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]
|train_X||np array with x dimension of information|
|train_Y||np array with y dimension of information|
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:
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: