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!

Asked By: dennis

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Source

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"])

Answered By: Dr. Snoopy

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)))

Answered By: Germán Sanchis

Answer 3

If you are using latest tensorflow nightly, although there is no RMSE in the documentation, there is a tf.keras.metrics.RootMeanSquaredError() in the source code.

sample usage:

model.compile(tf.compat.v1.train.GradientDescentOptimizer(learning_rate),
              loss=tf.keras.metrics.mean_squared_error,
              metrics=[tf.keras.metrics.RootMeanSquaredError(name='rmse')])

Answered By: Richard Xue

Answer 4

I prefer reusing part of the Keras work

from keras.losses import mean_squared_error

def root_mean_squared_error(y_true, y_pred):
    return K.sqrt(mean_squared_error(y_true, y_pred))

model.compile(optimizer = "rmsprop", loss = root_mean_squared_error, 
          metrics =["accuracy"])

Answered By: George C

Answer 5

You can do RMSLE the same way RMSE is shown in the other answers, you just also need to incorporate the log function:

from tensorflow.keras import backend as K

def root_mean_squared_log_error(y_true, y_pred):
    return K.sqrt(K.mean(K.square(K.log(1+y_pred) - K.log(1+y_true))))

Answered By: rbarden

Answer 6

Just like before, but more simplified (directly) version for RMSLE using Keras Backend:

import tensorflow as tf
import tensorflow.keras.backend as K

def root_mean_squared_log_error(y_true, y_pred):
    msle = tf.keras.losses.MeanSquaredLogarithmicError()
    return K.sqrt(msle(y_true, y_pred))

Answered By: Difagama

RMSE/ RMSLE loss function in Keras

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