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Results 1 - 2 of 2 for JAC_T (0.02 sec)

  1. tensorflow/cc/framework/gradient_checker.cc

    template <typename T, typename JAC_T>
    typename std::enable_if<std::is_floating_point<T>::value>::type SetJacobian(
        typename TTypes<JAC_T>::Matrix* jacobian, const int row, const int col,
        const T& value, const bool expand_by_row) {
      (*jacobian)(row, col) = JAC_T{value};
    }
    
    template <typename T, typename JAC_T>
    typename std::enable_if<is_complex<T>::value>::type SetJacobian(
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Sat Apr 13 05:57:22 UTC 2024
    - 18.2K bytes
    - Viewed (0)
  2. tensorflow/cc/framework/gradient_checker.h

    /// <X_T, Y_T, JAC_T> should be <complex64, complex64, float>
    /// Note that JAC_T is always real-valued, and should be an appropriate
    /// precision to host the partial derivatives for dy/dx
    ///
    /// if y = ComplexAbs(x) where x is DT_COMPLEX64 (so y is DT_FLOAT)
    /// <X_T, Y_T, JAC_T> should be <complex64, float, float>
    ///
    /// if y = Complex(x, x) where x is DT_FLOAT (so y is DT_COMPLEX64)
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Wed Oct 05 15:35:17 UTC 2022
    - 2.8K bytes
    - Viewed (0)
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