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

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

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

W = tf.Variable(tf.zeros([784,10]),name="weights")
b = tf.Variable(tf.zeros([10]),name="biases")
y = tf.matmul(x,W) + b

session = tf.InteractiveSession()
train_writer = tf.summary.FileWriter("/tmp/mnist_logs/train",session.graph)
session.run(tf.global_variables_initializer())

xent = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(xent)

for _ 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}))


