#MNIST is a computer vision dataset consisting of images of handwritten digits
# input images x will consist of a 2d tensor of floating point numbers
from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf
sess = tf.InteractiveSession()

def wt_variable(shape):#for initialzing weights and biases
  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 max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')

def conv2d(x, W):#function for convolving
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def deepnn(x):
    
    #first convolutional layer
    #first two dimensions are patch size followed by input and output channels
    W_conv1 = wt_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    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)
    #convolution is done followed by max pooling
    #Relu function is used=max(0,z)

    W_conv2 = wt_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)

    W_fc1 = wt_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 to prevent overfitting
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    W_fc2 = wt_variable([1024, 10])
    b_fc2 = bias_variable([10])
    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
    
    return y_conv,keep_prob

def main(_):
    
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
    x = tf.placeholder(tf.float32, [None, 784])
    y_ = tf.placeholder(tf.float32, [None, 10])
    y_conv, keep_prob = deepnn(x)
    
    cross_ent = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_ent)
    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(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}))
