For performing linear regression, we will do the following :
1. Create the linear regression computational graph output. This means we will accept an input, x, and generate the output, Ax + b.
2. We create a loss function, the L2 loss, and use that output with the learning rate to compute the gradients of the model variables, A and B to minimize the loss.
Import tensorflow as tf
# Creating variable for parameter slope (W) with initial value as 0.4
W = tf.Variable([.4], tf.float32)
#Creating variable for parameter bias (b) with initial value as -0.4
b = tf.Variable([-0.4], tf.float32)
# Creating placeholders for providing input or independent variable, denoted by x
x = tf.placeholder(tf.float32)
# Equation of Linear Regression
linear_model = W * x + b
# Initializing all the variables
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
# Running regression model to calculate the output w.r.t. to provided x values
print(sess.run(linear_model {x: [1, 2, 3, 4]}))
