#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 15 18:39:35 2018

@author: leanderheuvel
"""
import tensorflow as tf
import Semeion_data_loader as data
import numpy as np
import random
import matplotlib.pyplot as plt

#from tensorflow.examples.tutorials.mnist import input_data
#mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

sess = tf.InteractiveSession()

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 next_batch(size):
    batch_x = np.empty([size,256])
    batch_y = np.empty([size,10])
    for s in range(size):
        index = random.randint(0,1392)
        batch_x[s,] = data.x_train[index]
        batch_y[s,] = data.y_train[index]
    return batch_x, batch_y

x = tf.placeholder(tf.float32, shape=[None, 256])
y_ = tf.placeholder(tf.float32, shape=[None, 10])

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

x_image = tf.reshape(x, [-1, 16, 16, 1])

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([4 * 4 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 4*4*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)


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

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

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
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))
print('training model...')
accuracies=[]
with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  for i in range(20000):
    batch_xs, batch_ys = next_batch(50)
    #batch = mnist.train.next_batch(50)
    if i % 100 == 0:
        train_accuracy = accuracy.eval(feed_dict={
              #x: batch[0], y_: batch[1], keep_prob: 1.0})
              x: batch_xs, y_: batch_ys, keep_prob: 1.0})
        accuracies.append(accuracy.eval(feed_dict={
      x: data.x_test, y_: data.y_test, keep_prob: 1.0}))
        print('step %d, training accuracy %f' % (i, train_accuracy))
    train_step.run(feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5})
    #train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
  print('test accuracy %g' % accuracy.eval(feed_dict={
      x: data.x_test, y_: data.y_test, keep_prob: 1.0}))
  plt.plot(accuracies)