# Import MINST data

import input_data

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

import tensorflow as tf

# Parameters

learning_rate = 0.001

training_iters = 100000

batch_size = 128

display_step = 10

#Network Parameters

n_input = 784 #MNIST data input

n_classes = 10 #MNIST total classes

dropout = 0.75

# Create model

x = tf.placeholder(tf.types.float32, [None, n_input])

y = tf.placeholder(tf.types.float32, [None, n_classes])

keep_prob = tf.placeholder(tf.types.float32) #dropout

def conv2d(img, w, b):

return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(img, w, strides=[1, 1, 1, 1], padding='SAME'),b))

def max_pool(img, k):

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

def conv_net(_X, _weights, _biases, _dropout):

_X = tf.reshape(_X, shape=[-1, 28, 28, 1])

conv1 = conv2d(_X, _weights['wc1'], _biases['bc1'])

conv1 = max_pool(conv1, k=2)

conv1 = tf.nn.dropout(conv1, _dropout)

conv2 = conv2d(conv1, _weights['wc2'], _biases['bc2'])

conv2 = max_pool(conv2, k=2)

conv2 = tf.nn.dropout(conv2, _dropout)

dense1 = tf.reshape(conv2, [-1, _weights['wd1'].get_shape().as_list()[0]])

dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'])

dense1 = tf.nn.dropout(dense1, _dropout)

out = tf.matmul(dense1, _weights['out']) + _biases['out']

return out

weights = {

'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),

'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),

'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),

'out': tf.Variable(tf.random_normal([1024, 10]))

}

biases = {

'bc1': tf.Variable(tf.random_normal([32])),

'bc2': tf.Variable(tf.random_normal([64])),

'bd1': tf.Variable(tf.random_normal([1024])),

'out': tf.Variable(tf.random_normal([n_classes]))

}

pred = conv_net(x, weights, biases, keep_prob)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

#Evaluate model

correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))

accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.types.float32))

# Train

init = tf.initialize_all_variables()

with tf.Session() as sess:

sess.run(init)

step = 1

while step * batch_size < training_iters:

batch_xs, batch_ys = mnist.train.next_batch(batch_size)

sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})

if step % display_step == 0:

acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})

loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})

print "Iter " + str(step*batch_size) + ", Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)

step += 1

print "Optimization Finished!"

#Accuracy on 256 mnist test images

print "Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.})