RMSE/ RMSLE loss function in Keras

问题

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!

回答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"])

回答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)))

回答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')])

回答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"])

来源：https://stackoverflow.com/questions/43855162/rmse-rmsle-loss-function-in-keras

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python

keras

custom-function

loss-function