import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np

mnist = input_data.read_data_sets('MNIST_data', one_hot = True)
sess = tf.InteractiveSession()

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

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

sess.run(tf.global_variables_initializer())

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

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y,y_))

#train the model
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

for i in range(1000):
    batch = mnist.train.next_batch(100)
    train_step.run(feed_dict={x:batch[0], y_:batch[1]})

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

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

print(accuracy.eval(feed_dict={x:mnist.test.images,y_:mnist.test.labels}))

