Comments (8)
I'm just about done with this feature request. Would you care to try it out? @ethanluoyc it is released so you can update if you like.
from tensor-sensor.
Heh, wow. jax looks really cool. I literally had not heard of this! Sure, I will poke around to see how this would work. parrt: check out https://sjmielke.com/jax-purify.htm
from tensor-sensor.
Thanks for the interest! It's getting more attention these days and I have found it to be quite useful in a broader sense of ML (probabilistic modeling). Please let me know if there is anything that I can help with.
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Wow this is absolutely amazing! I will give it a try and let you know how it works!
from tensor-sensor.
Lemme know what you think. it even works for weird stuff:
jnp.fft.fft(x, norm="something weird")
from tensor-sensor.
I just tried it out. It really works like magic. My work involves working with lots of broadcasting and reshaping so when something goes wrong it was usually very hard to understand what the problem is. Your work has really made things a lot simpler!
from tensor-sensor.
That's great to hear, @ethanluoyc ! It would be very helpful if you could advertise TensorSensor in the JAX community :) thank you!
from tensor-sensor.
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Related Issues (20)
- Graphviz errors for abstract syntax tree (AST) vizualisation HOT 2
- Power `**` operator not handled HOT 1
- explain() likely affects PyTorch autograd operations
- Unhandled statements cause exceptions (Was: Nested calls to clarify can raise stacked Exceptions) HOT 3
- Seem a problem with np.ones() function HOT 3
- Identify nested clarify() calls and ignored nested ones. HOT 1
- can't handle `a = b = np.ones(3)` statements
- Can't visualize operations in pytorch modules HOT 1
- Suppress visualisation of () as operator in tree HOT 3
- Add tensor element type info HOT 3
- Improvement: See into nn.Sequential models
- Showing too many matrices for complicated operands
- Boxes for operands packed too tightly
- Default font's spacing on mac incorrect HOT 6
- Not an issue - livepython fyi HOT 2
- executing and pure_eval HOT 5
- Contribution Guidelines HOT 3
- Optional dependencies not working properly HOT 10
- Remove hard torch dependecies for keras/tensorflow user HOT 5
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