'''
Created on May 7, 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 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])
    
    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_, logits = y))
    
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
    
    sess = tf.InteractiveSession()
    tf.global_variables_initializer().run()
    
    #Train
    for _ in range(1000) : 
        batch_xs, batch_ys = mnist.train.next_batch(100)
        sess.run(train_step,feed_dict={x:batch_xs,y_ : batch_ys})
        
    #Test trained model 
    correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    
    print(sess.run(accuracy,feed_dict = {x: mnist.test.images,y_:mnist.test.labels}))
    

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)
    
    
    