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Supporting scalar tensor broadcasting for AddOp#66
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fix include name NNOps to NnOps
1. extend different type tensor for sd, memory 2. inherit super class for polymorphism
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
f095c05 to9c7fcb1CompareNoted, but broadcasting rule should extend to non-scalar cases. |
Yes, so just leave it here for now. |
@dboyliao Is this still relevant? Or can I close it? |
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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.