automatic-differentiation

In operations_broadcast_test.go there are some tests that are not yet filled in. The point is to test that broadcasting works for different shapes. The semantics of broadcast probably isn't clear, so please do send me a message for anything.

This is a good first issue for anyone looking to get interested

I'm using TF 2.0, and I get this error when I import tangent, due to a list of non-differentiable functions that includes tf.to_float (line 60), which is deprecated:

https://www.tensorflow.org/versions/r1.14/api_docs/python/tf/to_float

I found that function mod2pi is not implemented yet, but mod works. Is there any list of implemented functions? Minimal working example is:

using Zygote

# This is working
gradient(x -> mod(x, 2pi), 1.)

# This is not
gradient(x -> mod2pi(x), 1.)

Feature details

Most non-parametric operations have their matrix representation and eigenvalues defined as class attributes (matrix and eigvals). There are some, however, which define these directly within the _matrix and _eigvals classmethods (e.g., [S gate](https://github.com/PennyLaneAI/pennylane/blob/8e57efa4a85ea635665a44b19c9113f3f38acd3b/pennylane/ops/qubit/non_parametric_ops

Description

Unit vector transforms should work with a zero-length unconstrained vector.

Example

The size-1 unit vector is just a constant [1]'. This should be the result of transforming the unconstrained 0-vector []'.

Expected Output

Stan programs that use:

parameters {
  unit_vector[1] alpha;
}

should lead to alpha == [1]'.

Current Ver

Debugging Kotlin∇ code within IntelliJ IDEA can be somewhat cumbersome due to the functional API structure (lots of deeply-nested stack traces and context switching). To facilitate more user-friendly debugging, we should add support for visual debugging by exposing Kaliningraph’s built-in graph visualization capabilities. For example, the use

numpy and pandas both provide conda environment.yml files to set up a development environment for the respective projects. We should provide a similar environment.yml for aesara.

The init module has been deprecated, and the recommend approach for generating initial weights is to use the Template.shape method:

>>> from pennylane.templates import StronglyEntanglingLayers
>>> qml.init.strong_ent_layers_normal(n_layers=3, n_wires=2) # deprecated
>>> np.random.random(StronglyEntanglingLayers.shape(n_layers=3, n_wires=2))  # new approach

We should upd

I suspect some of the scalar rules should be using oneunit instead of one. For example, sign.

At the moment profiles.h (in pkg/profiles) lacks many (any?) comments. Also lots of variables are declared somewhat separately from where they are associated with heap storage.

Both these make it a bit hard to read.

It would be nicer if it was called PROFILES.h too.