Log dataset size if known
@@ -268,6 +268,7 @@ def training_pipeline(dataset,
"""
if shuffle_buffer_size is not None and shuffle_buffer_size != 0:
if dataset_size is not None:
+ tf.logging.info("Training on %s examples", dataset_size)
if shuffle_buffer_size < 0 or shuffle_buffer_size > dataset_size:
shuffle_buffer_size = dataset_size
elif shuffle_buffer_size < dataset_size:
