custom_loss.py

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))