#  source and further documentation :
#  https://www.tensorflow.org/versions/r1.2/get_started/mnist/pros

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data



def deep_nn(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)

	# Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
	# is down to 7x7x64 feature maps -- maps this to 1024 features.
	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 - controls the complexity of the model, prevents co-adaptation of
	# features.
	keep_prob = tf.placeholder(tf.float32)
	h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

	# Map the 1024 features to 10 classes, one for each digit
	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

def conv2d(x, W):
	"""conv2d returns a 2d convolution layer with full stride."""
	return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
	"""max_pool_2x2 downsamples a feature map by 2X."""
	return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')


def weight_variable(shape):
	"""weight_variable generates a weight variable of a given shape."""
	initial = tf.truncated_normal(shape, stddev=0.1)
	return tf.Variable(initial)


def bias_variable(shape):
	"""bias_variable generates a bias variable of a given shape."""
	initial = tf.constant(0.1, shape=shape)
	return tf.Variable(initial)
def main(_):
	RANDOM_SEED = 117
	tf.set_random_seed(RANDOM_SEED)
	mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

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

	# building the network

	y_conv, keep_prob = deep_nn(x)
	ce_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_conv, labels=y_))


	#optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(ce_loss)
	train_step = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(ce_loss)
	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(2000):
		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}))
a = None
main(a)