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

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

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("float", [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 id in range(1000):
    batch_xs, batch_ys=mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
    if id%100==0:
        writer=tf.summary.FileWriter("D:\\minist_nn_log",sess.graph)
        writer.close()


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