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Results 21 - 30 of 45 for grads (0.16 sec)
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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; };
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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/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/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
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tensorflow/c/c_api_function.cc
if (TF_GetCode(status) != TF_OK) return; if (!grad) return; status->status = g->graph.AddFunctionDef(grad->record->fdef(), grad->record->stack_traces()); if (TF_GetCode(status) != TF_OK) return; tensorflow::GradientDef gdef; gdef.set_function_name(func->record->fdef().signature().name()); gdef.set_gradient_func(grad->record->fdef().signature().name());
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 13.6K bytes - Viewed (2) -
tensorflow/c/eager/gradients_test.cc
#include "tensorflow/c/eager/gradients_internal.h" #include "tensorflow/c/eager/unified_api_testutil.h" #include "tensorflow/c/experimental/gradients/array_grad.h" #include "tensorflow/c/experimental/gradients/math_grad.h" #include "tensorflow/c/experimental/gradients/not_differentiable.h" #include "tensorflow/c/experimental/gradients/tape/tape_context.h" #include "tensorflow/c/experimental/ops/array_ops.h"
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tensorflow/c/c_api.h
// Adds a copy of function `func` and optionally its gradient function `grad` // to `g`. Once `func`/`grad` is added to `g`, it can be called by creating // an operation using the function's name. // Any changes to `func`/`grad` (including deleting it) done after this method // returns, won't affect the copy of `func`/`grad` in `g`. // If `func` or `grad` are already in `g`, TF_GraphCopyFunction has no
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tensorflow/c/c_test_util.h
// graph_def.library().gradient() std::vector<std::pair<string, string>> GetGradDefs( const tensorflow::GraphDef& graph_def); // Returns a sorted vector of names contained in `grad_def` std::vector<string> GetFuncNames(const tensorflow::GraphDef& graph_def); class CSession { public: CSession(TF_Graph* graph, TF_Status* s, bool use_XLA = false); explicit CSession(TF_Session* session);
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SECURITY.md
[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions in TensorFlow ops can be recognized as security issues only if they are reachable and exploitable through production-grade, benign models. ### Compilation Compiling models via the recommended entry points described in [XLA](https://www.tensorflow.org/xla) and [JAX](https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html)
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cni/README.md
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