RMSE/ RMSLE loss function in Keras

Question

I try to participate in my first Kaggle competition where RMSLE is given as the required loss function. For I have found nothing how to implement this loss function I tried to settle for RMSE. I know this was part of Keras in the past, is there any way to use it in the latest version, maybe with a customized function via backend?

This is the NN I designed:

from keras.models import Sequential from keras.layers.core import Dense , Dropout from keras import regularizers  model = Sequential() model.add(Dense(units = 128, kernel_initializer = "uniform", activation = "relu", input_dim = 28,activity_regularizer = regularizers.l2(0.01))) model.add(Dropout(rate = 0.2)) model.add(Dense(units = 128, kernel_initializer = "uniform", activation = "relu")) model.add(Dropout(rate = 0.2)) model.add(Dense(units = 1, kernel_initializer = "uniform", activation = "relu")) model.compile(optimizer = "rmsprop", loss = "root_mean_squared_error")#, metrics =["accuracy"])  model.fit(train_set, label_log, batch_size = 32, epochs = 50, validation_split = 0.15) 

I tried a customized root_mean_squared_error function I found on GitHub but for all I know the syntax is not what is required. I think the y_true and the y_pred would have to be defined before passed to the return but I have no idea how exactly, I just started with programming in python and I am really not that good in math...

from keras import backend as K  def root_mean_squared_error(y_true, y_pred):         return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))  

I receive the following error with this function:

ValueError: ('Unknown loss function', ':root_mean_squared_error') 

Thanks for your ideas, I appreciate every help!

Answer 1

When you use a custom loss, you need to put it without quotes, as you pass the function object, not a string:

def root_mean_squared_error(y_true, y_pred):         return K.sqrt(K.mean(K.square(y_pred - y_true)))   model.compile(optimizer = "rmsprop", loss = root_mean_squared_error,                metrics =["accuracy"]) 

Answer 2

The accepted answer contains an error, which leads to that RMSE being actually MAE, as per the following issue:

https://github.com/keras-team/keras/issues/10706

The correct definition should be

def root_mean_squared_error(y_true, y_pred):         return K.sqrt(K.mean(K.square(y_pred - y_true)))