from tensorflow.examples.tutorials.mnist import input_data
#MNIST data is split into 3 parts: 55000 data points of training data, 1000 of test data, 5000 of validation data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

import tensorflow as tf

x = tf.placeholder(tf.float32, [None,784])

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

#y is the predicted probability distribution
y = tf.nn.softmax(tf.matmul(x,W)+ b)

#y_ is the true distribution
y_ = tf.placeholder(tf.float32, [None, 10])

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices = [1]))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)

#training 1000 times
for i in range(1000):
    batch_xs = batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict = {x: batch_xs, y: batch_ys})

#testing
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction , tf.float32))

#printing accuracy
print sess.run(accuracy, feed_dict = {x: mnist.test.images, y_: mnist.test.labels})
