CODEWITHSUNDEEP
python-3.xtensorflowkeras
Makis Kans
Makis Kans

Reputation: 337

gradients of custom loss

Suppose a model as in:

model = Model(inputs=[A, B], outputs=C)

With custom loss:

def actor_loss(y_true, y_pred):
   log_lik = y_true * K.log(y_pred)
   loss = -K.sum(log_lik * K.stop_gradient(B))
   return loss

Now I'm trying to define a function that returns the gradients of the loss wrt to the weights for a given pair of input and target output and expose it as such. Here is an idea of what I mean in pseudocode

def _get_grads(inputs, targets):
   loss = model.loss(targets, model.output)
   weights = model.trainable_weights
   grads = K.gradients(loss, weights)
   model.input[0] (aka 'A') <----inputs[0]
   model.input[1] (aka 'B') <----inputs[1]
   return K.function(model.input, grads)

self.get_grads = _get_grads

My question is how do I feed inputs argument to the graph inside said function. (So far I've only worked with .fit and not with .gradients and I can't find any decent documentation with custom loss or multiple inputs)

Upvotes: 1

Views: 291

Answers (2)

Makis Kans
Makis Kans

Reputation: 337

My understanding of K.function ,K.gradients and custom loss was fundamentally wrong. You use the function to construct a mini-graph that computes gradients of loss wrt to weights. No need for the function itself to have arguments. 

def _get_grads():
   targets = Input(shape=...)
   loss = model.loss(targets, model.output)
   weights = model.trainable_weights
   grads = K.gradients(loss, weights)
   return K.function(model.input + [targets], grads)

I was under the impression that _get_grads was itself K.function but that was wrong. _get_grads() returns K.function. And then you use that as 

f = _get_grads() # constructs the mini-graph that gives gradients
grads = f([inputs, labels])

inputs is fed to model.inputs, labels to targets and it returns grads.

Upvotes: 0

Dr. Snoopy
Dr. Snoopy

Reputation: 56367

If you call K.function, you get an actual callable function, so you should just call it with some parameter values. The format is exactly the same as model.fit, in your case it should be two arrays of values, including the batch dimension:

self.get_grads = _get_grads(inputs, targets)

grad_value = self.get_grads([input1, input2])

Where input1 and input2 are numpy arrays that include the batch dimension.

Upvotes: 2

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