How to implement a filter in tensorflow?

I have a convolutional neural network with three images as inputs:

x_anchor = tf.placeholder('float', [None, 4900], name='x_anchor')
x_positive = tf.placeholder('float', [None, 4900], name='x_positive')
x_negative = tf.placeholder('float', [None, 4900], name='x_negative')

Within a train function, I feed the placeholders with the actual images:

 input1, input2, input3 = training.next_batch(start,end)
    ....some other operations...
loss_value  = sess.run([cost], feed_dict={x_anchor:input1, x_positive:input2, x_negative:input3})

I'm using a triplet loss function on these three inputs (that's actually the cost variable above):

def triplet_loss(d_pos, d_neg):

    margin = 0.2

    loss = tf.reduce_mean(tf.maximum(0., margin + d_pos - d_neg))

    return loss

How can I filter the losses, so only the images with loss_value > 0 will be used to train the network?

How can I implement something like:

if(loss_value for input1, input2, input3 > 0)
  use inputs to train network
else
 do nothing/try another input

What I have tried so far:

I took the images one by one (input1[0], input2[0], input3[0]), calculated the loss, and if the loss was positive I would calculate (and apply) the gradients. But the problem is I use dropout in my model and I have to apply the model twice on my inputs:

First to calculate the loss and verify whether it's greater than 0
Second to run the optimizer: this is when things go wrong. As I mentioned before, I use dropout, so the results of the model on my inputs are different, so the new loss will sometimes be 0 even if the loss determined at step 1 is greater than 0.

I also tried to use tf.py_func but got stuck.

Solution

There's a new TensorFlow feature called “AutoGraph”. AutoGraph converts Python code, including control flow, print() and other Python-native features, into pure TensorFlow graph code. For example:

@autograph.convert()
def huber_loss(a):
  if tf.abs(a) <= delta:
    loss = a * a / 2
  else:
    loss = delta * (tf.abs(a) - delta / 2)
  return loss

becomes this code at execution time due to the decorator:

def tf__huber_loss(a):
  with tf.name_scope('huber_loss'):
    def if_true():
      with tf.name_scope('if_true'):
        loss = a * a / 2
        return loss,
    def if_false():
      with tf.name_scope('if_false'):
        loss = delta * (tf.abs(a) - delta / 2)
        return loss,
    loss = ag__.utils.run_cond(tf.less_equal(tf.abs(a), delta), if_true,
        if_false)
    return loss

What you wanted to do could have been implemented before using tf.cond().

I found out about this through this medium post.