#load MNIST data

 import tensorflow.examples.tutorials.mnist.input_data as input_data

 mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)

 #start tensorflow interactiveSession

 import tensorflow as tf

 sess = tf.InteractiveSession()

 #weight initilization

 def weight_variable(shape):

     initial = tf.truncated_normal(shape, stddev=0.1)

     return tf.Variable(initial)

 def bias_variable(shape):

     initial = tf.constant(0.1, shape= shape)

     return tf.Variable(initial)

 #convolution

 def conv2d(x, W):

     return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')

 #pooling

 def max_pool_2x2(x):

     return tf.nn.max_pool(x, ksize=[1,2,2,1],strides=[1,2,2,1], padding='SAME')

 #Create the model

 #placeholder

 x = tf.placeholder("float",[None, 784])

 y_ = tf.placeholder("float", [None, 10])

 #variable

 W = tf.Variable(tf.zeros([784,10]))

 b = tf.Variable(tf.zeros([10]))

 y = tf.nn.softmax(tf.matmul(x,W) +b)

 #first convolutional layer

 w_conv1 = weight_variable([5,5,1,32])

 b_conv1 = bias_variable([32])

 x_image = tf.reshape(x,[-1,28,28,1])

 h_conv1 =tf.nn.relu(conv2d(x_image,w_conv1) + b_conv1)

 h_pool1 =max_pool_2x2(h_conv1)

 #second convolutional layer

 w_conv2 = weight_variable([5,5,32,64])

 b_conv2 = bias_variable([64])

 h_conv2 =tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2)

 h_pool2 =max_pool_2x2(h_conv2)

 #densely connected layer

 w_fc1 = weight_variable([7*7*64, 1024])

 b_fc1 = bias_variable([1024])

 h_pool2_flat = tf.reshape(h_pool2, [-1,7*7*64])

 h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)

 #dropout

 keep_prob = tf.placeholder("float")

 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

 #readout layer

 w_fc2 = weight_variable([1024,10])

 b_fc2 = bias_variable([10])

 y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop,w_fc2) + b_fc2)

 #train and evaluate the model

 cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))

 #train_step = tf.train.AdagradOptimizer(1e-4).minimize(cross_entropy)

 train_step = tf.train.GradientDescentOptimizer(1e-4).minimize(cross_entropy)

 correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))

 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

 sess.run(tf.initialize_all_variables())

 for i in range(5000):

     batch = mnist.train.next_batch(50)

     if i%100 ==0:

         train_accuracy = accuracy.eval(feed_dict={x:batch[0],y_:batch[1], keep_prob:1.0})

         print "step %d, train accuracy %g " %(i,train_accuracy)

     train_step.run(feed_dict={x:batch[0],y_:batch[1], keep_prob:0.5})

 print "test accuracy %g" % accuracy.eval(fedd_dict={x:mnist.test.images, y_:mnist.test.labels, keep_prob:1.0})