使用汉明距离损失 Function 和 Tensorflow GradientTape：无梯度。 是不可微分的吗？

Question

I'm using Tensorflow 2.1 and Python 3, creating my custom training model following the tutorial " Tensorflow - Custom training: walkthrough ".

I'm trying to use Hamming Distance on my loss function:

import tensorflow as tf
import tensorflow_addons as tfa

def my_loss_hamming(model, x, y):
  global output
  output = model(x)

  return tfa.metrics.hamming.hamming_loss_fn(y, output, threshold=0.5, mode='multilabel')


def grad(model, inputs, targets):
  with tf.GradientTape() as tape:
      tape.watch(model.trainable_variables)
      loss_value = my_loss_hamming(model, inputs, targets)

  return loss_value, tape.gradient(loss_value, model.trainable_variables)

When I call it:

loss_value, grads = grad(model, feature, label)
optimizer.apply_gradients(zip(grads, model.trainable_variables))

grads variable is a list with 38 None.

And I get the error:

No gradients provided for any variable: ['conv1_1/kernel:0', ...]

Is there any way to use Hamming Distance without "interrupts the gradient chain registered by the gradient tape"?

Answer 1

Apology if I'm saying something obvious, but the way how backpropagation works as a fitting algorithm for neural networks is through gradients - eg for each batch of training data you compute how much the loss function will improve/degrade if you move a particular trainable weight by a very small amount delta .

Hamming loss is by definition not differentiable, so for small movements of trainable weights you will never experience any changes in the loss. I imagine it is only added to be used for final measurements of trained models' performance rather than for training.

If you want to train a neural net through backpropagation you need to use some differentiable loss - such that can help the model to move weights in the right direction. Sometimes people use different techniques to smooth such losses as Hamming less and create approximations - eg here it could be something which would penalize less predictions which are closer to the target answer rather then just giving out 1 for everything above threshold and 0 for everything else.