import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
sess = tf.InteractiveSession()
x = tf.placeholder("float", [None, 784])
y_ = tf.placeholder("float", [None, 10])
W = tf.Variable(tf.zeros([784, 10]), name = "W")
b = tf.Variable(tf.zeros([10]), name = "b")
sess.run(tf.initialize_all_variables())
y = tf.nn.softmax(tf.matmul(x, W) + b)
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.ProximalGradientDescentOptimizer(0.01).minimize(cross_entropy)
for i in range(10000):
    batch = mnist.train.next_batch(1)
    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, "float"))
print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
