Custom weighted loss function in Keras for weighing each element

In model.fit the batch size is 32 by default, that's where this number is coming from. Here's what's happening:

• In custom_loss_1 the tensor K.abs(y_true-y_pred) has shape (batch_size=32, 5), while the numpy array weights has shape (100, 5). This is an invalid multiplication, since the dimensions don't agree and broadcasting can't be applied.

• In custom_loss_2 this problem doesn't exist because you're multiplying 2 tensors with the same shape (batch_size=32, 5).

• In custom_loss_3 the problem is the same as in custom_loss_1, because converting weights into a Keras variable doesn't change their shape.

UPDATE: It seems you want to give a different weight to each element in each training sample, so the weights array should have shape (100, 5) indeed.In this case, I would input your weights' array into your model and then use this tensor within the loss function:

import numpy as npfrom keras.layers import Dense, Inputfrom keras import Modelimport keras.backend as Kfrom functools import partialdef custom_loss_4(y_true, y_pred, weights):    return K.mean(K.abs(y_true - y_pred) * weights)train_X = np.random.randn(100, 5)train_Y = np.random.randn(100, 5) * 0.01 + train_Xweights = np.random.randn(*train_X.shape)input_layer = Input(shape=(5,))weights_tensor = Input(shape=(5,))out = Dense(5)(input_layer)cl4 = partial(custom_loss_4, weights=weights_tensor)model = Model([input_layer, weights_tensor], out)model.compile('adam', cl4)model.fit(x=[train_X, weights], y=train_Y, epochs=10)