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Results 1 - 6 of 6 for dx (0.15 sec)
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tensorflow/c/c_api_test.cc
TF_Operation* y = Placeholder(graph_, s_, "y", TF_FLOAT); TF_Operation* xy = Mul(x, y, graph_, s_, "xy"); TF_Output dxy_dx, dxy_dy; TF_Output outputs[1] = {{xy, 0}}; TF_Output inputs[1] = {{x, 0}}; TF_AddGradients(graph_, outputs, 1, inputs, 1, nullptr, s_, &dxy_dx); ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); inputs[0] = {y, 0};
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 96.9K bytes - Viewed (3) -
tensorflow/c/experimental/gradients/math_grad.cc
* * dX = U / Y * dY = -U*X / Y^2 = (X/Y) * -U / Y = -U*Z / Y * */ AbstractTensorHandle* upstream_grad = grad_outputs[0]; AbstractTensorHandle* Y = forward_inputs_[1]; AbstractTensorHandle* Z = forward_outputs_[0]; // Calculate dX = U / Y std::string name = "Div_Grad_X"; TF_RETURN_IF_ERROR(
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) -
tensorflow/c/eager/gradient_checker_test.cc
ASSERT_EQ(errors::OK, s.code()) << s.message(); y.reset(y_raw); } float expected_dx[1] = {7.0f}; ASSERT_NO_FATAL_FAILURE(CompareNumericalAndManualGradients( MulModel, ctx_.get(), {x.get(), y.get()}, 0, expected_dx, 1, UseFunction())); } #ifdef PLATFORM_GOOGLE INSTANTIATE_TEST_SUITE_P( UnifiedCAPI, GradientCheckerTest,
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/c_api.h
// i.e., d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2... // // `dx` are used as initial gradients (which represent the symbolic partial // derivatives of some loss function `L` w.r.t. `y`). // `dx` must be nullptr or have size `ny`. // If `dx` is nullptr, the implementation will use dx of `OnesLike` for all // shapes in `y`.
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Oct 26 21:08:15 GMT 2023 - 82.3K bytes - Viewed (3) -
tensorflow/c/eager/tape.h
op_tape_.erase(op_it); } // Terminology: // // - op: a possibly composite operation, which has an entry in the tape // - 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: //
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) -
tensorflow/c/c_api.cc
NewInternalScope(&g->graph, &status->status, &g->refiner) .NewSubScope(child_scope_name); if (dx != nullptr) { std::vector<tensorflow::Output> dx_arg = OutputsFromTFOutputs(dx, ny); status->status = AddSymbolicGradients(scope, y_arg, x_arg, dx_arg, &dy_arg); } else { status->status = AddSymbolicGradients(scope, y_arg, x_arg, &dy_arg); }
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 102.3K bytes - Viewed (0)