RMSLE Keras Metric

Overview

Root Mean Squared Logarithmic Error (RMSLE) penalizes under-prediction more than over-prediction, making it ideal for price, sales, or count regression. Implement it as a custom Keras metric so it displays during training alongside loss. Use K.clip to avoid log(0).

Quick Start

from tensorflow.keras import backend as K

def rmsle(y_true, y_pred):
    first_log = K.log(K.clip(y_pred, K.epsilon(), None) + 1.)
    second_log = K.log(K.clip(y_true, K.epsilon(), None) + 1.)
    return K.sqrt(K.mean(K.square(first_log - second_log), axis=-1))

model.compile(optimizer='adam', loss='mse', metrics=['mae', rmsle])

Workflow

1. Define rmsle as a function of (y_true, y_pred) using Keras backend ops
2. Clip predictions to [epsilon, inf) before log to prevent NaN
3. Add 1 before log (log1p) to handle zero values
4. Pass as metrics=[rmsle] in model.compile
5. Monitor during training — it tracks the competition metric directly

Key Decisions

• Clip vs relu: K.clip is safer — relu zeros out negatives silently
• Loss vs metric: use MSE or MAE as loss (smoother gradients), RMSLE as metric
• Log1p target: if target is already log-transformed, use RMSE instead of RMSLE
• vs sklearn: sklearn.metrics.mean_squared_log_error works for evaluation only, not as a Keras metric

References

• A simple nn solution with Keras