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tensorflow/c/experimental/gradients/array_grad_test.cc
==============================================================================*/ #include "tensorflow/c/experimental/gradients/array_grad.h" #include "tensorflow/c/eager/c_api_test_util.h" #include "tensorflow/c/eager/c_api_unified_experimental_internal.h" #include "tensorflow/c/eager/unified_api_testutil.h" #include "tensorflow/c/experimental/gradients/grad_test_helper.h" #include "tensorflow/c/experimental/gradients/tape/tape_context.h"
C++ - Registered: Tue Mar 26 12:39:09 GMT 2024 - Last Modified: Wed Feb 28 13:53:47 GMT 2024 - 5K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/nn_grad_test.cc
limitations under the License. ==============================================================================*/ #include "tensorflow/c/experimental/gradients/nn_grad.h" #include "tensorflow/c/eager/c_api_test_util.h" #include "tensorflow/c/eager/unified_api_testutil.h" #include "tensorflow/c/experimental/gradients/grad_test_helper.h" #include "tensorflow/c/experimental/gradients/tape/tape_context.h" #include "tensorflow/c/experimental/ops/nn_ops.h"
C++ - Registered: Tue Mar 26 12:39:09 GMT 2024 - Last Modified: Wed Feb 28 13:53:47 GMT 2024 - 8.3K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/not_differentiable.cc
namespace tensorflow { namespace gradients { Status NotDifferentiableGradientFunction::Compute( AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> grad_outputs, absl::Span<AbstractTensorHandle*> grad_inputs) { for (int i = 0; i < grad_inputs.size(); i++) { grad_inputs[i] = nullptr; } return OkStatus(); } Status RegisterNotDifferentiable(GradientRegistry* registry, const string& op) {
C++ - Registered: Tue Feb 27 12:39:08 GMT 2024 - Last Modified: Wed Jun 15 01:15:58 GMT 2022 - 1.3K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/BUILD
], ) cc_library( name = "gradients", hdrs = [ "array_grad.h", "math_grad.h", "nn_grad.h", "not_differentiable.h", ], visibility = [ "//tensorflow:internal", ], deps = [ ":array_grad", ":math_grad", ":nn_grad", ":not_differentiable", "//tensorflow/c/eager:abstract_context",
Plain Text - Registered: Tue Apr 09 12:39:09 GMT 2024 - Last Modified: Mon Apr 01 20:39:44 GMT 2024 - 6.7K bytes - Viewed (0) -
tensorflow/c/eager/gradient_checker.h
namespace tensorflow { namespace gradients { /* Returns numerical grad inside `dtheta_approx` given `forward` model and * parameter specified by `input_index`. * * I.e. if y = <output of the forward model> and w = inputs[input_index], * this will calculate dy/dw numerically. * * `use_function` indicates whether to use graph mode(true) or eager(false). * * `numerical_grad` is the pointer to the AbstractTensorHandle* which will
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Dec 11 02:34:32 GMT 2020 - 1.8K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/custom_gradient_test.cc
public: Status Compute(AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> grad_outputs, absl::Span<AbstractTensorHandle*> grad_inputs) override { CHECK_EQ(grad_outputs.size(), 1); CHECK_EQ(grad_inputs.size(), 1); grad_inputs[0] = grad_outputs[0]; if (grad_inputs[0]) { grad_inputs[0]->Ref(); } return absl::OkStatus(); } }; // Computes: //
C++ - Registered: Tue Mar 26 12:39:09 GMT 2024 - Last Modified: Wed Feb 28 13:53:47 GMT 2024 - 4.8K bytes - Viewed (0) -
tensorflow/c/eager/gradient_checker_test.cc
float* expected_grad, int num_grad, bool use_function, double abs_error = 1e-2) { Status s; AbstractTensorHandlePtr numerical_grad; { AbstractTensorHandle* numerical_grad_raw; s = CalcNumericalGrad(ctx, model, inputs, input_index, use_function, &numerical_grad_raw); ASSERT_EQ(errors::OK, s.code()) << s.message();
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Apr 14 10:03:59 GMT 2023 - 6.5K bytes - Viewed (0) -
tensorflow/c/eager/gradients.h
// public: // Status Compute(Context* ctx, // absl::Span<AbstractTensorHandle* const> grad_inputs, // absl::Span<AbstractTensorHandle*> grad_outputs) override { // grad_outputs[0] = grad_inputs[0]; // grad_outputs[1] = grad_inputs[0]; // grad_outputs[0]->Ref(); // grad_outputs[1]->Ref(); // return OkStatus(); // } // ~AddGradientFunction() override {} // }; //
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Sep 26 10:27:05 GMT 2022 - 6.9K bytes - Viewed (0) -
tensorflow/c/eager/gradient_checker.cc
TF_Tensor* grad_tensor; TF_RETURN_IF_ERROR(GetValue(diff_quotient.get(), &grad_tensor)); float grad_data[1]; memcpy(&grad_data[0], TF_TensorData(grad_tensor), TF_TensorByteSize(grad_tensor)); TF_DeleteTensor(grad_tensor); dtheta_approx[i] = grad_data[0]; } // Populate *numerical_grad with the data from dtheta_approx.
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Feb 15 09:49:45 GMT 2024 - 7.3K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/not_differentiable.h
namespace tensorflow { namespace gradients { // Ignores `grad_outputs` and sets all entries in grad_inputs to nullptr. class NotDifferentiableGradientFunction : public GradientFunction { Status Compute(AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> grad_outputs, absl::Span<AbstractTensorHandle*> grad_inputs) override; };
C - Registered: Tue Feb 27 12:39:08 GMT 2024 - Last Modified: Thu Dec 03 22:28:48 GMT 2020 - 1.5K bytes - Viewed (0)