diff --git a/.clang-format b/.clang-format index b84be43a..1ad24e4c 100755 --- a/.clang-format +++ b/.clang-format @@ -34,4 +34,4 @@ SpacesInParentheses: 'true' SpacesInSquareBrackets: 'false' Standard: Cpp11 TabWidth: '4' -UseTab: Never \ No newline at end of file +UseTab: Never diff --git a/examples/configs/hohlraum_MN.cfg b/examples/configs/hohlraum_MN.cfg new file mode 100644 index 00000000..1f296d41 --- /dev/null +++ b/examples/configs/hohlraum_MN.cfg @@ -0,0 +1,64 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Hohlraum Benchmarking File MN % +% Author % +% Date 27.04.2022 % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% ---- File specifications ---- +% +OUTPUT_DIR = ../result +% Output file +OUTPUT_FILE = hohlraum +% Log directory +LOG_DIR = ../result/logs +% Mesh File +MESH_FILE = meshes/hohlraum.su2 +% +% ---- Problem specifications ---- +% +PROBLEM = HOHLRAUM +SPATIAL_DIM = 3 +SOURCE_MAGNITUDE = 1.0 +% +% ---- Solver specifications ---- +% +SPHERICAL_BASIS = SPHERICAL_HARMONICS +% +CFL_NUMBER = 0.1 +TIME_FINAL = 2 +SOLVER = MN_SOLVER +RECONS_ORDER = 2 +MAX_MOMENT_SOLVER = 3 +% +% ---- Entropy settings ---- +% +ENTROPY_FUNCTIONAL = MAXWELL_BOLTZMANN +ENTROPY_OPTIMIZER = PART_REGULARIZED_NEWTON +REGULARIZER_GAMMA = 0.00005 +%NEURAL_MODEL = 7 +% +% ----- Newton Solver Specifications ---- +% +NEWTON_FAST_MODE = NO +NEWTON_ITER = 100000 +NEWTON_EPSILON = 1e-5 +NEWTON_STEP_SIZE = 0.2 +NEWTON_LINE_SEARCH_ITER = 100000 +% +% ---- Boundary Conditions ---- +% +BC_DIRICHLET = ( void ) +% +% ---- Quadrature ---- +% +QUAD_TYPE = GAUSS_LEGENDRE_TENSORIZED +QUAD_ORDER = 20 +% +% ----- Output ---- +% +VOLUME_OUTPUT = (MINIMAL, MOMENTS, DUAL_MOMENTS) +VOLUME_OUTPUT_FREQUENCY = 0 +SCREEN_OUTPUT = (ITER, MASS, RMS_FLUX, VTK_OUTPUT, CSV_OUTPUT) +SCREEN_OUTPUT_FREQUENCY = 20 +HISTORY_OUTPUT = (ITER, MASS, RMS_FLUX, VTK_OUTPUT, CSV_OUTPUT) +HISTORY_OUTPUT_FREQUENCY = 1 diff --git a/examples/configs/hohlraum_MN_neural.cfg b/examples/configs/hohlraum_MN_neural.cfg new file mode 100644 index 00000000..13592a47 --- /dev/null +++ b/examples/configs/hohlraum_MN_neural.cfg @@ -0,0 +1,57 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Hohlraum Benchmarking File MN % +% Author % +% Date 27.04.2022 % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% ---- File specifications ---- +% +OUTPUT_DIR = ../result +% Output file +OUTPUT_FILE = hohlraum_neural +% Log directory +LOG_DIR = ../result/logs +% Mesh File +MESH_FILE = meshes/hohlraum.su2 +% +% ---- Problem specifications ---- +% +PROBLEM = HOHLRAUM +SPATIAL_DIM = 2 +SOURCE_MAGNITUDE = 1.0 +% +% ---- Solver specifications ---- +% +SPHERICAL_BASIS = SPHERICAL_MONOMIALS +% +CFL_NUMBER = 0.1 +TIME_FINAL = 2 +SOLVER = MN_SOLVER_NORMALIZED +RECONS_ORDER = 2 +MAX_MOMENT_SOLVER = 3 +% +% ---- Entropy settings ---- +% +ENTROPY_FUNCTIONAL = MAXWELL_BOLTZMANN +ENTROPY_OPTIMIZER = ML +NEURAL_MODEL_MK = 11 +NEURAL_MODEL_GAMMA = 0 +NEURAL_MODEL_ENFORCE_ROTATION_SYMMETRY = NO +% +% ---- Boundary Conditions ---- +% +BC_DIRICHLET = ( void ) +% +% ---- Quadrature ---- +% +QUAD_TYPE = GAUSS_LEGENDRE_TENSORIZED_2D +QUAD_ORDER = 10 +% +% ----- Output ---- +% +VOLUME_OUTPUT = (MINIMAL, MOMENTS, DUAL_MOMENTS) +VOLUME_OUTPUT_FREQUENCY = 0 +SCREEN_OUTPUT = (ITER, MASS, RMS_FLUX, VTK_OUTPUT, CSV_OUTPUT) +SCREEN_OUTPUT_FREQUENCY = 20 +HISTORY_OUTPUT = (ITER, MASS, RMS_FLUX, VTK_OUTPUT, CSV_OUTPUT) +HISTORY_OUTPUT_FREQUENCY = 1 diff --git a/examples/configs/hohlraum_PN.cfg b/examples/configs/hohlraum_PN.cfg new file mode 100644 index 00000000..1c6186e3 --- /dev/null +++ b/examples/configs/hohlraum_PN.cfg @@ -0,0 +1,43 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Hohlraum Benchmarking File PN % +% Author % +% Date 27.04.2022 % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% ---- File specifications ---- +% +OUTPUT_DIR = ../result +% Output file +OUTPUT_FILE = hohlraum_PN +% Log directory +LOG_DIR = ../result/logs +% Mesh File +MESH_FILE = meshes/hohlraum.su2 +% +% ---- Problem specifications ---- +% +PROBLEM = HOHLRAUM +SPATIAL_DIM = 3 +SOURCE_MAGNITUDE = 10.0 +% +% ---- Solver specifications ---- +% +CFL_NUMBER = 0.1 +TIME_FINAL = 2 +SOLVER = PN_SOLVER +RECONS_ORDER = 2 +MAX_MOMENT_SOLVER = 3 +% +% +% ---- Boundary Conditions ---- +% +BC_DIRICHLET = ( void ) +% +% ----- Output ---- +% +VOLUME_OUTPUT = (MINIMAL,MOMENTS) +VOLUME_OUTPUT_FREQUENCY = 0 +SCREEN_OUTPUT = (ITER, MASS, RMS_FLUX, VTK_OUTPUT, CSV_OUTPUT) +SCREEN_OUTPUT_FREQUENCY = 20 +HISTORY_OUTPUT = (ITER, MASS, RMS_FLUX, VTK_OUTPUT, CSV_OUTPUT) +HISTORY_OUTPUT_FREQUENCY = 0 diff --git a/examples/meshes/create_hohlraum_2d_mesh.py b/examples/meshes/create_hohlraum_2d_mesh.py new file mode 100644 index 00000000..84dfbc61 --- /dev/null +++ b/examples/meshes/create_hohlraum_2d_mesh.py @@ -0,0 +1,133 @@ +import pygmsh as pg +import numpy as np +import itertools +import os +from optparse import OptionParser + + +def add_block(x0, y0, x_len, y_len, char_length, geom): + coords = np.array([ + [x0, y0, 0.0], + [x0 + x_len, y0, 0.0], + [x0 + x_len, y0 + y_len, 0.0], + [x0, y0 + y_len, 0.0] + ]) + return geom.add_polygon(coords, char_length) + + +def main(): + print("---------- Start Creating the mesh ------------") + print("Parsing options") + # --- parse options --- + parser = OptionParser() + parser.add_option("-o", "--output_name", dest="output_name", default="hohlraum") + parser.add_option("-c", "--char_length", dest="char_length", default=0.025) + (options, args) = parser.parse_args() + + options.output_name = str(options.output_name) + options.char_length = float(options.char_length) + char_length = options.char_length + geom = pg.opencascade.Geometry() + domain = add_block(-0.1, -0.05, 1.45, 1.4, char_length, geom) + + boxes = [domain] + lines = [] + + # add interior polygon + coords = np.array([ + [0.85, 0.25, 0.0], + [0.45, 0.25, 0.0], + [0.45, 1.05, 0.0], + [0.85, 1.05, 0.0], + [0.85, 1.0, 0.0], + [0.5, 1.0, 0.0], + [0.5, 0.3, 0.0], + [0.85, 0.3, 0.0], + ]) + boxes.append(geom.add_polygon(coords, char_length)) + + # add outer polygon + coords = np.array([ + [0.0, 0.0, 0.0], + [1.3, 0.0, 0.0], + [1.3, 1.3, 0.0], + [0.0, 1.3, 0.0], + [0.0, 1.25, 0.0], + [1.25, 1.25, 0.0], + [1.25, 0.05, 0.0], + [0.0, 0.05, 0.0], + ]) + boxes.append(geom.add_polygon(coords, char_length)) + + """ + points = [] + points.append(geom.add_point([1.0, 1.05, 0.0], lcar=char_length)) + points.append(geom.add_point([1.05, 1.05, 0.0], lcar=char_length)) + points.append(geom.add_point([1.05, 1.0, 0.0], lcar=char_length)) + points.append(geom.add_point([1.0, 1.0, 0.0], lcar=char_length)) + lines = [] + lines.append(geom.add_line(points[0], points[1])) + lines.append(geom.add_line(points[1], points[2])) + lines.append(geom.add_line(points[2], points[3])) + lines.append(geom.add_line(points[3], points[0])) + + lineloop = geom.add_line_loop(lines) + surf = geom.add_surface(lineloop) + geom.add_physical(surf) + """ + # points = [] + # points.append(geom.add_point([0.0, 0.0, 0.0], lcar=char_length)) + # points.append(geom.add_point([1.3, 0.0, 0.0], lcar=char_length)) + # points.append(geom.add_point([1.3, 1.3, 0.0], lcar=char_length)) + # points.append(geom.add_point([0.0, 1.3, 0.0], lcar=char_length)) + # points.append(geom.add_point([0.0, 1.25, 0.0], lcar=char_length)) + # points.append(geom.add_point([0.05, 1.25, 0.0], lcar=char_length)) + # points.append(geom.add_point([1.25, 1.25, 0.0], lcar=char_length)) + # points.append(geom.add_point([1.25, 1.25, 0.0], lcar=char_length)) + # points.append(geom.add_point([1.25, 0.05, 0.0], lcar=char_length)) + # points.append(geom.add_point([0.05, 0.05, 0.0], lcar=char_length)) + # points.append(geom.add_point([0.0, 0.05, 0.0], lcar=char_length)) + # + # for i in range(len(points) - 1): + # lines.append(geom.add_line(points[i], points[i + 1])) + # lines.append(geom.add_line(points[len(points) - 1], points[0])) + # + # lineloop = geom.add_line_loop(lines) + # surf = geom.add_surface(lineloop) + # geom.add_physical(surf) + + # add left side absorption region + boxes.append(add_block(0, 0.25, 0.05, 0.8, char_length, geom)) + boxes.append(add_block(-0.05, 0.05, 0.05, 0.2, char_length, geom)) + boxes.append(add_block(-0.05, 1.05, 0.05, 0.2, char_length, geom)) + + # add line that denotes the inflow ghost cells + # coords = np.array([ + # [-0.01, -0.01, 0.0], + # [-0.01, 1.31, 0.0]]) + + # point1 = geom.add_point((0.0, 0.05, 0.0), lcar=char_length) + # point2 = geom.add_point((0.0, 0.25, 0.0), lcar=char_length) + # t = geom.add_line(point1, point2) + # geom.add_physical_line([t], label="void") + + # lines.append(t) + + geom.boolean_fragments(boxes, []) + geom.add_physical(domain.lines, label="void") + + # domain.lines.append(t) + # geom.add_raw_code("Transfinite Surface{:};") + # geom.add_raw_code('Mesh.Algorithm= 3;') + # geom.add_raw_code("Recombine Surface {:} = 0;") + + mesh_code = geom.get_code() + with open(options.output_name + ".geo", "w") as mesh_file: + mesh_file.write(mesh_code) + os.system('gmsh ' + options.output_name + '.geo -2 -format su2 -save_all') + # os.system('rm ' + options.output_name + '.geo') + print("---------- Successfully created the mesh ------------") + + +if __name__ == '__main__': + main() diff --git a/examples/meshes/create_linesource_mesh.py b/examples/meshes/create_linesource_mesh.py index 166103b9..e3da430a 100644 --- a/examples/meshes/create_linesource_mesh.py +++ b/examples/meshes/create_linesource_mesh.py @@ -29,7 +29,7 @@ def main(): char_length = options.char_length geom = pg.opencascade.Geometry() domain = add_block(-1, -1, 2,2, char_length, geom) - geom.add_raw_code('psource = newp;\nPoint(psource) = {0.0, 0.0, 0.0, '+str(char_length)+'};\nPoint{psource} In Surface{'+domain.id+'};') + #geom.add_raw_code('psource = newp;\nPoint(psource) = {0.0, 0.0, 0.0, '+str(char_length)+'};\nPoint{psource} In Surface{'+domain.id+'};') geom.add_physical(domain.lines, label="void") @@ -37,7 +37,7 @@ def main(): with open( options.output_name +".geo","w") as mesh_file: mesh_file.write(mesh_code) os.system('gmsh ' + options.output_name +'.geo -2 -format su2 -save_all') - os.system('rm ' + options.output_name +'.geo') + #os.system('rm ' + options.output_name +'.geo') print("---------- Successfully created the mesh ------------") if __name__ == '__main__': diff --git a/include/common/config.hpp b/include/common/config.hpp index a4a2a789..683477a5 100644 --- a/include/common/config.hpp +++ b/include/common/config.hpp @@ -110,7 +110,9 @@ class Config bool _newtonFastMode; /*!< @brief If true, we skip the NewtonOptimizer for quadratic entropy and assign alpha = u */ double _regularizerGamma; /*!< @brief Regularization parameter for the regularized closure */ // NeuralModel - unsigned short _neuralModel; /*!< @brief Version number of the employed neural model */ + unsigned short _neuralModel; /*!< @brief Version number of the employed neural model */ + unsigned short _neuralGamma; /*!< @brief Gamma value (regularization parameter) of the employed neural model */ + bool _enforceNeuralRotationalSymmetry; /*!< @brief Flag if rotational symmtry of model is enforced */ // Output Options unsigned short _nVolumeOutput; /*!< @brief Number of volume outputs */ std::vector _volumeOutput; /*!< @brief Output groups for volume output*/ @@ -313,6 +315,8 @@ class Config double inline GetRegularizerGamma() const { return _regularizerGamma; } // Neural Closure unsigned short inline GetModelMK() { return _neuralModel; } + unsigned short inline GetNeuralModelGamma() { return _neuralGamma; } + bool inline GetEnforceNeuralRotationalSymmetry() { return _enforceNeuralRotationalSymmetry; } // Boundary Conditions BOUNDARY_TYPE GetBoundaryType( std::string nameMarker ) const; /*!< @brief Get Boundary Type of given marker */ @@ -340,7 +344,7 @@ class Config SAMPLER_NAME inline GetSamplerName() { return _sampler; } unsigned long inline GetTrainingDataSetSize() { return _tainingSetSize; } bool inline GetSizeByDimension() { return _sizeByDimension; } - unsigned long inline GetMaxValFirstMoment() { return _maxValFirstMoment; } // Deprecated + unsigned long inline GetMaxValFirstMoment() { return _maxValFirstMoment; } // Deprecated double GetRealizableSetEpsilonU0() { return _RealizableSetEpsilonU0; } double GetRealizableSetEpsilonU1() { return _RealizableSetEpsilonU1; } bool inline GetNormalizedSampling() { return _normalizedSampling; } diff --git a/include/common/globalconstants.hpp b/include/common/globalconstants.hpp index 4c692d14..8a76ede2 100644 --- a/include/common/globalconstants.hpp +++ b/include/common/globalconstants.hpp @@ -101,7 +101,8 @@ enum PROBLEM_NAME { PROBLEM_StarmapValidation, PROBLEM_RadiationCT, PROBLEM_Meltingcube, - PROBLEM_Meltingcube1D + PROBLEM_Meltingcube1D, + PROBLEM_Hohlraum }; inline std::map Problem_Map{ { "LINESOURCE", PROBLEM_Linesource }, @@ -114,7 +115,8 @@ inline std::map Problem_Map{ { "LINESOURCE", PROBLEM_ { "WATERPHANTOM_1D", PROBLEM_Waterphantom1D }, { "RADIATIONCT", PROBLEM_RadiationCT }, { "MELTINGCUBE", PROBLEM_Meltingcube }, - { "MELTINGCUBE_1D", PROBLEM_Meltingcube1D } }; + { "MELTINGCUBE_1D", PROBLEM_Meltingcube1D }, + { "HOHLRAUM", PROBLEM_Hohlraum } }; // Kernel name enum KERNEL_NAME { KERNEL_Isotropic, KERNEL_Isotropic1D }; diff --git a/include/common/mesh.hpp b/include/common/mesh.hpp index b6531246..283d3b44 100644 --- a/include/common/mesh.hpp +++ b/include/common/mesh.hpp @@ -104,6 +104,12 @@ class Mesh * @return dimension: numCells */ const std::vector& GetBoundaryTypes() const; + /*! @brief Set a boundary type for a cell. Use with caution! + * @param idx_cell: cell index + * @param boundary_type: boundary_type to change the cell to + * @return void */ + void SetBoundaryType( int idx_cell, BOUNDARY_TYPE boundary_type ); + /*! @brief Returns the minimal and maximal coordinates of all nodes for each dimension * @return dimension: dim */ const std::vector> GetBounds() const; diff --git a/include/datagenerator/datageneratorregression.hpp b/include/datagenerator/datageneratorregression.hpp index 0fc37efb..134b633a 100644 --- a/include/datagenerator/datageneratorregression.hpp +++ b/include/datagenerator/datageneratorregression.hpp @@ -36,6 +36,11 @@ class DataGeneratorRegression : public DataGeneratorBase virtual void CheckRealizability() = 0; /*!< @brief Debugging helper. Will be removed */ virtual void ComputeSetSizeU() = 0; /*!< @brief Computes the size of the training set, depending on the chosen settings.*/ void ComputeSetSizeAlpha(); /*!< @brief Computes the seSize for alphasampling */ + + // Debugging + Matrix CreateRotator( const Vector& uFirstMoment ); + Vector RotateM1( Vector& vec, Matrix& R ); + Matrix RotateM2( Matrix& vec, Matrix& R, Matrix& Rt ); }; #endif // DATAGENERATORREGRESSION_H diff --git a/include/optimizers/neuralnetworkoptimizer.hpp b/include/optimizers/neuralnetworkoptimizer.hpp index 3bdc371d..4a66390b 100644 --- a/include/optimizers/neuralnetworkoptimizer.hpp +++ b/include/optimizers/neuralnetworkoptimizer.hpp @@ -30,6 +30,7 @@ class NeuralNetworkOptimizer : public OptimizerBase unsigned _nq; /*!< @brief number of quadrature points */ Vector _weights; /*!< @brief quadrature weights, dim(_weights) = (_nq) */ + std::string _tfModelInputName; /*!< @brief Name of the tf model input */ cppflow::model* _tfModel; /*!< @brief wrapper object for the compiled tensorflow model*/ cppflow::tensor _modelInput; /*!< @brief model input tensor. dims: _nCellsx_nSys*/ std::vector _modelServingVectorU; /*!< @brief model input as a 1D vector. dims: _nCells*(_nSys-1) */ @@ -38,6 +39,14 @@ class NeuralNetworkOptimizer : public OptimizerBase // std::vector _modelOutput; /*!< @brief model input tensor. dims: _nModelOutputx_nCellsx_nSys*/ unsigned _nSystem; /*!< @brief size of the moment system including zero order moment*/ VectorVector _reducedMomentBasis; /*!< @brief reduced basis functions (excluding order zero) */ + + vector _rotationMats; /*!< @brief vector of Rotation matrices for symmetry enforcing */ + vector _rotationMatsT; /*!< @brief vector of transpose Rotation matrices for symmetry enforcing */ + + Matrix CreateRotator( const Vector& uFirstMoment ); /*!< @brief Creates a rotation matrix R using the first moment of a momnet vector */ + Vector RotateM1( Vector& vec, Matrix& R ); /*!< @brief Rotates the M1 part of a 2D moment vector using a rotation matrix R */ + /*!< @brief Rotates the tensorized M2 part of a 2D moment vector using a rotation matrix R */ + Matrix RotateM2( Matrix& vec, Matrix& R, Matrix& Rt ); }; #else // Dummy class diff --git a/include/problems/hohlraum.hpp b/include/problems/hohlraum.hpp new file mode 100644 index 00000000..365a18e5 --- /dev/null +++ b/include/problems/hohlraum.hpp @@ -0,0 +1,34 @@ +#ifndef HOHLRAUM_H +#define HOHLRAUM_H + +#include "problems/problembase.hpp" + +class Hohlraum : public ProblemBase +{ + private: + Hohlraum() = delete; + Vector _scatteringXS; /*!< @brief Vector of scattering crosssections */ + Vector _totalXS; /*!< @brief Vector of total crosssections */ + + public: + Hohlraum( Config* settings, Mesh* mesh ); + virtual ~Hohlraum(); + virtual std::vector GetExternalSource( const Vector& energies ) override; + virtual VectorVector SetupIC() override; + VectorVector GetScatteringXS( const Vector& energies ) override final; + VectorVector GetTotalXS( const Vector& energies ) override final; +}; + +class Hohlraum_Moment : public Hohlraum +{ + private: + Hohlraum_Moment() = delete; + + public: + Hohlraum_Moment( Config* settings, Mesh* mesh ); + ~Hohlraum_Moment(); + std::vector GetExternalSource( const Vector& energies ) override final; + VectorVector SetupIC() override final; +}; + +#endif // HOHLRAUM_H diff --git a/include/problems/radiationctimage.hpp b/include/problems/radiationctimage.hpp index e4525383..2f66efc6 100644 --- a/include/problems/radiationctimage.hpp +++ b/include/problems/radiationctimage.hpp @@ -1,39 +1,33 @@ -#ifndef ISOTROPICSOURCE2D_CT_H -#define ISOTROPICSOURCE2D_CT_H +#ifndef RADIATION_CT_H +#define RADIATION_CT_H #include "problems/problembase.hpp" class RadiationCTImage : public ProblemBase -{ - protected: - double NormPDF( double x, double mu, double sigma ); /*!< Creates an 1D normal distribution at x with mean mu and stddev sigma */ - +{ private: RadiationCTImage() = delete; public: RadiationCTImage( Config* settings, Mesh* mesh ); - virtual ~RadiationCTImage(); - virtual std::vector GetExternalSource( const Vector& energies ); - virtual VectorVector SetupIC(); - std::vector GetDensity( const VectorVector& cellMidPoints ); - VectorVector GetScatteringXS( const Vector& energies ) override; - VectorVector GetTotalXS( const Vector& energies ) override; + ~RadiationCTImage(); + std::vector GetExternalSource( const Vector& energies ) override; + VectorVector SetupIC() override; + std::vector GetDensity( const VectorVector& cellMidPoints ) override final; + VectorVector GetScatteringXS( const Vector& energies ) override final; + VectorVector GetTotalXS( const Vector& energies ) override final; }; -class RadiationCTImage_Moment : public ProblemBase +class RadiationCTImage_Moment : public RadiationCTImage { private: RadiationCTImage_Moment() = delete; public: RadiationCTImage_Moment( Config* settings, Mesh* mesh ); - virtual ~RadiationCTImage_Moment(); - virtual std::vector GetExternalSource( const Vector& energies ); - virtual VectorVector SetupIC(); - std::vector GetDensity( const VectorVector& cellMidPoints ); - VectorVector GetScatteringXS( const Vector& energies ) override; - VectorVector GetTotalXS( const Vector& energies ) override; + ~RadiationCTImage_Moment(); + std::vector GetExternalSource( const Vector& energies ) override final; + VectorVector SetupIC() override final; }; -#endif // ISOTROPICSOURCE2D_CT_H +#endif // RADIATION_CT_H diff --git a/include/solvers/csdmnsolver.hpp