Custom loss function in Keras based on the input data - keras

Question

I am trying to create the custom loss function using Keras. I want to compute the loss function based on the input and predicted the output of the neural network.
I tried using the customloss function in Keras. I think y_true is the output that we give for training and y_pred is the predicted output of the neural network. The below loss function is same as "mean_squared_error" loss in Keras.
def customloss(y_true, y_pred):
return K.mean(K.square(y_pred - y_true), axis=-1)
I would like to use the input to the neural network also to compute the custom loss function in addition to mean_squared_error loss. Is there a way to send an input to the neural network as an argument to the customloss function.
Thank you.

Answer 1

I have come across 2 solutions to the question you asked.
You can pass your input (scalar only) as an argument to the custom loss wrapper function.
def custom_loss(i):
def loss(y_true, y_pred):
return K.mean(K.square(y_pred - y_true), axis=-1) + something with i...
return loss
def baseline_model():
# create model
i = Input(shape=(5,))
x = Dense(5, kernel_initializer='glorot_uniform', activation='linear')(i)
o = Dense(1, kernel_initializer='normal', activation='linear')(x)
model = Model(i, o)
model.compile(loss=custom_loss(i), optimizer=Adam(lr=0.0005))
return model
This solution is also mentioned in the accepted answer here
You can pad your label with extra data columns from input and write a custom loss. This is helpful if you just want one/few feature column(s) from your input.
def custom_loss(data, y_pred):
y_true = data[:, 0]
i = data[:, 1]
return K.mean(K.square(y_pred - y_true), axis=-1) + something with i...
def baseline_model():
# create model
i = Input(shape=(5,))
x = Dense(5, kernel_initializer='glorot_uniform', activation='linear')(i)
o = Dense(1, kernel_initializer='normal', activation='linear')(x)
model = Model(i, o)
model.compile(loss=custom_loss, optimizer=Adam(lr=0.0005))
return model
model.fit(X, np.append(Y_true, X[:, 0], axis =1), batch_size = batch_size, epochs=90, shuffle=True, verbose=1)
This solution can be found also here in this thread.
I have only used the 2nd method when I had to use input feature columns in the loss. The first method can be only used with scalar arguments as mentioned in the comments.

Answer 2

You could wrap your custom loss with another function that takes the input tensor as an argument:
def customloss(x):
def loss(y_true, y_pred):
# Use x here as you wish
err = K.mean(K.square(y_pred - y_true), axis=-1)
return err
return loss
And then compile your model as follows:
model.compile('sgd', customloss(x))
where x is your input tensor.
NOTE: Not tested.