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

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
nb_classes = 10

X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, nb_classes])
W = tf.get_variable("weight", shape=[784, nb_classes], initializer=tf.contrib.layers.xavier_initializer())
b = tf.Variable(tf.random.normal([nb_classes]))

H = tf.nn.softmax(tf.matmul(X, W) + b)

cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(H), axis = 1))
optimizer = tf.train.AdamOptimizer(learning_rate=0.005).minimize(cost)

is_correct = tf.equal(tf.arg_max(H, 1), tf.arg_max(Y, 1))
accuarcy = tf.reduce_mean(tf.cast(is_correct, tf.float32))

training_epochs = 14
batch_size = 100

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(training_epochs):
        avg_cost = 0
        total_batch = int(mnist.train.num_examples / batch_size)

        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            c, _ = sess.run([cost, optimizer], feed_dict={X: batch_xs, Y: batch_ys})
            avg_cost += c / total_batch
        print('Epoch :', "%04d" % (epoch + 1), 'cost = ', '{:9f}'.format(avg_cost))

    print('Accuracy : ', accuarcy.eval(session=sess, feed_dict={X:mnist.test.images, Y: mnist.test.labels}))