# Import MINST data
import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

import tensorflow as tf

# Parameters
learning_rate = 0.001
training_epochs = 15
batch_size = 100
display_step = 1

#Network Parameters
n_hidden_1 = 256
n_hidden_2 = 256
n_input = 784 #MNIST data input
n_classes = 10 #MNIST total classes

# Create model
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])

def multilayer_perceptron(_X, _weights, _biases):
    layer_1 = tf.nn.relu(tf.matmul(_X, _weights['h1']) + _biases['b1']) #Hidden layer with RELU activation
    layer_2 = tf.nn.relu(tf.matmul(layer_1, _weights['h2']) + _biases['b2']) #Hidden layer with RELU activation
    return tf.matmul(layer_2, weights['out']) + biases['out']

weights = {
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}

biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

pred = multilayer_perceptron(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# Train
init = tf.initialize_all_variables()
with tf.Session() as sess:
    sess.run(init)
    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)
            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
            avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch
        if epoch % display_step == 0:
            print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)

    print "Optimization Finished!"

    # Test trained model
    correct_prediction = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})