* Tensorflow 2.3.0 building and passing tests.
* Added einsum and test.
* Added ByteString as a possible argument to a function.
* Support more data types for Adam.
* Move to later version of LTS on stackage.
* Added a wrapper module for convolution functions.
* Update ci build to use a later version of stack.
* Removed a deprecated import in GradientTest.
The main difference between these and the `Ref`-bases ops is the explicit
`readValue` op. I'm not sure how this should interact with gradients
and save/restore, so I'm keeping it as a separate module for now. Once we
figure out the details, we can merge it into `TensorFlow.Ops` and replace
all uses of the old `Ref`-based ops. (That would also fix #92.)
Also replaces our special case newtype `ResourceHandle` to
`Tensor Value ResourceHandle`, where `ResourceHandle` is the TF proto
corresponding to `DT_RESOURCE`.
Distinguish between "rendered" and "unrendered" Tensors.
There are now three types of `Tensor`:
- `Tensor Value a`: rendered value
- `Tensor Ref a`: rendered reference
- `Tensor Build a` : unrendered value
The extra bookkeeping makes it easier to track (and enforce) which tensors are
rendered or not. For examples where this has been confusing in the past, see
With this change, pure ops look similar to before, returning `Tensor Build`
instead of `Tensor Value`. "Stateful" (monadic) ops are unchanged. For
example:
add :: OneOf [..] t => Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
assign :: (MonadBuild m, TensorType t)
=> Tensor Ref t -> Tensor v'2 t -> m (Tensor Ref t)
The `gradients` function now requires that the variables over which it's
differentiating are pre-rendered:
gradients :: (..., Rendered v2) => Tensor v1 a -> [Tensor v2 a]
-> m [Tensor Value a]
(`Rendered v2` means that `v2` is either a `Ref` or a `Value`.)
Additionally, the implementation of `gradients` now takes care to render every
intermediate value when performing the reverse accumulation. I suspect this
fixes an exponential blowup for complicated expressions.
Adds a new type `ListOf` which wraps a heterogeneous list; for example,
`ListOf (Tensor Value) '[Int32, Float]` represents a list of two
elements: a tensor of int32s and a tensor of floats.
Also changes the `Queue2` type (which suppored pairs of tensors) to
`Queue` (which supports arbitrary lists).
This change allows us to reenable the rest of the ResourceHandle ops, and
future-proofs us against more being added. It removes the custom logic that
assumed there was a "dtype" attribute to guess what the type parameter is
(which wasn't true in general.)
When we switch to ResourceHandle (e.g., for queues and variables) we can add
parameters to the wrapper types like "Queue" on a case-by-case basis.
We should treat such attributes as regular `DataType` values rather than type
parameters; otherwise we'll get ambiguous types. As with other attributes,
they can either set by default or passed in as an explicit argument to the op.
Allows us to reenable a couple more ops.
Also fixes op lists when the same attribute specifies the length of
both an input and an output. I added a test of "shapeN" which
previously failed with the following error:
ERROR: Ran out of counts in toResult. Likely misuse of buildListOp.
