edit | edit source]

I used TensorBoard sintaxis to track the behavior of some parts of the model, cost, train and activation elements.

with tf.name_scope("") as scope:

Imports used:

import numpy as np
import tensorflow as tf

Type of application and language used:

I have used a traditional console implementation app type, developed in Python, to represent the example.

Version of TensorFlow used:


Conceptual academic example/reference extracted from here:

Simple regression function code structure[edit | edit source]

Function definition:

def run_training(train_X, train_Y):

Inputs variables:

X = tf.placeholder(tf.float32, [m, n])
Y = tf.placeholder(tf.float32, [m, 1])

Weight and bias representation

W = tf.Variable(tf.zeros([n, 1], dtype=np.float32), name="weight")
b = tf.Variable(tf.zeros([1], dtype=np.float32), name="bias")

Lineal Model:

with tf.name_scope("linear_Wx_b") as scope:
    activation = tf.add(tf.matmul(X, W), b)


with tf.name_scope("cost") as scope:
    cost = tf.reduce_sum(tf.square(activation - Y)) / (2 * m)
    tf.summary.scalar("cost", cost)


with tf.name_scope("train") as scope:
    optimizer = tf.train.GradientDescentOptimizer(0.07).minimize(cost)

TensorFlow session:

with tf.Session() as sess:
    merged = tf.summary.merge_all()
    writer = tf.summary.FileWriter(log_file, sess.graph)

Note: merged and writer are part of the TensorBoard strategy to track the model behavior.

    init = tf.global_variables_initializer()

Repeating 1.5k times the training loop:

    for step in range(1500):
       result, _ =[merged, optimizer], feed_dict={X: np.asarray(train_X), Y: np.asarray(train_Y)})
       writer.add_summary(result, step)

Print Training Cost:

    training_cost =, feed_dict={X: np.asarray(train_X), Y: np.asarray(train_Y)})
    print "Training Cost: ", training_cost, "W=",, "b=",, '\n'

Concrete prediction based on the model trained:

    print "Prediction for 3.5 years"
    predict_X = np.array([3.5], dtype=np.float32).reshape([1, 1])

    predict_X = (predict_X - mean) / std
    predict_Y = tf.add(tf.matmul(predict_X, W), b)
    print "Child height(Y) =",

Main Routine[edit | edit source]

def main():
    train_X, train_Y = read_data()
    train_X = feature_normalize(train_X)
    run_training(train_X, train_Y)

Note: remember review functions dependencies. read_data, feature_normalize and run_training

Normalization Routine[edit | edit source]

def feature_normalize(train_X):
    global mean, std
    mean = np.mean(train_X, axis=0)
    std = np.std(train_X, axis=0)

    return np.nan_to_num((train_X - mean) / std)

Read Data routine[edit | edit source]

def read_data():
    global m, n

    m = 50
    n = 1

    train_X = np.array(

Internal data for the array


    train_Y = np.array(

Internal data for the array


    return train_X, train_Y

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