# Import the MINST dataset
from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_ ("/tmp/data/", one_hot=True)

import tensorflow as tf 
import matplotlib.pyplot as plt 
# Parameters 
learning_rate = 0.01 
training_epochs = 25 
batch_size = 100 
display_step = 1 

# tf Graph Input 
x = tf.placeholder("float", [none, 784]) # MNIST data image of shape 28*28 = 784 
y = tf.placeholder("float", [none, 10]) # 0-9 digits recognition => 10 classes 
# Create model 
# Set model weights 
W = tf.Variable(tf.zeros([784, 10])) 
b = tf.Variable(tf.zeros([10])) 
# Constructing the model
activation=tf.nn.softmaxx(tf.matmul (x, W)+b) # Softmax
 of function 
# Minimizing error using cross entropy
cross_entropy = y*tf.log(activation) 
cost = tf.reduce_mean\ (-tf.reduce_sum\ (cross_entropy, reduction_indice = 1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
#Plot settings 
avg_set = [] 
epoch_set = [] 
# Initializing the variables where init = tf.initialize_all_variables()
# Launching the graph
with tf.Session() as sess:
   sess.run(init)
   
# Training of the cycle in  the dataset
   for epoch in range(training_epochs):
      avg_cost = 0.
      total_batch = int(mnist.train.num_example/batch_size)
      
# Creating loops at all the batches in the code
      for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
         # Fitting the training by the batch data sess.run(optimizr,  feed_dict = {
x: batch_xs, y: batch_ys})
 # Compute all the average of loss avg_cost += sess.run(cost, \ feed_dict = {
x: batch_xs, \ y: batch_ys}) //total batch
      # Display the logs at each epoch steps 
      if epoch % display_step==0: 
      print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format (avg_cost))
            avg_set.append(avg_cost) epoch_set.append(epoch+1)
   print ("Training phase finished")
    
   plt.plot(epoch_set,avg_set, 'o', label = 'Logistics Regression Training')   
   plt.ylabel('cost') 
   plt.xlabel('epoch') 
   plt.legend() 
   plt.show() 
    
# Test the model
   correct_prediction = tf.equal (tf.argmax (activation, 1),tf.argmax(y,1)) 
  
# Calculating the accuracy of dataset
accuracy = tf.reduce_mean(tf.cast (correct_prediction, "float")) print 
("Model accuracy:", accuracy.eval({x:mnist.test.images, y: mnist.test.labels}))