from tensorflow.python.keras.datasets.mnist import load_data

import tensorflow as tf
import numpy as np
import random as rn

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

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

(x_train, y_train), (x_test, y_test) = load_data()

x = tf.placeholder(tf.float32, [None, 28, 28])

y = tf.placeholder(tf.int64, [None])

with tf.name_scope('reshape'):
  x_image = tf.reshape(x, [-1, 28, 28, 1])

with tf.name_scope('conv1'):
  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)

with tf.name_scope('pool1'):
  h_pool1 = max_pool_2x2(h_conv1)

with tf.name_scope('conv2'):
  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)

with tf.name_scope('pool2'):
  h_pool2 = max_pool_2x2(h_conv2)

with tf.name_scope('fc1'):
  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)

with tf.name_scope('dropout'):
  keep_prob = tf.placeholder(tf.float32)
  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

with tf.name_scope('fc2'):
  W_fc2 = weight_variable([1024, 10])
  b_fc2 = bias_variable([10])

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

with tf.name_scope('loss'):
  cross_entropy = tf.losses.sparse_softmax_cross_entropy(
      labels=y, logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)

with tf.name_scope('adam_optimizer'):
  train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

with tf.name_scope('accuracy'):
  correct_prediction = tf.equal(tf.argmax(y_conv, 1), y)
  correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)

graph = tf.get_default_graph()
saver = tf.train.Saver()

def load(sess, path):
  saver.restore(sess, path)
  print('Model restored from %s' % path)
  print('test accuracy %g' % accuracy.eval(feed_dict={
      x: x_test, y: y_test, keep_prob: 1.0}, session=sess))

def main():
  with tf.Session() as sess:
    load(sess, './untrained.ckpt')
    load(sess, './train0.ckpt')
    load(sess, './train1.ckpt')

if __name__ == '__main__':
  main()
