I am looking to design a custom loss function for Keras model. The model itself is neural network that accepts a set of images and is supposed to run a regression to get an output, which is a value. Due to the physical conditions of the problem, I need to add a regularization term which would calculate the $cos(y_{pred})*f(X_i)$, where $y_{pred}$ is the output of the neural network, $X_i$ is the training example used to calculate $y_{pred}$, $f$ is some function which would calculate a value based on the image.

My problem is how to get the $X_i$ from the model? Loss function is supposed to accept just two inputs: $y_{pred}$ and $y_{true}$ which are tensors.

  • $\begingroup$ So your loss function is not equal to zero when $Y_{pred} = Y_{true}$? And also, if you have a function to calculate the loss using $X_i$, then you don't need a neural network. You can find a mathematical model that always generates expected output. $\endgroup$
    – aminrd
    Nov 4, 2019 at 23:53
  • $\begingroup$ I am sorry for the confusion. It is not exactly like that. For $y_{true}$ from the data we have that $cos(y_{true})*f(X_i) $ is supposed to be approximately zero. So everything should work. I just need a way to somehow access $X_i$ from within the loss function. $\endgroup$ Nov 4, 2019 at 23:59
  • $\begingroup$ You could do something like this stackoverflow.com/questions/51680818/… $\endgroup$ May 2, 2020 at 17:52

1 Answer 1


You should insert Xi into Y_true or Y_pred.

pseudocode is here.

Xi: np.array (10,256,256,1) Y_pred: np.array (10,256,256,1) Y_true: np.array (10,256,256,1)

input_Xi_Y_pred: np.array (10,256,256,2)

  • [:,:,:,0] is Xi
  • [:,:,:,1] is Y_pred


input_Xi_Y_pred = np.concatenate((Xi, Y_pred), axis=3))

def cosine_f_loss2(Y_true, input_Xi_Y_pred):
    return Your_calculation_cos_f (y_pred, Xi)

Data type of input is tf tensor. If You need to do numpy calculation, You have to change tf to numpy array. return should be numpy array

def cosine_f_loss1(Y_true, input_Xi_Y_pred):
    return tf.py_function (cosine_f_loss2, inp=[Y_true, input_Xi_Y_pred], Tout=[tf.float32])

model.compile (loss = cosine_f_loss1)
model.fit (input_Xi_Y_pred, Y_true)

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