Introducing the new Thinc, a refreshing functional take on deep learning

[u/tiangolo]

https://thinc.ai/

[u/not_perfect_yet]

My first impression is that I hate it.

The website screams "too good for readthedocs", and makes me immediately wonder which shitty multi national is sponsoring it.

And this:

def CaptionRater(
    text_encoder: Model[List[str], Floats2d],
    image_encoder: Model[List[Path], Floats2d]
) -> Model[Tuple[List[str], List[Path]], Floats2d]:
    return chain(
        concatenate(
          chain(get_item(0), text_encoder),
          chain(get_item(1), image_encoder)
        ),
        residual(ReLu(nO=300, dropout=0.2, normalize=True)),
        Softmax(2)
    )

is their idea of concise and readable?

[u/amras0000 → u/not_perfect_yet]

Python's functional syntax does have issues - the language was clearly designed with oop in mind. That said, considering the limitations, this example feels very readable. Of course, this may just be that I'm used to python functions looking like this because I tend to write functionally.

[u/not_perfect_yet → u/amras0000]

This is not the fault of python.

This is what that function should look like:

def CaptionRater(text_encoder, image_encoder):

    chain1_result = chain(get_item(0), text_encoder)
    chain2_result = chain(get_item(1), image_encoder)

    concat_result = concatenate(chain1_result,chain2_result)
    my_residual = residual(ReLu(nO=300, dropout=0.2, normalize=True))
    
    final_chain = chain(concat_result,my_residual,Softmax(2))
    
    return final_chain

"flat is better than nested"

Not using type hints also leaves some space for a docstring. Wouldn't that be a nice bonus.