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

parser = argparse.ArgumentParser(description="Batch arguments")
parser.add_argument('--num-training-steps', type=int, dest='num_training_steps', default=2000)
parser.add_argument('--mini-batch-size', type=int, dest='mini_batch_size', default=50)
args = parser.parse_args()

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')


def run():
    sess = tf.Session()

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

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

    x_image = tf.reshape(x, [-1, 28, 28, 1])

    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)


    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])

    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)

    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    keep_prob = tf.placeholder(tf.float32, name="keep_prob")
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])

    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2, name="y_conv")

    cross_entropy = tf.reduce_mean(
        -tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    sess.run(tf.initialize_all_variables())
    for i in xrange(args.num_training_steps):
        batch = mnist.train.next_batch(args.mini_batch_size)
        if i % 100 == 0:
            train_accuracy = accuracy.eval(
                feed_dict={
                    x:batch[0], y_: batch[1], keep_prob: 1.0},
                session=sess)
            print "step %d, training accuracy %g" %(i, train_accuracy)
        train_step.run(session=sess,
            feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

    print "test accuracy %s"%accuracy.eval(feed_dict={
        x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0},
        session=sess)

    saver = tf.train.Saver()
    save_path = saver.save(sess, 'model.ckpt')
    print "Model saved in file {}".format(save_path)

if __name__=='__main__':
    run()
