在损失函数中使用生成器

Question

I need to incorporate additional information into a Keras loss function that depends on the current batch. Since Keras losses only take two arguments, I considered adding this information by making the loss function call next() on a generator object. However, the generator is only called once (probably when adding the loss function in model.compile()). Here is a sample code:

data_batches = yield_data_batches()
meta_batches = yield_meta_batches()
....
model.compile(loss=loss_function, ...)
model.fit_generator(generator=data_batches, ....)

def loss_function(x, y):
      meta_x, meta_y = next(meta_batches)
      x *= meta_x  # component-wise matrix multiplication
      y *= meta_y  # component-wise matrix multiplication
      return mse(x, y)

Is there a way to make the loss function get a new meta_batch each time it is evaluated on a data_batch? Or is there another way to incorporate this meta information into the loss function?

Clarification: The meta_x and meta_y are binary matrices that should cancel out certain elements from the prediction as they should not count to the loss. For example:

y_true = (a,b,c,0) y_pred = (d,e,f,g) y_meta = (1,1,1,0)

Now, y_pred*y_meta should cancel out g so that it does not count to the loss.

Answer 1

This does not work, since the loss function will be compiled and added to the compute graph. Your loss function may only depend on y_pred and y_true .

You can either incorporate this information in y_true , or weight the resulting loss with sample weights.

Your approach would be equivalent to a combination of both: Assuming positive weights a and b (you call them meta_x and meta_y): |ax-by| = a|xb/a*y|, so you just weight y_pred by b/a and add the sample weight a.