Added TF specific documentation to DistributedEmbedding.
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cantonios
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cantonios
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| In addition to `tf.Tensor`, `DistributedEmbedding` accepts `tf.RaggedTensor` | ||
| and `tf.SparseTensor` as inputs for the embedding lookups. Ragged tensors | ||
| must be ragged in dimension 1. Note that if weights are passed, each weight |
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Is dimension 1 the dimension with index 0? Does TF actually support any other kind of raggedness?
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I meant dimension with index 1. TF supports any ragged dimension and in fact multiple ragged dimensions, a tensor can be twice ragged.
https://www.tensorflow.org/api_docs/python/tf/RaggedTensor#multiple_ragged_dimensions
https://www.tensorflow.org/api_docs/python/tf/RaggedTensor#attributes (see ragged_rank)
| In addition to `tf.Tensor`, `DistributedEmbedding` accepts `tf.RaggedTensor` | ||
| and `tf.SparseTensor` as inputs for the embedding lookups. Ragged tensors | ||
| must be ragged in dimension 1. Note that if weights are passed, each weight | ||
| tensor must be of the same type as the inputs for that particular feature |
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"same type" might be a bit confusing because we expect the datatype for indices to be integers, but weights to be floats.
Same class?
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