why dataset.output_shapes returns demension(none) after batching

Question

I\'m using the Dataset API for input pipelines in TensorFlow (version: r1.2). I built my dataset and batched it with a batch size of 128. The dataset fed into the RNN.

Answer 1

They hardcoded batch size in implementation and it always will return None (tf 1.3).

def _padded_shape_to_batch_shape(s):
  return tensor_shape.vector(None).concatenate(
      tensor_util.constant_value_as_shape(s))

In this way, they can batch all elements (e.g. dataset_size=14, batch_size=5, last_batch_size=4).

You can use dataset.filter and dataset.map to fix this issue

d = contrib.data.Dataset.from_tensor_slices([[5] for x in range(14)])
batch_size = 5
d = d.batch(batch_size)
d = d.filter(lambda e: tf.equal(tf.shape(e)[0], batch_size))
def batch_reshape(e):
    return  tf.reshape(e, [args.batch_size] + [s if s is not None else -1 for s in e.shape[1:].as_list()])
d = d.map(batch_reshape)
r = d.make_one_shot_iterator().get_next()
print('dataset_output_shape = %s' % r.shape)
with tf.Session() as sess:
    while True:
        print(sess.run(r))

Output

dataset_output_shape = (5, 1)

[[5][5][5][5][5]]

[[5][5][5][5][5]]

OutOfRangeError