def metrics_builder(): """Returns a `list` of `tf.keras.metric.Metric` objects.""" return [ keras_metrics.NumBatchesCounter(), keras_metrics.NumExamplesCounter(), keras_metrics.FlattenedNumExamplesCounter(name='num_tokens', mask_zero=True), keras_metrics.FlattenedCategoricalAccuracy(vocab_size=VOCAB_SIZE, mask_zero=True), ]
def metrics_builder(): """Returns a `list` of `tf.keras.metric.Metric` objects.""" pad_token, _, _, _ = shakespeare_dataset.get_special_tokens() return [ keras_metrics.NumBatchesCounter(), keras_metrics.NumExamplesCounter(), keras_metrics.NumTokensCounter(masked_tokens=[pad_token]), keras_metrics.MaskedCategoricalAccuracy(masked_tokens=[pad_token]), ]
def metrics_builder(): return [ keras_metrics.MaskedCategoricalAccuracy(name='accuracy_with_oov', masked_tokens=[pad_token]), keras_metrics.MaskedCategoricalAccuracy( name='accuracy_no_oov', masked_tokens=[pad_token, oov_token]), # Notice BOS never appears in ground truth. keras_metrics.MaskedCategoricalAccuracy( name='accuracy_no_oov_or_eos', masked_tokens=[pad_token, oov_token, eos_token]), keras_metrics.NumBatchesCounter(), keras_metrics.NumTokensCounter(masked_tokens=[pad_token]) ]
def metrics_builder(): return [ keras_metrics.FlattenedCategoricalAccuracy( # Plus 4 for PAD, OOV, BOS and EOS. vocab_size=FLAGS.vocab_size + 4, name='accuracy_with_oov', masked_tokens=pad_token), keras_metrics.FlattenedCategoricalAccuracy( vocab_size=FLAGS.vocab_size + 4, name='accuracy_no_oov', masked_tokens=[pad_token, oov_token]), # Notice BOS never appears in ground truth. keras_metrics.FlattenedCategoricalAccuracy( vocab_size=FLAGS.vocab_size + 4, name='accuracy_no_oov_or_eos', masked_tokens=[pad_token, oov_token, eos_token]), keras_metrics.NumBatchesCounter(), keras_metrics.FlattenedNumExamplesCounter(name='num_tokens', mask_zero=True), ]