Comments (2)
Hi @mnishiguchi, I intend to also port TFLite in Erlang so I'll probably keep them for the completeness of the APIs for now. And I'll revisit this issue when the Erlang part is done. :)
from tflite_beam.
out_tensor.type # vs type = TFLite.TFLiteTensor.type(out_tensor)
I think this should be okay as we also have the same thing in :nx
.
iex> tensor = Nx.iota({2,2})
iex> tensor.type
{:s, 64}
iex> Nx.type(tensor)
{:s, 64}
As for TFLiteTensor.dims/1
vs tensor.shape
, I'm thinking that keeping both, but TFLiteTensor.dims/1
will behave the same as TFLite's C++ API, which gives tensor's dimension in an array, e.g., [1, 224, 224, 3]
; while tensor.shape
will follow :nx
's convention, which would return the dimensions in a tuple, e.g., {1, 224, 224, 3}
. Furthermore, I'll add a TFLiteTensor.shape/1
function to mirror what we have in :nx
.
iex> tflite_tensor.type
{:s, 64}
iex> TFLiteTensor.type(tflite_tensor)
{:s, 64}
iex> tflite_tensor.shape
{1, 224, 224, 3}
iex> TFLiteTensor.shape(tflite_tensor)
{1, 224, 224, 3}
iex> TFLiteTensor.dims(tflite_tensor)
[1, 224, 224, 3]
from tflite_beam.
Related Issues (17)
- Add a wrapper layer `TFLite.Backend` for `TfLiteTensor` HOT 1
- Segfault HOT 3
- Missing module/function docs HOT 1
- Getting "Runtime node closed unexpectedly - no connection" on example tpu notebook HOT 6
- cryptic `TFLiteTensor.to_nx/2` error message when no Nx backend provided HOT 3
- Erlang Bindings
- Parse `TFLITE_METADATA`
- Create some high-level modules
- Change name to `tflite_beam`
- Convert from `lib/tflite_beam_nif.ex` to `tflite_beam_nif.erl`
- Port all existing modules to Erlang. HOT 1
- Example: BERT Question and Answer HOT 3
- Implement BERT Tokenizer HOT 1
- rebar3: Add support for using precompiled binaries. HOT 1
- [deps] add support for TensorFlow 2.12.0
- precompile for armv6? HOT 3
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from tflite_beam.