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

nb_classes = 10
X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, nb_classes])
W = tf.Variable(tf.random_normal([784, nb_classes]))
b = tf.Variable(tf.random_normal([nb_classes]))
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
#SOFTMAX

#Hypothesis
hypothesis = tf.nn.softmax(tf.matmul(X,W) + b)
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)

#Test model
is_correct = tf.equal(tf.arg_max(hypothesis, 1), tf.arg_max(Y, 1))
#Calculate Accuracy
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))

#parameters
training_epoch = 15
batch_size = 100

with tf.Session() as sess:
    #Initialize
    sess.run(tf.global_variables_initializer())
    #Cycle
    for epoch in range(training_epoch):
        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(100)
            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 : ", accuracy.eval(session = sess, feed_dict = {X:mnist.test.images, Y:mnist.test.labels}))

