How To Use Model Input In Loss Function?

I am trying to use a custom loss-function which depends on some arguments that the model does not have. The model has two inputs (mel_specs and pred_inp) and expects a labels tenso

Solution 1:

I have used the add_loss method as follow:

defcustom_loss(y_true, y_pred, input_):
# custom loss function
    y_estim = input_[...,0]*y_pred
    shape = tf.cast(tf.shape(y_true)[1], dtype='float32')
    return tf.reduce_mean(1/shape*tf.reduce_sum(tf.pow(y_true-y_estim, 2), axis=1))


mix_input = layers.Input(shape=(301, 257, 4)) # input 1
ref_input = layers.Input(shape=(301, 257, 1)) # input 2
target = layers.Input(shape=(301, 257))       # output target

smss_model = Model(inputs=[mix_input, ref_input], outputs=smss) # my model that accept two inputs

model = Model(inputs=[mix_input, ref_input, target], outputs=smss) # this one used just to train the model, with the additional paramters
model.add_loss(custom_loss(target, smss, mix_input)) # the add_loss where to pass the custom loss function
model.summary()

model.compile(loss=None, optimizer='sgd')
model.fit([mix, ref, y], epochs=1, batch_size=1, verbose=1)

even do I have used this method and works, I still looking for another method, that not involve creating a training model

Solution 2:

Did using lambda function work? (https://www.w3schools.com/python/python_lambda.asp)

loss = lambda x1, x2: rnnt_loss(x1, x2, acts, labels, input_lengths,
                                label_lengths, blank_label=0)

In this way your loss function should be a function accepting parameters x1 and x2, but rnnt_loss can also be aware of acts, labels, input_lengths, label_lengths and blank_label