b/include/solvers/csdmnsolver.hpp index f329c9c9..a3b5db5d 100644 --- a/include/solvers/csdmnsolver.hpp +++ b/include/solvers/csdmnsolver.hpp @@ -26,7 +26,6 @@ class CSDMNSolver : public MNSolver void FVMUpdate( unsigned idx_energy ) override; void PrepareVolumeOutput() override; void WriteVolumeOutput( unsigned idx_pseudoTime ) override; - Vector ConstructFlux( unsigned idx_cell ) override; // Helper Functions for CSD double NormPDF( double x, double mu, double sigma ); diff --git a/include/solvers/mnsolver.hpp b/include/solvers/mnsolver.hpp index 18339ee4..dc2b749b 100644 --- a/include/solvers/mnsolver.hpp +++ b/include/solvers/mnsolver.hpp @@ -54,10 +54,6 @@ class MNSolver : public SolverBase virtual void FluxUpdate() override; virtual void IterPreprocessing( unsigned /*idx_iter*/ ) override; virtual void IterPostprocessing( unsigned /*idx_iter*/ ) override; - /*! @brief Construct flux by computing the Moment of the sum of FVM discretization at the interface of cell - * @param idx_cell current cell id - * @returns sum over all neighbors of flux for all moments at interface of idx_cell, idx_neighbor */ - virtual Vector ConstructFlux( unsigned idx_cell ); void FluxUpdatePseudo1D(); // Helper void FluxUpdatePseudo2D(); // Helper diff --git a/include/toolboxes/textprocessingtoolbox.hpp b/include/toolboxes/textprocessingtoolbox.hpp index c7f1e967..ad3e2519 100644 --- a/include/toolboxes/textprocessingtoolbox.hpp +++ b/include/toolboxes/textprocessingtoolbox.hpp @@ -78,6 +78,15 @@ inline void PrintVectorToFile( const Vector vec, std::string filename, unsigned myfile.close(); } +inline void PrintCppVectorToFile( const std::vector vec, std::string filename, unsigned vecsize ) { + std::ofstream myfile; + myfile.open( filename ); + for( unsigned i = 0; i < vecsize; i++ ) { + myfile << vec[i] << "\n"; + } + myfile.close(); +} + inline void PrintVectorVectorToFile( const VectorVector vecvec, std::string filename, unsigned size_outer, unsigned size_inner ) { std::ofstream myfile; myfile.open( filename ); @@ -86,7 +95,7 @@ inline void PrintVectorVectorToFile( const VectorVector vecvec, std::string file myfile << vecvec[i][j] << ","; } myfile << vecvec[i][size_inner - 1]; - myfile << ";\n"; + myfile << "\n"; } myfile.close(); } diff --git a/python/callNN_MK1.py b/python/callNN_MK1.py deleted file mode 100644 index 53f63d7a..00000000 --- a/python/callNN_MK1.py +++ /dev/null @@ -1,65 +0,0 @@ -# imports -import tensorflow as tf -import numpy as np -import math - -# Custom Loss -def custom_loss1dMBPrime(): # (label,prediciton) - def loss(u_input, alpha_pred): - return 0.5*tf.square(4*math.pi*np.sqrt(1/(4*np.pi))*tf.math.exp(alpha_pred*np.sqrt(1/(4*np.pi))) - u_input) - return loss - - -def initialize_network(): - # Load model - model = tf.keras.models.load_model('neural_network_model/_EntropyLoss_1_300_M_0', custom_objects={ 'loss':custom_loss1dMBPrime }) - - # Check its architecture - model.summary() - - return model - -# make the network a gobal variable here -model = initialize_network() - -def call_network(input): - inputNP = np.asarray([input]) - - predictions = model.predict(inputNP) - - return predictions[0] - - -def call_networkBatchwise(input): - - #print(input) - inputNP = np.asarray(input) - #print(inputNP.shape) - #print(inputNP) - - predictions = model.predict(inputNP) - - #print(predictions) - - size = predictions.shape[0]*predictions.shape[1] - test = np.zeros(size) - for i in range(0,size): - test[i] = predictions.flatten(order='C')[i] - return test - - #return predictions.flatten(order='C')[0] - - -def main(): - input = [[1], [2], [3], [2], [3], [4], [5]] - - # initialize_network() - print(call_network(1)) - print("-----") - print(call_networkBatchwise(input)) - - return 0 - - -if __name__ == '__main__': - main() diff --git a/python/callNN_MK3.py b/python/callNN_MK3.py deleted file mode 100644 index 3f8b5f3c..00000000 --- a/python/callNN_MK3.py +++ /dev/null @@ -1,292 +0,0 @@ -# imports -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import tensorflow as tf -import tensorflow.keras.backend as K - -plt.style.use("kitish") # or "kitishnotex" to avoid text rendering with TeX. - -# in-project imports -#import sphericalquadpy as sqp - - - - -def initialize_network(): - # initalizes the MK3 network - - # Specify the Problem specific parameters - quadOrder = 10 # QuadOrder for training - BasisDegree = 2 # Determines the number of moments used - - # Load model - model = tf.keras.models.load_model('neural_network_model/MK3_nnM_1/model') # ,custom_objects={'loss': cLoss_FONC_varD(quadOrder, BasisDegree)}) - - # Check its architecture - model.summary() - return model - - -# make the network a gobal variable here -model = initialize_network() - - -def main(): - # 1) Read in test data - (u_test, alpha_test) = read_data("testData_Solver_M2.csv") - print("Read test data") - # 2) Print test data for overview - plot_data(u_test, alpha_test) - - # 3) Evaluate Network - print("Evaluate Network") - - # initialize_network() - print(call_network(u_test[0, :])) - print("Single input tested") - - # a) Evaluate model with custom loss - print(model.evaluate(u_test, u_test, verbose=1)) - print("Custom Loss evaluated") - - # b) Evaluate model with RMSE loss - modelRMSE = tf.keras.models.load_model('nn_MK3_M2/nn_model', custom_objects={'loss': tf.keras.losses.mse}) - print(model.evaluate(u_test, alpha_test, verbose=1)) - print("RMSE Loss evaluated") - - print("Network testing finished!") - return 0 - - -def call_network(input): - # Input: input.shape = (,nMaxMoment), nMaxMoment = 9 in case of MK3 - # ==> check this! - - inputNP = np.asarray([input]) - predictions = model.predict(inputNP) - return predictions[0] - - -def call_networkBatchwise(input): - # Input: input.shape = (batchSize,nMaxMoment), nMaxMoment = 9 in case of MK3 - # ==> check this! - - #print(input) - #print("printed raw input") - inputNP = np.asarray(input) - #print(inputNP.shape) - #print("printed input shape") - #print(inputNP) - #print("printed nparray input") - - predictions = model.predict(inputNP) - - # print(predictions) - - size = predictions.shape[0] * predictions.shape[1] - test = np.zeros(size) - for i in range(0, size): - test[i] = predictions.flatten(order='C')[i] - #print("print result") - #print(test) - #print(test.shape) - return test - - # return predictions.flatten(order='C')[0] - - -def read_data(filename): - # Note! This is essentially the same code as preprocess_data(filename) of trainNN_MK1.py. Needs to be refactored! - - ### ------- Code Starts Here ----- #### - - # reading csv file - dataFrameInput = pd.read_csv(filename) # outputs a dataframe object - - idx = 0 - xDataList = list() - yDataList = list() - data = dataFrameInput.values # numpy array - - ## Somehow t = 0 has an odd number of elements, just cut it out - - for row in data: - if (row[2] > 0): - if (idx % 2 == 0): - xDataList.append(row) - else: - yDataList.append(row) - - idx = idx + 1 - - # merge the lists - DataList = list() - for rowX, rowY in zip(xDataList, yDataList): - DataList.append([rowX, rowY]) - - # Shuffle data - # random.shuffle(DataList) - - DataArray = np.asarray(DataList) - - # Strip off header information, i.e. the first 3 cols - DataArraySlim = DataArray[:, :, 3:] - - xData = DataArraySlim[:, 0, :] - yData = DataArraySlim[:, 1, :] - - return (xData, yData) - - -def plot_data(xDataTrain, yDataTrain): - fig, axs = plt.subplots(4) - axs[0].plot(xDataTrain[:, 0]) - axs[0].set_title('$m_0^0$') - axs[0].set_ylabel('value') - axs[0].set_xlabel('sample id') - - axs[1].plot(xDataTrain[:, 1]) - axs[1].set_title('$m_1^-1$') - axs[1].set_ylabel('value') - axs[1].set_xlabel('sample id') - - axs[2].plot(xDataTrain[:, 2]) - axs[2].set_title('$m_1^0$') - axs[2].set_ylabel('value') - axs[2].set_xlabel('sample id') - - axs[3].plot(xDataTrain[:, 3]) - axs[3].set_title('$m_1^1$') - axs[3].set_ylabel('value') - axs[3].set_xlabel('sample id') - - plt.show() - - return 0 - -# Custom Loss -''' -def cLoss_FONC_varD(qOrder, bDegree): - # 1) Compute basis at quadrature points of GaussLegendreQuadrature - def computeBasis(quadorder, basisDegree): - Q = sqp.gausslegendre.GaussLegendre(order=quadorder) - - # a) compute quadrature points. - quadPts = Q.computequadpoints(quadorder) - quadWeights = Q.computequadweights(quadorder) - # b) Compute spherical harmonics basis at quadPts - mBasisList = list() - for l in range(0, basisDegree + 1): - for k in range(-l, l + 1): - temp = sqp.tools.sphericalharmonics.ylm(k, l, quadPts[:, 0], quadPts[:, 1], quadPts[:, 2]) - if (k < 0): - mBasisList.append(temp.imag) - elif (k == 0): - mBasisList.append(temp.real) - else: # (k > 0): - mBasisList.append(temp.real) - - temp = np.array(mBasisList) - - ### Manual computation of spherical harmonics up to order 2### - # the first nine harmonics: - def Y0_0(mu, phi): - return np.sqrt(1 / (4 * np.pi)) - - def Y1_m1(mu, phi): - return -np.sqrt(3 / (4 * np.pi)) * np.sqrt(1 - mu * mu) * np.sin(phi) - - def Y1_0(mu, phi): - return np.sqrt(3 / (4 * np.pi)) * mu - - def Y1_1(mu, phi): - return -np.sqrt(3 / (4 * np.pi)) * np.sqrt(1 - mu * mu) * np.cos(phi) - - def Y2_m2(mu, phi): - return np.sqrt(15 / (16 * np.pi)) * (1 - mu * mu) * np.sin(2 * phi) - - def Y2_m1(mu, phi): - return -1 * np.sqrt(15 / (4 * np.pi)) * mu * np.sqrt(1 - mu * mu) * np.sin(phi) - - def Y2_0(mu, phi): - return np.sqrt(5 / (16 * np.pi)) * (3 * mu * mu - 1) - - def Y2_1(mu, phi): - return -1 * np.sqrt(15 / (4 * np.pi)) * mu * np.sqrt(1 - mu * mu) * np.cos(phi) - - def Y2_2(mu, phi): - return np.sqrt(15 / (16 * np.pi)) * (1 - mu * mu) * np.cos(2 * phi) - - # Transform karth coordinates to shperical coordinates: - thetaMu = sqp.tools.xyz2thetaphi(quadPts) # mu in [0,pi] - phi = thetaMu[:, 0] - mu = np.cos(thetaMu[:, 1]) - nPts = quadPts.shape[0] - basisManual = np.zeros(temp.shape) - - for i in range(0, nPts): # manual computation... - - basisManual[0, i] = Y0_0(mu[i], phi[i]) - basisManual[1, i] = Y1_m1(mu[i], phi[i]) - basisManual[2, i] = Y1_0(mu[i], phi[i]) - basisManual[3, i] = Y1_1(mu[i], phi[i]) - basisManual[4, i] = Y2_m2(mu[i], phi[i]) - basisManual[5, i] = Y2_m1(mu[i], phi[i]) - basisManual[6, i] = Y2_0(mu[i], phi[i]) - basisManual[7, i] = Y2_1(mu[i], phi[i]) - basisManual[8, i] = Y2_2(mu[i], phi[i]) - #### End Manual compuation #### - - return (basisManual, quadPts, quadWeights) - - # 2) Helper functions - def innerProd(alphaX, mBasisY): - # alphaX.shape = (batchSize,NmaxMoment) - # mBasisY.shape = (NmaxMoment,1) - # output.shape = (batchSize,1) - return K.dot(alphaX, mBasisY) - - def entropyDualPrimeMaxwellBoltzmann(alpha, mBasisPt): - # alpha.shape = (batchSize,NmaxMoment) - # mBasisPt.shape = (NmaxMoment,1) - # output.shape = (batchSize,NmaxMoment) - - mBasisPtTensor = tf.constant(mBasisPt, dtype=tf.float32, shape=(mBasisPt.shape[0], 1), - name='Const') # shape = (N,1) - - temp = K.exp(innerProd(alpha, mBasisPtTensor)) - return K.dot(temp, K.transpose(mBasisPtTensor)) - - def integrate(mBasis, alpha, quadWeight): - # input: mBasis.shape = (NmaxMoment, numQuadPts) - # alpha.shape = (batchSize, NmaxMoment) - # quadWeight.shape = (numQuadPts) - # output: integral.shape = (batchSize, NmaxMoment) - - (NmaxMoment, numPts) = mBasis.shape - (batchSize, NmaxMoment_redundant) = alpha.shape - - integral = tf.constant(np.zeros((batchSize, NmaxMoment)), dtype=tf.float32, name='Const') - - for i in range(0, numPts): - integral += quadWeight[i] * entropyDualPrimeMaxwellBoltzmann(alpha, mBasis[:, i]) - - return integral - - (mbasis, quadPts, quadWeights) = computeBasis(qOrder, bDegree) - - # 3) Compute the Loss - def loss(u_input, alpha_pred): - # input: u_input.shape = (batchSize, NmaxMoment) - # alpha_pred.shape = (batchSize, NmaxMoment) - # output: out.shape = (batchSize, 1) - - temp = integrate(mbasis, alpha_pred, quadWeights) - return K.sum(K.square(temp - u_input), axis=1) - - return loss -''' - -if __name__ == '__main__': - main() diff --git a/requirements.txt b/requirements.txt index 2db4a923..3f7e1738 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,3 @@ - -pygmsh=6.1.1 +pygmsh==6.1.1 numpy pydicom diff --git a/src/common/config.cpp b/src/common/config.cpp index 58068f87..0ea69306 100644 --- a/src/common/config.cpp +++ b/src/common/config.cpp @@ -295,7 +295,15 @@ void Config::SetConfigOptions() { * \ingroup Config */ AddBoolOption( "NEWTON_FAST_MODE", _newtonFastMode, false ); // Neural Entropy Closure options + /*! @brief Neural Model \n DESCRIPTION: Specifies the neural netwok architecture \n DEFAULT 11 (values: 11=input convex, 12 non-convex) + * \ingroup Config */ AddUnsignedShortOption( "NEURAL_MODEL_MK", _neuralModel, 11 ); + /*! @brief Neural Model Gamma \n DESCRIPTION: Specifies regularization parameter for neural networks \n DEFAULT 0 (values: 0,1,2,3) + \ingroup Config */ + AddUnsignedShortOption( "NEURAL_MODEL_GAMMA", _neuralGamma, 0 ); + /*! @brief Neural Model Gamma \n DESCRIPTION: Specifies regularization parameter for neural networks \n DEFAULT 0 (values: 0,1,2,3) + \ingroup Config */ + AddBoolOption( "NEURAL_MODEL_ENFORCE_ROTATION_SYMMETRY", _enforceNeuralRotationalSymmetry, false ); // Mesh related options // Boundary Markers diff --git