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

# fetch dataset

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

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)

# train

x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

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

y_ = tf.placeholder(tf.float32, [None, 10])

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

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

init = tf.initialize_all_variables()

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

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})

# test 

correct_prediction = tf.equal( tf.argmax(y,1), tf.argmax(y_,1) )
accuracy = tf.reduce_mean( tf.cast(correct_prediction, tf.float32) )
print sess.run(accuracy, feed_dict = {x: mnist.test.images, y_: mnist.test.labels})

