tf.contrib.data.padded_batch_and_drop_remainder

tf.contrib.data.padded_batch_and_drop_remainder(
    batch_size,
    padded_shapes,
    padding_values=None
)

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

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

A batching and padding transformation that omits the final small batch. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.data.Dataset.padded_batch(..., drop_remainder=True).

Like tf.data.Dataset.padded_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.

See tf.contrib.data.batch_and_drop_remainder for more details.

Args:

• batch_size: A tf.int64 scalar tf.Tensor, representing the number of consecutive elements of this dataset to combine in a single batch.
• padded_shapes: A nested structure of tf.TensorShape or tf.int64 vector tensor-like objects. See tf.data.Dataset.padded_batch for details.
• padding_values: (Optional.) A nested structure of scalar-shaped tf.Tensor. See tf.data.Dataset.padded_batch for details.

Returns:

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