| FazBrowse GitHub Viewer | Trending | | Home |
| Tools: [Download Repo ZIP] [Original HTTPS Page] |
Sorry, something went wrong.
Integ test
fix include name NNOps to NnOps
1. extend different type tensor for sd, memory 2. inherit super class for polymorphism
Context dev
1. test idea quickly 2. sync idea 3. take type from tensor 4. make type system in ramtensor
1. implement add function 2. implement customized ram tensor constructor
…ensor into featuretensor_refactor
…r into feature_tensor_ref
Feature tensor ref initial merge commit
Add python requirements for SD preparation
Featuretensor refactor
…uTensor into featuretensor_refactor
2. define guard for deep_mnist_mlp for avoid preprocessing non include file 3. modify main function to focus on refactor point
2. modify tensorCast for name lookup
F/context cg ref
|
Noted, but broadcasting rule should extend to non-scalar cases. |
Sorry, something went wrong.
|
Yes, so just leave it here for now. |
Sorry, something went wrong.
|
@dboyliao Is this still relevant? Or can I close it? |
Sorry, something went wrong.
| Back | FazBrowse Home | New Git URL |
Supporting scalar tensor broadcasting.
ex:
tensor1: shape=(50,)
tensor2: shape=(1,)
then broadcasting tensor2 over tensor1 in AddOp.
That is, tensor1+tensor2 will be of shape (50,)
Rationale:
It's common for TensorFlow user to initialize their bias term in NN model as scaler.
So I think it's more consistent with TensorFlow's behavior and the graph pb file it generate if we support at least scalar broadcasting.