RaggedTensor

[markub3327]

Hi @fchollet,

I try to implement Embedding layer with RaggedTensor in TF 2.16.1:

inputs = tf.keras.layers.Input(shape=[None], dtype=tf.int64, ragged=True)
x = tf.keras.layers.Embedding(input_dim=vectorize_layer.vocabulary_size(), output_dim=16)(inputs)
m = tf.keras.Model(inputs=inputs, outputs=x)

where is vectorize_layer = tf.keras.layers.TextVectorization(..., ragged=True). I need a different size of sentences as input to the model. For more info here is docs: https://www.tensorflow.org/guide/ragged_tensor#tensorflow_apis

The issue is:

TypeError Traceback (most recent call last) Cell In[6], line 1 ----> 1 inputs = tf.keras.layers.Input(shape=[None], dtype=tf.int64, ragged=True) 2 x = tf.keras.layers.Embedding(input_dim=vectorize_layer.vocabulary_size(), output_dim=16)(inputs) 3 m = tf.keras.Model(inputs=inputs, outputs=x)

TypeError: Input() got an unexpected keyword argument 'ragged'

Thanks for your reply.

[fchollet]

Keras 3 does not support Ragged tensors at this time. You should drop the ragged argument.

[markub3327]

It is not an option that I need. Does not exist the path to I can solve it? I would like to have a different sentence length. Without padding it is consuming the memory unnecessarily. Thanks.

[fchollet]

You can bucket your inputs by sequence length and reduce your batch size in order to minimize padding requirements. With small batch sizes and sufficient bucketing padding becomes pretty much unnecessary.

[markub3327]

@fchollet This is working:

sample = [
    'A B',
    'A B C',
    'A B C D',
    'A B C D E',
    'A B C D E F',
    'A B C D E F G',
    'A B C D E F G H',
    'A B C D E F G H I',
    'A B C D E F G H I J',
    'A B C D E F G H I J K',
    'A B C D E F G H I J K L',
]
ds = tf.data.Dataset.from_tensor_slices(sample)
ds = ds.map(lambda x: tf.strings.split(x)).repeat().bucket_by_sequence_length(
    element_length_func=lambda x: tf.shape(x)[0],
    bucket_boundaries=[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
    bucket_batch_sizes=[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
)

for x in ds.take(10):
    print(x)

Result

tf.Tensor( [[b'A' b'B'] [b'A' b'B'] [b'A' b'B'] [b'A' b'B']], shape=(4, 2), dtype=string) tf.Tensor( [[b'A' b'B' b'C'] [b'A' b'B' b'C'] [b'A' b'B' b'C'] [b'A' b'B' b'C']], shape=(4, 3), dtype=string) tf.Tensor( [[b'A' b'B' b'C' b'D'] [b'A' b'B' b'C' b'D'] [b'A' b'B' b'C' b'D'] [b'A' b'B' b'C' b'D']], shape=(4, 4), dtype=string) tf.Tensor( [[b'A' b'B' b'C' b'D' b'E'] [b'A' b'B' b'C' b'D' b'E'] [b'A' b'B' b'C' b'D' b'E'] [b'A' b'B' b'C' b'D' b'E']], shape=(4, 5), dtype=string) tf.Tensor( [[b'A' b'B' b'C' b'D' b'E' b'F'] [b'A' b'B' b'C' b'D' b'E' b'F'] [b'A' b'B' b'C' b'D' b'E' b'F'] [b'A' b'B' b'C' b'D' b'E' b'F']], shape=(4, 6), dtype=string) tf.Tensor( [[b'A' b'B' b'C' b'D' b'E' b'F' b'G'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G']], shape=(4, 7), dtype=string) tf.Tensor( [[b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H']], shape=(4, 8), dtype=string) tf.Tensor( [[b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H' b'I'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H' b'I'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H' b'I'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H' b'I']], shape=(4, 9), dtype=string) tf.Tensor( [[b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H' b'I' b'J'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H' b'I' b'J'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H' b'I' b'J'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H' b'I' b'J']], shape=(4, 10), dtype=string) tf.Tensor( [[b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H' b'I' b'J' b'K'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H' b'I' b'J' b'K'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H' b'I' b'J' b'K'] [b'A' b'B' b'C' b'D' b'E' b'F' b'G' b'H' b'I' b'J' b'K']], shape=(4, 11), dtype=string)

The repeat() is only for illustration purposes. Thanks for your time.