#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 28 14:38:45 2017

@author: HAL-9000
"""

import numpy as np
import tensorflow as tf
import sys
from tensorflow.examples.tutorials.mnist import input_data

mnist=input_data.read_data_sets('MNIST_data/',one_hot=True)

print(__name__)

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 deepNN(x):

    x_image = tf.reshape(x, [-1, 28, 28, 1])

    # First convolutional layer - maps one grayscale image to 32 feature maps.
    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)
    
    # Pooling layer - downsamples by 2X.
    h_pool1 = max_pool_2x2(h_conv1)
    
    # Second convolutional layer -- maps 32 feature maps to 64.
    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)
    
    # Second pooling layer.
    h_pool2 = max_pool_2x2(h_conv2)

    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)

    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

    return y_conv,keep_prob

x=tf.placeholder(tf.float32,[None,784])
y_=tf.placeholder(tf.float32,[None,10])

y_conv,keep_prob=deepNN(x)

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))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(0,20000):
        batch=mnist.train.next_batch(50)
        if i%1000 == 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}))
    
            


























