model.py

from __future__ import generators, division, absolute_import, with_statement, print_function, unicode_literals

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import tensorflow.contrib.slim as slim

flags = tf.app.flags
FLAGS = flags.FLAGS

def mnist_model(input, reuse=False):
	with tf.name_scope("model"):
		with tf.variable_scope("conv1") as scope:
			net = tf.contrib.layers.conv2d(input, 32, [7, 7], activation_fn=tf.nn.relu, padding='SAME',
		        weights_initializer=tf.contrib.layers.xavier_initializer_conv2d(),scope=scope,reuse=reuse)
			net = tf.contrib.layers.max_pool2d(net, [2, 2], padding='SAME')

		with tf.variable_scope("conv2") as scope:
			net = tf.contrib.layers.conv2d(net, 64, [5, 5], activation_fn=tf.nn.relu, padding='SAME',
		        weights_initializer=tf.contrib.layers.xavier_initializer_conv2d(),scope=scope,reuse=reuse)
			net = tf.contrib.layers.max_pool2d(net, [2, 2], padding='SAME')

		with tf.variable_scope("conv3") as scope:
			net = tf.contrib.layers.conv2d(net, 128, [3, 3], activation_fn=tf.nn.relu, padding='SAME',
		        weights_initializer=tf.contrib.layers.xavier_initializer_conv2d(),scope=scope,reuse=reuse)
			net = tf.contrib.layers.max_pool2d(net, [2, 2], padding='SAME')

		with tf.variable_scope("conv4") as scope:
			net = tf.contrib.layers.conv2d(net, 256, [1, 1], activation_fn=tf.nn.relu, padding='SAME',
		        weights_initializer=tf.contrib.layers.xavier_initializer_conv2d(),scope=scope,reuse=reuse)
			net = tf.contrib.layers.max_pool2d(net, [2, 2], padding='SAME')

		with tf.variable_scope("conv5") as scope:
			net = tf.contrib.layers.conv2d(net, 2, [1, 1], activation_fn=None, padding='SAME',
		        weights_initializer=tf.contrib.layers.xavier_initializer_conv2d(),scope=scope,reuse=reuse)
			net = tf.contrib.layers.max_pool2d(net, [2, 2], padding='SAME')

		net = tf.contrib.layers.flatten(net)
	
	return net


def contrastive_loss(model1, model2, y, margin):
	with tf.name_scope("contrastive-loss"):
		distance = tf.sqrt(tf.reduce_sum(tf.pow(model1 - model2, 2), 1, keepdims=True))
		similarity = y * tf.square(distance)                                           # keep the similar label (1) close to each other
		dissimilarity = (1 - y) * tf.square(tf.maximum((margin - distance), 0))        # give penalty to dissimilar label if the distance is bigger than margin
		return tf.reduce_mean(dissimilarity + similarity) / 2