#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Feb  2 17:10:25 2017

@author: rajesh
"""

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)


import tensorflow as tf
import argparse

'''
#########################Single Layer
x = tf.placeholder(tf.float32,[None,784])


W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

# y is the predicted probability distribution
y = tf.nn.softmax(tf.matmul(x,W)+b)

# y_ is the true distribution ( one-hot vector with the digit labels)
y_ = tf.placeholder(tf.float32, [None, 10])


# implementing the cross entropy function ( -sum(y_log(y)) which serves as a cost function
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)

# Created the session
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()

# let's train the model- we will run the training step 1000 times
for i in range (1000):
    batch_xs, batch_ys= mnist.train.next_batch(100)
    sess.run(train_step, feed_dict= {x: batch_xs, y_: batch_ys})
    
# Using small batches of random data is called stochastic training ( in this case, stochastic gradient descent)


# Evaluating the model now
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# Finally we ask for our accuracy on our test data
print (sess.run(accuracy, feed_dict= {x: mnist.test.images, y_: mnist.test.labels}))

'''
############### Building a Multi-Layer Convolution Network

# Initializing the weights

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

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)
    
    
# Convolution and Pooling

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')
    
# 1st Convolution Layer

W_conv1= weight_variable([5,5,1,32])
b_conv1= bias_variable([32])

# To apply the layer, we need to reshape the image x to a 4d tensor

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

# convolve x_image with the weight tensor and add the bias. After then apply the ReLu function and finally do the max pool

h_conv1= tf.nn.relu(conv2d(x_image, W_conv1)+ b_conv1)
h_pool1= max_pool_2x2(h_conv1)

# 2nd Convolution 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)

# Densely or Fully 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)

# 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

# Train and Evaluate the model

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))


# Created the session
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()

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:0.1})
        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}))


    
    






