code for stochastic training on python using Tensorflow

stochastic_training.py

#Load our libraries
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
sess = tf.Session()

# We create our data, placeholders and variables
x_val = np.random.normal(1, 0.1, 100)   #input values
y_val = np.repeat(10., 100)   #target values

# placeholders
x_data = tf.placeholder(shape=[1], dtype=tf.float32)
y_target = tf.placeholder(shape=[1], dtype=tf.float32)

#Variables
w = tf.Variable(0.)

# Add linear function to the computational graph
y_pred = tf.multiply(x_data, w)

# Add a loss function(L2 norm)
loss = tf.square(y_pred - y_target)

# initialize our variables
init = tf.global_variables_initializer()
sess.run(init)