# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""A very simple MNIST classifier.

See extensive documentation at
http://tensorflow.org/tutorials/mnist/beginners/index.md
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse

# Import data
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

FLAGS = None


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 main(_):
  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

  # Create the model:
  # conv[32 feature maps] -> pooling
  x = tf.placeholder(tf.float32, [None, 784]) # input: Any size (# samples), 28*28 px
  W = tf.Variable(tf.zeros([784, 10]))        # model variables
  b = tf.Variable(tf.zeros([10]))
  y = tf.matmul(x, W) + b                     # simple regression / 1 neural layer
  W_conv1 = weight_variable([5, 5, 1, 32])

  # layer 1
  x_image = tf.reshape(x, [-1, 28, 28, 1])
  b_conv1 = bias_variable([32])
  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
  h_pool1 = max_pool_2x2(h_conv1)

  # layer 2
  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)

  # densely connected layer
  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 dropout
  keep_prob = tf.placeholder(tf.float32)
  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

  # readout layer
  W_fc2 = weight_variable([1024, 10])
  b_fc2 = bias_variable([10])
  y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2


  # Define loss and optimizer
  y_ = tf.placeholder(tf.float32, [None, 10])

  cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_))
  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))

  # Note: we use an interactive session here, so we don't have to build the
  # graph on forehand (like in helloworld.py)
  sess = tf.InteractiveSession() # start C++ backend
  sess.run(tf.initialize_all_variables())

  for i in range(20000):
    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})
      print("step %d, training accuracy %g"%(i, train_accuracy))
    train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

    if i%1000 == 0:
      print("TESTING")
      print("test accuracy %g"%accuracy.eval(feed_dict={
        x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument('--data_dir', type=str, default='/tmp/data',
                      help='Directory for storing data')
  FLAGS = parser.parse_args()
  tf.app.run()
