Here we report the complete source code for the softmax classifier:

import tensorflow as tf 
import mnist_data 
import matplotlib.pyplot as plt 
from random import randint 
import numpy as np 
 
logs_path = 'log_mnist_softmax' 
batch_size = 100 
learning_rate = 0.5 
training_epochs = 10 
 
mnist = mnist_data.read_data_sets("data") 
 
 
X = tf.placeholder(tf.float32, [None, 28, 28, 1],name="input") 
Y_ = tf.placeholder(tf.float32, [None, 10]) 
W = tf.Variable(tf.zeros([784, 10])) 
b = tf.Variable(tf.zeros([10])) 
XX = tf.reshape(X, [-1, 784]) 
 
Y = tf.nn.softmax(tf.matmul(XX, W) + b,name="output") 
cross_entropy = -tf.reduce_mean(Y_ * tf.log(Y)) * 1000.0 
correct_prediction = tf.equal(tf.argmax(Y, 1), 
                              tf.argmax(Y_, 1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 
                                  tf.float32)) 
train_step = tf.train.GradientDescentOptimizer 
                           (0.005).minimize(cross_entropy) 

tf.summary.scalar("cost", cross_entropy)tf.summary.scalar("accuracy", accuracy)summary_op = tf.summary.merge_all()
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
    for epoch in range(training_epochs):
        batch_count = int(mnist.train.num_examples/batch_size)
        for i in range(batch_count):
            batch_x, batch_y = mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={X: batch_x,
                                           Y_: batch_y})
       
        print "Epoch: ", epoch
             
    print "Accuracy: ", accuracy.eval 
          (feed_dict={X: mnist.test.images, 
                      Y_: mnist.test.labels}) 
    print "done" 
     
    num = randint(0, mnist.test.images.shape[0])  
    img = mnist.test.images[num]  
  
    classification = sess.run(tf.argmax(Y, 1),  
                               feed_dict={X: [img]})  
    print 'Neural Network predicted', classification[0]  
    print 'Real label is:', np.argmax(mnist.test.labels[num])  
 
    saver = tf.train.Saver() 
    save_path = saver.save(sess, "saved_mnist_cnn.ckpt") 
    print("Model saved to %s" % save_path)