► Keras 3 API documentation / KerasNLP / Metrics / Perplexity metric

Perplexity metric

[source]

Perplexity class

keras_nlp.metrics.Perplexity(
    from_logits=False, mask_token_id=None, dtype="float32", name="perplexity", **kwargs
)

Perplexity metric.

This class implements the perplexity metric. In short, this class calculates the cross entropy loss and takes its exponent. Note: This implementation is not suitable for fixed-size windows.

Arguments

  • from_logits: bool. If True, y_pred (input to update_state()) should be the logits as returned by the model. Otherwise, y_pred is a tensor of probabilities.
  • mask_token_id: int. ID of the token to be masked. If provided, the mask is computed for this class. Note that if this field is provided, and if the sample_weight field in update_state() is also provided, we will compute the final sample_weight as the element-wise product of the mask and the sample_weight.
  • dtype: string or tf.dtypes.Dtype. Precision of metric computation. If not specified, it defaults to "float32".
  • name: string. Name of the metric instance.
  • **kwargs: Other keyword arguments.

Examples

  1. Calculate perplexity by calling update_state() and result(). 1.1. sample_weight, and mask_token_id are not provided.
>>> np.random.seed(42)
>>> perplexity = keras_hub.metrics.Perplexity(name="perplexity")
>>> target = np.random.randint(10, size=[2, 5])
>>> logits = np.random.uniform(size=(2, 5, 10))
>>> perplexity.update_state(target, logits)
>>> perplexity.result()
<tf.Tensor: shape=(), dtype=float32, numpy=14.352535>

1.2. sample_weight specified (masking token with ID 0).

>>> np.random.seed(42)
>>> perplexity = keras_hub.metrics.Perplexity(name="perplexity")
>>> target = np.random.randint(10, size=[2, 5])
>>> logits = np.random.uniform(size=(2, 5, 10))
>>> sample_weight = (target != 0).astype("float32")
>>> perplexity.update_state(target, logits, sample_weight)
>>> perplexity.result()
<tf.Tensor: shape=(), dtype=float32, numpy=14.352535>
  1. Call perplexity directly.
>>> np.random.seed(42)
>>> perplexity = keras_hub.metrics.Perplexity(name="perplexity")
>>> target = np.random.randint(10, size=[2, 5])
>>> logits = np.random.uniform(size=(2, 5, 10))
>>> perplexity(target, logits)
<tf.Tensor: shape=(), dtype=float32, numpy=14.352535>
  1. Provide the padding token ID and let the class compute the mask on its own.
>>> np.random.seed(42)
>>> perplexity = keras_hub.metrics.Perplexity(mask_token_id=0)
>>> target = np.random.randint(10, size=[2, 5])
>>> logits = np.random.uniform(size=(2, 5, 10))
>>> perplexity(target, logits)
<tf.Tensor: shape=(), dtype=float32, numpy=14.352535>
Perplexity metric
Perplexity class