Shared weights for subclass in a siamese model in Tensorflow - class

Question

I've some problem with the organization of my code in TENSORFLOW.
I want to implement a siamese model that compares the outputs of two convolutional network that have the same weights.
I want to create a class to define my convolutional network, and an other class to define my global model. It seems that there are several ways to share weights (lazy-loading, use many scopes,...) but how can I do this between many objects ?
Are FLAGS useful in my case ?
Any help would be useful

Answer 1

I've found it easiest to use tf.variable_scope with reuse=tf.AUTO_REUSE. tf.name_scope is optional, but keeps your graphs clean for tensorboard visualizations.
import tensorflow as tf
def get_logits(image):
with tf.variable_scope('my_network', reuse=tf.AUTO_REUSE):
# more complex network probably
x = image
x = tf.layers.conv2d(x, 3, 1, activation=tf.nn.relu)
x = tf.layers.conv2d(x, 3, 1, activation=tf.nn.relu)
x = tf.layers.flatten(x)
x = tf.layers.dense(x, 10)
return x
batch_size = 2
height = 6
width = 6
# dummy images
image1 = tf.zeros((batch_size, height, width, 3), dtype=tf.float32)
image2 = tf.zeros((batch_size, height, width, 3), dtype=tf.float32)
with tf.name_scope('instance1'):
out1 = get_logits(image1)
print(len(tf.global_variables())) # 6
with tf.name_scope('instance2'):
out2 = get_logits(image2)
print(len(tf.global_variables())) # still 6
I'm unsure of your exact issue with different objects. If you have multiple different objects, just make sure they call the same function.
class MyNetwork(object):
def __init__(self, name):
self.name = name
def get_network_logits(self, image):
with tf.name_scope(self.name):
return get_logits(image)
n1 = MyNetwork('instance1')
n2 = MyNetwork('instance2')
l1 = n1(image1)
l2 = n2(image2)