64 lines
2.8 KiB
C++
64 lines
2.8 KiB
C++
/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#ifndef TENSORFLOW_CC_FRAMEWORK_GRADIENT_CHECKER_H_
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#define TENSORFLOW_CC_FRAMEWORK_GRADIENT_CHECKER_H_
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#include "tensorflow/cc/framework/ops.h"
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#include "tensorflow/cc/framework/scope.h"
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#include "tensorflow/core/framework/tensor.h"
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namespace tensorflow {
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/// Returns in 'max_error' the maximum element-wise error for dy/dx between the
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/// computed and numeric Jacobian matrices where 'xs' and 'ys' are tensors.
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/// X_T and Y_T are the c++ types for the x and y tensors, and JAC_T is a
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/// real-valued type to store the Jacobian derivatives dy/dx.
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/// This function adds operations to the graph associated with 'scope'.
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///
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/// Examples:
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/// if y = Square(x), where x (and so y) are DT_FLOAT,
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/// <X_T, Y_T, JAC_T> should be <float, float, float>
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///
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/// if y = Square(x), where x (and so y) are DT_DOUBLE,
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/// <X_T, Y_T, JAC_T> should be <double, double, double>
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///
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/// if y = Square(x), where x (and so y) are DT_COMPLEX64,
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/// <X_T, Y_T, JAC_T> should be <complex64, complex64, float>
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/// Note that JAC_T is always real-valued, and should be an appropriate
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/// precision to host the partial derivatives for dy/dx
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///
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/// if y = ComplexAbs(x) where x is DT_COMPLEX64 (so y is DT_FLOAT)
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/// <X_T, Y_T, JAC_T> should be <complex64, float, float>
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///
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/// if y = Complex(x, x) where x is DT_FLOAT (so y is DT_COMPLEX64)
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/// <X_T, Y_T, JAC_T> should be <float, complex64, float>
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template <typename X_T, typename Y_T, typename JAC_T>
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Status ComputeGradientError(const Scope& scope, const OutputList& xs,
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const std::vector<TensorShape>& x_shapes,
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const OutputList& ys,
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const std::vector<TensorShape>& y_shapes,
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JAC_T* max_error);
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/// Overload of ComputeGradientError which takes an initial value for 'x'.
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template <typename X_T, typename Y_T, typename JAC_T>
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Status ComputeGradientError(const Scope& scope, const Output& x,
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const Tensor& x_init_value, const Output& y,
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const TensorShape& y_shape, JAC_T* max_error);
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} // namespace tensorflow
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#endif // TENSORFLOW_CC_FRAMEWORK_GRADIENT_CHECKER_H_
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