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CONTRIBUTING.md
* If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](https://code.google.com/legal/corporate-cla-v1.0.html).
Plain Text - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Thu Mar 21 11:45:51 GMT 2024 - 15.6K bytes - Viewed (0) -
tensorflow/c/eager/parallel_device/parallel_device_lib.cc
tensorflow::mutex execution_mutex_; ExecutionState execution_state_ TF_GUARDED_BY(execution_mutex_) = ExecutionState::kIdle; // Tells the worker thread that there is new work. tensorflow::condition_variable start_execute_; // The worker thread notifies that work has finished. tensorflow::condition_variable finished_execute_; // Notifies a StartExecute that the previous Join has finished. tensorflow::condition_variable finished_join_;
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tensorflow/c/eager/tape.h
// - target: dy in dx/dy // - source: dx in dx/dy // - tensor: one of the many inputs or outputs of an operation // // Below here we do the gradient algorithm. It works as follows: // // First we filter the tape to just the subset of operations we want to // differentiate. In the process of doing so we count how many times each Tensor
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tensorflow/c/eager/gradients.cc
forward_op_->outputs.push_back(retvals[i]); } // TODO(b/166669239): This is needed to support AttrBuilder::Get for string // attributes. Number type attrs and DataType attrs work fine without this. // Consider getting rid of this and making the behavior between number types // and string consistent. forward_op_->attrs.BuildNodeDef(); std::unique_ptr<GradientFunction> gradient_fn;
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