# Import MINST data

import input_data

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

import tensorflow as tf

# Parameters

learning_rate = 0.01

training_epochs = 25

batch_size = 100

display_step = 1

# Create model

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

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

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

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

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

cost = -tf.reduce_sum(y*tf.log(activation)) #cross entropy

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

# Train

init = tf.initialize_all_variables()

with tf.Session() as sess:

sess.run(init)

for epoch in range(training_epochs):

avg_cost = 0.

total_batch = int(mnist.train.num_examples/batch_size)

for i in range(total_batch):

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

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

avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch

if epoch % display_step == 0:

print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)

print "Optimization Finished!"

# Test trained model

correct_prediction = tf.equal(tf.argmax(activation,1), tf.argmax(y,1))

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

print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})