# 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})