a/src/common/mesh.cpp b/src/common/mesh.cpp index e9a1d036..39868545 100644 --- a/src/common/mesh.cpp +++ b/src/common/mesh.cpp @@ -438,6 +438,8 @@ const std::vector& Mesh::GetBoundaryTypes() const { return _cellB const std::vector> Mesh::GetBounds() const { return _bounds; } const std::vector> Mesh::GetInterfaceMidPoints() const { return _cellInterfaceMidPoints; } +void Mesh::SetBoundaryType( int idx_cell, BOUNDARY_TYPE boundary_type ) { _cellBoundaryTypes[idx_cell] = boundary_type; } + double Mesh::GetDistanceToOrigin( unsigned idx_cell ) const { double distance = 0.0; for( unsigned idx_dim = 0; idx_dim < _dim; idx_dim++ ) { diff --git a/src/datagenerator/datageneratorregression.cpp b/src/datagenerator/datageneratorregression.cpp index 9bcc11e0..a72f8728 100644 --- a/src/datagenerator/datageneratorregression.cpp +++ b/src/datagenerator/datageneratorregression.cpp @@ -10,6 +10,8 @@ #include "optimizers/newtonoptimizer.hpp" #include "toolboxes/errormessages.hpp" +#include "toolboxes/textprocessingtoolbox.hpp" + #include DataGeneratorRegression::DataGeneratorRegression( Config* settings ) : DataGeneratorBase( settings ) { @@ -32,6 +34,47 @@ void DataGeneratorRegression::ComputeTrainingData() { // --- Postprocessing ComputeRealizableSolution(); + + // debugging purposes for rotation + TextProcessingToolbox::PrintVectorVector( _uSol ); + TextProcessingToolbox::PrintVectorVector( _alpha ); + + std::cout << "here\n"; + + VectorVector rot_uSol = VectorVector( _setSize, Vector( _nTotalEntries, 0.0 ) ); + VectorVector rot_alpha_comp = VectorVector( _setSize, Vector( _nTotalEntries, 0.0 ) ); + VectorVector rot_alpha = VectorVector( _setSize, Vector( _nTotalEntries, 0.0 ) ); + + for( unsigned i = 0; i < _setSize; i++ ) { + Vector u1{ _uSol[i][1], _uSol[i][2] }; + Matrix u2{ { _uSol[i][3], _uSol[i][4] }, { _uSol[i][4], _uSol[i][5] } }; + + Vector alpha1{ _alpha[i][1], _alpha[i][2] }; + Matrix alpha2{ { _alpha[i][3], _alpha[i][4] }, { _alpha[i][4], _alpha[i][5] } }; + + Matrix rotationMat = CreateRotator( u1 ); + Matrix rotationMatT = blaze::trans( rotationMat ); + + u1 = RotateM1( u1, rotationMat ); + u2 = RotateM2( u2, rotationMat, rotationMatT ); + + rot_uSol[i][0] = (float)( u1[0] ); + rot_uSol[i][1] = (float)( u1[1] ); // should be zero + rot_uSol[i][2] = (float)( u2( 0, 0 ) ); + rot_uSol[i][3] = (float)( u2( 0, 1 ) ); + rot_uSol[i][4] = (float)( u2( 1, 1 ) ); + + rot_alpha[i][0] = (float)( alpha1[0] ); + rot_alpha[i][1] = (float)( alpha1[1] ); // should be zero + rot_alpha[i][2] = (float)( alpha2( 0, 0 ) ); + rot_alpha[i][3] = (float)( alpha2( 0, 1 ) ); + rot_alpha[i][4] = (float)( alpha2( 1, 1 ) ); + } + _optimizer->SolveMultiCell( rot_alpha_comp, rot_uSol, _momentBasis ); + + TextProcessingToolbox::PrintVectorVector( rot_alpha ); + TextProcessingToolbox::PrintVectorVector( rot_alpha_comp ); + std::cout << "here\n"; } else { // --- sample u --- @@ -128,6 +171,22 @@ void DataGeneratorRegression::ComputeTrainingData() { PrintTrainingData(); } +Matrix DataGeneratorRegression::CreateRotator( const Vector& uFirstMoment ) { + double a = uFirstMoment[0]; + double b = uFirstMoment[1]; + double c, s, r; + + r = norm( uFirstMoment ); // sqrt( a * a + b * b ); + c = a / r; + s = -b / r; + + return Matrix{ { c, -s }, { s, c } }; // Rotation Matrix +} + +Vector DataGeneratorRegression::RotateM1( Vector& vec, Matrix& R ) { return R * vec; } + +Matrix DataGeneratorRegression::RotateM2( Matrix& vec, Matrix& R, Matrix& Rt ) { return R * vec * Rt; } + void DataGeneratorRegression::ComputeEntropyH_dual() { #pragma omp parallel for schedule( guided ) for( unsigned idx_set = 0; idx_set < _setSize; idx_set++ ) { diff --git a/src/optimizers/neuralnetworkoptimizer.cpp b/src/optimizers/neuralnetworkoptimizer.cpp index e2103de9..b89d4ec6 100644 --- a/src/optimizers/neuralnetworkoptimizer.cpp +++ b/src/optimizers/neuralnetworkoptimizer.cpp @@ -51,61 +51,253 @@ NeuralNetworkOptimizer::NeuralNetworkOptimizer( Config* settings ) : OptimizerBa case SPHERICAL_HARMONICS: basisTypeStr = "Harmonic"; break; case SPHERICAL_MONOMIALS: basisTypeStr = "Monomial"; break; } - std::string modelFolder = TENSORFLOW_MODEL_PATH; - std::string tfModelPath = modelFolder + "/" + basisTypeStr + "_Mk" + modelMkStr + "_M" + polyDegreeStr + "_" + dimStr + "D"; - // std::cout << "Load Tensorflow model from:\n "; - // std::cout << tfModelPath << "\n"; + // std::string modelFolder = TENSORFLOW_MODEL_PATH; + std::string tfModelName = basisTypeStr + "_Mk" + modelMkStr + "_M" + polyDegreeStr + "_" + dimStr + "D"; + if( _settings->GetNeuralModelGamma() > 0 ) { + tfModelName += +"_gamma" + std::to_string( _settings->GetNeuralModelGamma() ); + } + std::string tfModelPath = TENSORFLOW_MODEL_PATH; + tfModelPath += "/" + tfModelName + "/best_model"; + auto log = spdlog::get( "event" ); + log->info( "Load Tensorflow model from:\n " + tfModelPath + "\n" ); // Load model - _tfModel = new cppflow::model( tfModelPath ); // load model - _modelServingVectorU.resize( _settings->GetNCells() * ( _nSystem - 1 ) ); // reserve size for model servitor + _tfModel = new cppflow::model( tfModelPath ); // load model + unsigned servingSize = _settings->GetNCells(); + if( _settings->GetEnforceNeuralRotationalSymmetry() ) { + if( _settings->GetMaxMomentDegree() > 2 ) { + ErrorMessages::Error( "This postprocessing step is currently only for M1 and M2 models available.", CURRENT_FUNCTION ); + } + servingSize *= 2; // Double number of vectors, since we mirror the rotated vector + _rotationMats.resize( _settings->GetNCells() ); + _rotationMatsT.resize( _settings->GetNCells() ); + } + _modelServingVectorU.resize( servingSize * ( _nSystem - 1 ) ); // reserve size for model servitor + + // Specify model input name + // Call Model (change call depending on model mk) (Seems to be randomly assigned by tensorflow) + _tfModelInputName = ""; + bool model_found = false; + std::vector input_name_1_models = { + "Monomial_Mk11_M1_2D", "Monomial_Mk12_M1_2D", "Monomial_Mk12_M1_2D_gamma3", "Monomial_Mk11_M2_2D", + "Monomial_Mk11_M2_2D_gamma1", "Monomial_Mk11_M2_2D_gamma2", "Monomial_Mk11_M2_2D_gamma3", "Monomial_Mk12_M2_2D", + "Monomial_Mk12_M2_2D_gamma1", "Monomial_Mk12_M2_2D_gamma2", "Monomial_Mk12_M2_2D_gamma3", "Monomial_Mk11_M3_2D", + "Monomial_Mk11_M3_2D_gamma1", "Monomial_Mk11_M3_2D_gamma2", "Monomial_Mk11_M3_2D_gamma3", "Monomial_Mk12_M3_2D", + "Monomial_Mk12_M3_2D_gamma1", "Monomial_Mk12_M3_2D_gamma2", "Monomial_Mk12_M3_2D_gamma3", "Monomial_Mk13_M3_2D", + "Monomial_Mk13_M3_2D_gamma1", "Monomial_Mk13_M3_2D_gamma2", "Monomial_Mk13_M3_2D_gamma3", "Monomial_Mk11_M4_2D", + "Monomial_Mk11_M4_2D_gamma1", "Monomial_Mk11_M4_2D_gamma2", "Monomial_Mk11_M4_2D_gamma3", "Monomial_Mk12_M4_2D", + "Monomial_Mk12_M4_2D_gamma1", "Monomial_Mk12_M4_2D_gamma2", "Monomial_Mk12_M4_2D_gamma3", "Monomial_Mk11_M5_2D", + "Monomial_Mk11_M5_2D_gamma1", "Monomial_Mk11_M5_2D_gamma2", "Monomial_Mk11_M5_2D_gamma3", "Monomial_Mk12_M5_2D", + "Monomial_Mk12_M5_2D_gamma1", "Monomial_Mk12_M5_2D_gamma2", "Monomial_Mk12_M5_2D_gamma3" }; + for( std::vector::iterator