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Results 1 - 6 of 6 for Howard (0.26 sec)

  1. tensorflow/c/eager/parallel_device/parallel_device_lib.cc

        device_threads_.emplace_back(new DeviceThread(
            devices[device_index].c_str(), is_async, in_flight_nodes_limit));
      }
    }
    
    // Necessary for a unique_ptr to a forward-declared type.
    ParallelDevice::~ParallelDevice() = default;
    
    std::unique_ptr<ParallelTensor> ParallelDevice::CopyToParallelDevice(
        TFE_Context* context, TFE_TensorHandle* tensor, TF_Status* status) const {
    C++
    - Registered: Tue Apr 30 12:39:09 GMT 2024
    - Last Modified: Fri Feb 09 07:47:20 GMT 2024
    - 25.4K bytes
    - Viewed (1)
  2. tensorflow/c/eager/tape.h

        forward_grads.resize(output_tensors.size());
        TF_RETURN_IF_ERROR(ForwardpropFromTape(
            op_type, output_tensors, backward_function_getter,
            backward_function_deleter, in_grads, absl::MakeSpan(forward_grads)));
      } else {
        TF_RETURN_IF_ERROR(
            (*forward_function)(in_grads, &forward_grads, use_batch_));
      }
      for (int i = 0; i < forward_grads.size(); ++i) {
        if (forward_grads[i] != nullptr) {
    C
    - Registered: Tue Apr 30 12:39:09 GMT 2024
    - Last Modified: Tue Apr 02 12:40:29 GMT 2024
    - 47.2K bytes
    - Viewed (1)
  3. tensorflow/c/experimental/gradients/math_grad_test.cc

          ASSERT_EQ(errors::OK, status_.code()) << status_.message();
          immediate_execution_ctx_.reset(ctx_raw);
        }
    
        // Computing numerical gradients with TensorFloat-32 is numerically
        // unstable. Some forward pass tests also fail with TensorFloat-32 due to
        // low tolerances
        enable_tensor_float_32_execution(false);
      }
    
      AbstractContextPtr immediate_execution_ctx_;
      GradientRegistry registry_;
    C++
    - Registered: Tue Mar 26 12:39:09 GMT 2024
    - Last Modified: Thu Apr 13 17:32:14 GMT 2023
    - 16.3K bytes
    - Viewed (0)
  4. tensorflow/c/eager/gradients.cc

                 const char* raw_device_name, ForwardOperation* forward_op_) {
      forward_op_->op_name = op;
      forward_op_->attrs.Reset(op);
      return op_->Reset(op, raw_device_name);
    }
    Status AddInput(AbstractOperation* op_, AbstractTensorHandle* input,
                    ForwardOperation* forward_op_) {
      TF_RETURN_IF_ERROR(op_->AddInput(input));
      forward_op_->inputs.push_back(input);
      return absl::OkStatus();
    }
    C++
    - Registered: Tue Apr 30 12:39:09 GMT 2024
    - Last Modified: Thu Feb 15 09:49:45 GMT 2024
    - 19.3K bytes
    - Viewed (0)
  5. tensorflow/c/experimental/gradients/math_grad.cc

                                        vector<AbstractTensorHandle*> f_outputs)
          : forward_inputs_(f_inputs), forward_outputs_(f_outputs) {
        for (auto input : forward_inputs_) {
          if (input) {
            input->Ref();
          }
        }
        for (auto output : forward_outputs_) {
          if (output) {
            output->Ref();
          }
        }
      }
    
    C++
    - Registered: Tue Mar 26 12:39:09 GMT 2024
    - Last Modified: Wed Feb 28 13:53:47 GMT 2024
    - 15.2K bytes
    - Viewed (0)
  6. CONTRIBUTING.md

        your response for more than 2 weeks.
    
    **4. Approved**
    
    -   Once the PR is approved, it gets `kokoro:force-run` label applied and it
        initiates CI/CD tests.
    -   We can't move forward if these tests fail.
    -   In such situations, we may request you to make further changes to your PR
        for the tests to pass.
    -   Once the tests pass, we now bring all the code into the internal code base,
    Plain Text
    - Registered: Tue Apr 30 12:39:09 GMT 2024
    - Last Modified: Thu Mar 21 11:45:51 GMT 2024
    - 15.6K bytes
    - Viewed (0)
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