In this tutorial, we will introduce the way to implement linear regression in tensorflow, this is the basic of deep learning.

1. Import libraries

import numpy as np 
import tensorflow as tf 
import matplotlib.pyplot as plt

2. Set random seed

np.random.seed(10) 
tf.set_random_seed(10)

3. Create train data

x = np.linspace(0, 50, 50) 
y = np.linspace(0, 50, 50) 
  
# Adding noise to the random linear data 
x += np.random.uniform(-4, 4, 50) 
y += np.random.uniform(-4, 4, 50) 
  
n = len(x) # Number of data points

Then we will start to implement linear regression to fit data above.

4. Define tensorflow variables

X = tf.placeholder("float") 
Y = tf.placeholder("float") 

W = tf.Variable(np.random.randn(), name = "W") 
b = tf.Variable(np.random.randn(), name = "b")

5. Set learning rate and epochs

learning_rate = 0.01
training_epochs = 1000

6. Build linear regression to train

# Hypothesis 
y_pred = tf.add(tf.multiply(X, W), b) 
  
# Mean Squared Error Cost Function 
cost = tf.reduce_sum(tf.pow(y_pred-Y, 2)) / (2 * n) 
  
# Gradient Descent Optimizer 
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) 
  
# Global Variables Initializer 
init = tf.global_variables_initializer()

7. Start to train model

# Starting the Tensorflow Session 
with tf.Session() as sess: 
      
    # Initializing the Variables 
    sess.run(init) 
      
    # Iterating through all the epochs 
    for epoch in range(training_epochs): 
          
        # Feeding each data point into the optimizer using Feed Dictionary 
        for (_x, _y) in zip(x, y): 
            sess.run(optimizer, feed_dict = {X : _x, Y : _y}) 
          
        # Displaying the result after every 50 epochs 
        if (epoch + 1) % 50 == 0: 
            # Calculating the cost a every epoch 
            c = sess.run(cost, feed_dict = {X : x, Y : y}) 
            print("Epoch", (epoch + 1), ": cost =", c, "W =", sess.run(W), "b =", sess.run(b)) 
      
    # Storing necessary values to be used outside the Session 
    training_cost = sess.run(cost, feed_dict ={X: x, Y: y}) 
    weight = sess.run(W) 
    bias = sess.run(b)

Then you will get the best W and b.