'''
Created on May 8, 2017

@author: deckyal
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf 

FLAGS = None

def weight_variable(shape):
    #intialize variable with random weights
    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(_):
    #import data
    mnist = input_data.read_data_sets(FLAGS.data_dir,one_hot = True)
    
    #Create the model 
    x = tf.placeholder(tf.float32, [None,784]) #AmountOfData, dimensionOfMnistData(flattened)
    w = tf.Variable(tf.zeros([784,10])) #Variables must alwyas be initialized (here by zero)
    b = tf.Variable(tf.zeros([10]))
    y = tf.matmul(x,w) + b
    #los and optimizer 
    y_ = tf.placeholder(tf.float32,[None,10])
    
    #5x5 patch, 1 channel, 32 outputs
    
    W_conv1 = weight_variable([5,5,1,32])
    b_conv1 = bias_variable([32])
    
    #reshape into 4d tensor, -1 to flatten, 28x28 image dim, 1 color channel(last)
    x_image = tf.reshape(x,[-1,28,28,1])
    
    h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)
    
    
    #Second conv layer 
    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)
    
    
    #Fully conneced layers
    
    #Reduced image size of 7*7, and 1024 neurons 
    w_fc1 = weight_variable([7*7*64,1024])
    b_fc1 = bias_variable([1024]) #Bias size always the same with the last dim of w. Here is 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)
    
    #Dropout layer
    
    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
    
    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))
    
    
    sess = tf.InteractiveSession()
    sess.run(tf.global_variables_initializer())
    
    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})
    
    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/tensorflow/mnist/input_data',help='Directory for storing input data')
    FLAGS,unparsed = parser.parse_known_args()
    tf.app.run(main = main, argv = [sys.argv[0]]+unparsed)
    