tf.contrib.data.batch_and_drop_remainder

tf.contrib.data.batch_and_drop_remainder(batch_size)

Defined in tensorflow/contrib/data/python/ops/batching.py.

See the guide: Dataset Input Pipeline > Transformations on existing datasets

A batching transformation that omits the final small batch (if present).

Like tf.data.Dataset.batch, this transformation combines consecutive elements of this dataset into batches. However, if the batch size does not evenly divide the input dataset size, this transformation will drop the final smaller element.

The following example illustrates the difference between this transformation and Dataset.batch():

dataset = tf.data.Dataset.range(200)
batched = dataset.apply(tf.contrib.data.batch_and_drop_remainder(128))
print(batched.output_shapes)  # ==> "(128,)" (the batch dimension is known)

By contrast, dataset.batch(128) would yield a two-element dataset with shapes (128,) and (72,), so the batch dimension would not be statically known.

Args:

• batch_size: A tf.int64 scalar tf.Tensor, representing the number of consecutive elements of this dataset to combine in a single batch.

Returns:

A Dataset transformation function, which can be passed to tf.data.Dataset.apply