import tensorflow as tf
from tensorflow.examples.tutorial.mnist import input_data

mnist = input_data_sets('MNIST_data', one_hot=True)

batch_size = 100
n_batch = mnist.train.num_examples // batch_size

x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])


W = tf.Variable(tf.zeros[784, 10])
b = tf.Variable(tf.zeros([10]))
prediction = tf.nn.softmax(tf.matmul(x, W) + b)

#loss = tf.reduce_mean(tf.square(y-prediction))

train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

init = tf.global_variables_initializer()

correct_predition = 
    tf.equal(tf.argmax(y, 1), tf.argmax(prediciton, 1))

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

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(21):
        for batch in range(n_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})

        acc = sess.run(accuracy, feed_dict={x:mnist.teset.images,y:mnist.test.labels})
        print('Iter' + str(epoch) + 'Testing Accuracy' + str(acc))



