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Negative binomial loss function
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| import tensorflow as tf | |
| def negative_binomial_loss(y_true, y_pred): | |
| """ | |
| Negative binomial loss function. | |
| Assumes tensorflow backend. | |
| Parameters | |
| ---------- | |
| y_true : tf.Tensor | |
| Ground truth values of predicted variable. | |
| y_pred : tf.Tensor | |
| n and p values of predicted distribution. | |
| Returns | |
| ------- | |
| nll : tf.Tensor | |
| Negative log likelihood. | |
| """ | |
| # Separate the parameters | |
| n, p = tf.unstack(y_pred, num=2, axis=-1) | |
| # Add one dimension to make the right shape | |
| n = tf.expand_dims(n, -1) | |
| p = tf.expand_dims(p, -1) | |
| # Calculate the negative log likelihood | |
| nll = ( | |
| tf.math.lgamma(n) | |
| + tf.math.lgamma(y_true + 1) | |
| - tf.math.lgamma(n + y_true) | |
| - n * tf.math.log(p) | |
| - y_true * tf.math.log(1 - p) | |
| ) | |
| return nll |
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