I'm looking for a way to create a conditional loss function that looks like this: there is a vector of labels, say l (l has the same length as the input x), then for a given input (y_true, y_pred, l) the loss should be:
def conditional_loss_function(y_true, y_pred, l):
loss = if l is 0: loss_funtion1
if l is 1: loss_funtion2
return loss
It is like a kind of semi-supervised loss funtion.
You should be able to solve this with currying. Make a function that takes the label as input and returns a function which takes y_true and y_pred as input. Note that the label needs to be a constant or a tensor for this to work.
def conditional_loss_function(l):
def loss(y_true, y_pred):
if l == 0:
return loss_funtion1(y_true, y_pred)
else:
return loss_funtion2(y_true, y_pred)
return loss
model.compile(loss=conditional_loss_function(l), optimizer=...)
Small working example with different loss function depending on the label:
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
data = dataset[:,0:8]
label = dataset[:,8]
X = Input(shape=(8,))
Y = Input(shape=(1,))
x = Dense(12, input_dim=8, activation='relu')(X)
x = Dense(8, activation='relu')(x)
predictions = Dense(1, activation='sigmoid')(x)
def custom_loss(l):
def loss(y_true, y_pred):
if l == 0:
return binary_crossentropy(y_true, y_pred)
else:
return mean_squared_error(y_true, y_pred)
return loss
# Compile model
model = Model(inputs=[X, Y], outputs=predictions)
model.compile(loss=custom_loss(Y), optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(x=[data, label], y=label, epochs=150)
# evaluate the model
scores = model.evaluate([data, label], label)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