it = input_name_1_models.begin(); it != input_name_1_models.end(); ++it ) { + if( tfModelName.compare( *it ) == 0 ) { + model_found = true; + _tfModelInputName = "serving_default_input_1:0"; + break; + } + } + std::vector input_name_2_models = { "Monomial_Mk12_M1_2D_gamma1", "Monomial_Mk12_M1_2D_gamma2" }; + for( std::vector::iterator it = input_name_2_models.begin(); it != input_name_2_models.end(); ++it ) { + if( tfModelName.compare( *it ) == 0 ) { + model_found = true; + _tfModelInputName = "serving_default_x:0"; + break; + } + } + if( !model_found ) { + ErrorMessages::Error( "Model input name <" + tfModelName + "> unknown. Use Tensorflow CLI to determine input name and add it to source code", + CURRENT_FUNCTION ); + } } NeuralNetworkOptimizer::~NeuralNetworkOptimizer() {} void NeuralNetworkOptimizer::Solve( Vector& alpha, Vector& u, const VectorVector& /*moments*/, unsigned /*idx_cell*/ ) {} +Matrix NeuralNetworkOptimizer::CreateRotator( const Vector& uFirstMoment ) { + double a = uFirstMoment[0]; + double b = uFirstMoment[1]; + double c, s, r; + + r = norm( uFirstMoment ); // sqrt( a * a + b * b ); + c = a / r; + s = -b / r; + + return Matrix{ { c, -s }, { s, c } }; // Rotation Matrix +} + +Vector NeuralNetworkOptimizer::RotateM1( Vector& vec, Matrix& R ) { return R * vec; } + +Matrix NeuralNetworkOptimizer::RotateM2( Matrix& vec, Matrix& R, Matrix& Rt ) { return R * vec * Rt; } + void NeuralNetworkOptimizer::SolveMultiCell( VectorVector& alpha, VectorVector& u, const VectorVector& /*moments*/ ) { + unsigned servingSize = _settings->GetNCells(); + Matrix rot180{ { -1.0, 0.0 }, { 0.0, -1.0 } }; + + if( _settings->GetEnforceNeuralRotationalSymmetry() ) { // Rotation Preprocessing + if( _settings->GetMaxMomentDegree() == 1 ) { #pragma omp parallel for - for( unsigned idx_cell = 0; idx_cell < _settings->GetNCells(); idx_cell++ ) { - if( abs( u[idx_cell][0] ) > 1e-9 ) { - for( unsigned idx_sys = 0; idx_sys < _nSystem - 1; idx_sys++ ) { - _modelServingVectorU[idx_cell * ( _nSystem - 1 ) + idx_sys] = (float)( u[idx_cell][idx_sys + 1] / u[idx_cell][0] ); + for( unsigned idx_cell = 0; idx_cell < _settings->GetNCells(); idx_cell++ ) { + Vector u1{ u[idx_cell][1], u[idx_cell][2] }; + _rotationMats[idx_cell] = CreateRotator( u1 ); + _rotationMatsT[idx_cell] = blaze::trans( _rotationMats[idx_cell] ); + + u1 = RotateM1( u1, _rotationMats[idx_cell] ); //, _rotationMatsT[idx_cell] ); + // Save rotated moment + _modelServingVectorU[idx_cell * ( _nSystem - 1 )] = (float)( u1[0] ); + _modelServingVectorU[idx_cell * ( _nSystem - 1 ) + 1] = (float)( u1[1] ); // should be zero + + // Rotate Moment by 180 degrees and save mirrored moment + RotateM1( u1, rot180 ); + _modelServingVectorU[( _settings->GetNCells() + idx_cell ) * ( _nSystem - 1 )] = (float)( u1[0] ); // Only first moment is mirrored + _modelServingVectorU[( _settings->GetNCells() + idx_cell ) * ( _nSystem - 1 ) + 1] = (float)( u1[1] ); // should be zero } } else { - ErrorMessages::Error( "Particle Density is zero causing divide by zero error.", CURRENT_FUNCTION ); + // Rotate everything to x-Axis of first moment tensor + //#pragma omp parallel for + for( unsigned idx_cell = 0; idx_cell < _settings->GetNCells(); idx_cell++ ) { + Vector u1{ u[idx_cell][1], u[idx_cell][2] }; + Matrix u2{ { u[idx_cell][3], u[idx_cell][4] }, { u[idx_cell][4], u[idx_cell][5] } }; + + _rotationMats[idx_cell] = CreateRotator( u1 ); + _rotationMatsT[idx_cell] = blaze::trans( _rotationMats[idx_cell] ); + + u1 = RotateM1( u1, _rotationMats[idx_cell] ); + u2 = RotateM2( u2, _rotationMats[idx_cell], _rotationMatsT[idx_cell] ); + + _modelServingVectorU[idx_cell * ( _nSystem - 1 )] = (float)( u1[0] ); + _modelServingVectorU[idx_cell * ( _nSystem - 1 ) + 1] = (float)( u1[1] ); // should be zero + _modelServingVectorU[idx_cell * ( _nSystem - 1 ) + 2] = (float)( u2( 0, 0 ) ); + _modelServingVectorU[idx_cell * ( _nSystem - 1 ) + 3] = (float)( u2( 0, 1 ) ); + _modelServingVectorU[idx_cell * ( _nSystem - 1 ) + 4] = (float)( u2( 1, 1 ) ); + + // Rotate Moment by 180 degrees and save mirrored moment + u1 = RotateM1( u1, rot180 ); + u2 = RotateM2( u2, rot180, rot180 ); // mirror matrix is symmetri + _modelServingVectorU[( _settings->GetNCells() + idx_cell ) * ( _nSystem - 1 )] = (float)( u1[0] ); // Only first moment is mirrored + _modelServingVectorU[( _settings->GetNCells() + idx_cell ) * ( _nSystem - 1 ) + 1] = (float)( u1[1] ); // should be zero + + // Even Moments cancel rotation + _modelServingVectorU[( _settings->GetNCells() + idx_cell ) * ( _nSystem - 1 ) + 2] = (float)( u2( 0, 0 ) ); + _modelServingVectorU[( _settings->GetNCells() + idx_cell ) * ( _nSystem - 1 ) + 3] = (float)( u2( 0, 1 ) ); + _modelServingVectorU[( _settings->GetNCells() + idx_cell ) * ( _nSystem - 1 ) + 4] = (float)( u2( 1, 1 ) ); + } + } + servingSize *= 2; + } + else { // No Postprocessing +#pragma omp parallel for + for( unsigned idx_cell = 0; idx_cell < _settings->GetNCells(); idx_cell++ ) { + for( unsigned idx_sys = 0; idx_sys < _nSystem - 1; idx_sys++ ) { + _modelServingVectorU[idx_cell * ( _nSystem - 1 ) + idx_sys] = (float)( u[idx_cell][idx_sys + 1] ); + } } } // Create tensor from flattened vector - _modelInput = cppflow::tensor( _modelServingVectorU, { _settings->GetNCells(), _nSystem - 1 } ); + _modelInput = cppflow::tensor( _modelServingVectorU, { servingSize, _nSystem - 1 } ); - // Call Model (change call depending on model mk) // Specific for MK11 2D now + // Call model std::vector output = _tfModel->operator()( - { { "serving_default_input_1:0", _modelInput } }, { "StatefulPartitionedCall:0", "StatefulPartitionedCall:1", "StatefulPartitionedCall:2" } ); + { { _tfModelInputName, _modelInput } }, { "StatefulPartitionedCall:0", "StatefulPartitionedCall:1", "StatefulPartitionedCall:2" } ); // reform output to vector _modelServingVectorAlpha = output[1].get_data(); + + // Postprocessing + if( _settings->GetEnforceNeuralRotationalSymmetry() ) { // Rotational postprocessing + if( _settings->GetMaxMomentDegree() == 1 ) { +#pragma omp parallel for + for( unsigned idx_cell = 0; idx_cell < _settings->GetNCells(); idx_cell++ ) { + Vector alphaRed = Vector( _nSystem - 1, 0.0 ); // local reduced alpha + Vector alphaRedMirror = Vector( _nSystem - 1, 0.0 ); // local reduced mirrored alpha + for( unsigned idx_sys = 0; idx_sys < _nSystem - 1; idx_sys++ ) { + alphaRed[idx_sys] = (double)_modelServingVectorAlpha[idx_cell * ( _nSystem - 1 ) + idx_sys]; + alphaRedMirror[idx_sys] = (double)_modelServingVectorAlpha[( _settings->GetNCells() + idx_cell ) * ( _nSystem - 1 ) + idx_sys]; + // Mirror order 1 Moments + alphaRedMirror[idx_sys] = + -1 * (double)_modelServingVectorAlpha[( _settings->GetNCells() + idx_cell ) * ( _nSystem - 1 ) + idx_sys]; + + alphaRed[idx_sys] = ( alphaRed[idx_sys] + alphaRedMirror[idx_sys] ) / 2; // average (and store in alphaRed) + } + + // Rotate Back + alphaRed = RotateM1( alphaRed, _rotationMatsT[idx_cell] ); + + // Restore alpha_0 + double integral = 0.0; + for( unsigned idx_quad = 0; idx_quad < _nq; idx_quad++ ) { + integral += _entropy->EntropyPrimeDual( dot( alphaRed, _reducedMomentBasis[idx_quad] ) ) * _weights[idx_quad]; + } + alpha[idx_cell][0] = -log( integral ); // log trafo + + // Store output + for( unsigned idx_sys = 0; idx_sys < _nSystem - 1; idx_sys++ ) { + alpha[idx_cell][idx_sys + 1] = alphaRed[idx_sys]; + } + } + } + else { // Degree 2 #pragma omp parallel for - for( unsigned idx_cell = 0; idx_cell < _settings->GetNCells(); idx_cell++ ) { - Vector alphaRed = Vector( _nSystem - 1, 0.0 ); // local reduced alpha - for( unsigned idx_sys = 0; idx_sys < _nSystem - 1; idx_sys++ ) { - alphaRed[idx_sys] = (double)_modelServingVectorAlpha[idx_cell * ( _nSystem - 1 ) + idx_sys]; - alpha[idx_cell][idx_sys + 1] = alphaRed[idx_sys]; + for( unsigned idx_cell = 0; idx_cell < _settings->GetNCells(); idx_cell++ ) { + Vector alphaRed = Vector( _nSystem - 1, 0.0 ); // local reduced alpha + Vector alphaRedMirror = Vector( _nSystem - 1, 0.0 ); // local reduced mirrored alpha + for( unsigned idx_sys = 0; idx_sys < _nSystem - 1; idx_sys++ ) { + alphaRed[idx_sys] = (double)_modelServingVectorAlpha[idx_cell * ( _nSystem - 1 ) + idx_sys]; + alphaRedMirror[idx_sys] = (double)_modelServingVectorAlpha[( _settings->GetNCells() + idx_cell ) * ( _nSystem - 1 ) + idx_sys]; + if( idx_sys < 2 ) { // Miror order 1 moments back + alphaRedMirror[idx_sys] = + -1 * (double)_modelServingVectorAlpha[( _settings->GetNCells() + idx_cell ) * ( _nSystem - 1 ) + idx_sys]; + } + alphaRed[idx_sys] = ( alphaRed[idx_sys] + alphaRedMirror[idx_sys] ) / 2; // average (and store in alphaRed) + } + + // Rotate Back + Vector alpha1{ alphaRed[0], alphaRed[1] }; + Matrix alpha2{ { alphaRed[2], alphaRed[3] * 0.5 }, { alphaRed[3] * 0.5, alphaRed[4] } }; + + alpha1 = RotateM1( alpha1, _rotationMatsT[idx_cell] ); // Rotate Back + alpha2 = RotateM2( alpha2, _rotationMatsT[idx_cell], _rotationMats[idx_cell] ); // Rotate Back + // Store back-rotated alpha + alphaRed[0] = alpha1[0]; + alphaRed[1] = alpha1[1]; + alphaRed[2] = alpha2( 0, 0 ); + alphaRed[3] = 2 * alpha2( 1, 0 ); + alphaRed[4] = alpha2( 1, 1 ); + + // Restore alpha_0 + double integral = 0.0; + for( unsigned idx_quad = 0; idx_quad < _nq; idx_quad++ ) { + integral += _entropy->EntropyPrimeDual( dot( alphaRed, _reducedMomentBasis[idx_quad] ) ) * _weights[idx_quad]; + } + alpha[idx_cell][0] = -log( integral ); // log trafo + + // Store output + for( unsigned idx_sys = 0; idx_sys < _nSystem - 1; idx_sys++ ) { + alpha[idx_cell][idx_sys + 1] = alphaRed[idx_sys]; + } + } } - // Restore alpha_0 - double integral = 0.0; - for( unsigned idx_quad = 0; idx_quad < _nq; idx_quad++ ) { - integral += _entropy->EntropyPrimeDual( dot( alphaRed, _reducedMomentBasis[idx_quad] ) ) * _weights[idx_quad]; + } + else { +#pragma omp parallel for + for( unsigned idx_cell = 0; idx_cell < _settings->GetNCells(); idx_cell++ ) { + Vector alphaRed = Vector( _nSystem - 1, 0.0 ); // local reduced alpha + for( unsigned idx_sys = 0; idx_sys < _nSystem - 1; idx_sys++ ) { + alphaRed[idx_sys] = (double)_modelServingVectorAlpha[idx_cell * ( _nSystem - 1 ) + idx_sys]; + alpha[idx_cell][idx_sys + 1] = alphaRed[idx_sys]; + } + // Restore alpha_0 + double integral = 0.0; + for( unsigned idx_quad = 0; idx_quad < _nq; idx_quad++ ) { + integral += _entropy->EntropyPrimeDual( dot( alphaRed, _reducedMomentBasis[idx_quad] ) ) * _weights[idx_quad]; + } + alpha[idx_cell][0] = -log( integral ); // log trafo } - alpha[idx_cell][0] = -log( integral ); // log trafo - // rescale alpha_0 ==> done by normalized MN Solver - // alpha[idx_cell][0] += log( u[idx_cell][0] ); } - // postprocessing depending on model mk - // TextProcessingToolbox::PrintVectorVector( alpha ); + _modelServingVectorAlpha.clear(); } void NeuralNetworkOptimizer::ReconstructMoments( Vector& sol, const Vector& alpha, const VectorVector& moments ) { diff --git a/src/problems/hohlraum.cpp b/src/problems/hohlraum.cpp new file mode 100644 index 00000000..f7925605 --- /dev/null +++ b/src/problems/hohlraum.cpp @@ -0,0 +1,200 @@ +#include "problems/hohlraum.hpp" +#include "common/config.hpp" +#include "common/io.hpp" +#include "common/mesh.hpp" +#include "quadratures/qgausslegendretensorized.hpp" +#include "quadratures/quadraturebase.hpp" +#include "solvers/csdpn_starmap_constants.hpp" +#include "toolboxes/errormessages.hpp" +#include "toolboxes/interpolation.hpp" +#include "toolboxes/textprocessingtoolbox.hpp" +#include "velocitybasis/sphericalbase.hpp" +#include "velocitybasis/sphericalharmonics.hpp" + +Hohlraum::Hohlraum( Config* settings, Mesh* mesh ) : ProblemBase( settings, mesh ) { + _scatteringXS = Vector( _mesh->GetNumCells(), 0.1 ); // white area default + _totalXS = Vector( _mesh->GetNumCells(), 0.1 ); // white area default + +#pragma omp parallel for + for( unsigned idx_cell = 0; idx_cell < _mesh->GetNumCells(); idx_cell++ ) { + double x = _mesh->GetCellMidPoints()[idx_cell][0]; + double y = _mesh->GetCellMidPoints()[idx_cell][1]; + // red area + if( x < 0.05 && y > 0.25 && y < 1.05 ) { + _scatteringXS[idx_cell] = 95.0; + _totalXS[idx_cell] = 100.0; + } + // green area 1 + if( x > 0.45 && x < 0.85 && y > 0.25 && y < 0.3 ) { + _scatteringXS[idx_cell] = 90.0; + _totalXS[idx_cell] = 100.0; + } + // green area 2 + if( x > 0.45 && x < 0.85 && y > 1.0 && y < 1.05 ) { + _scatteringXS[idx_cell] = 90.0; + _totalXS[idx_cell] = 100.0; + } + // green area 3 + if( x > 0.45 && x < 0.5 && y > 0.25 && y < 1.05 ) { + _scatteringXS[idx_cell] = 90.0; + _totalXS[idx_cell] = 100.0; + } + // black area + if( x > 0.5 && x < 0.85 && y > 0.3 && y < 1.0 ) { + _scatteringXS[idx_cell] = 50.0; + _totalXS[idx_cell] = 100.0; + } + // blue area + if( x > 1.25 || y < 0.05 || y > 1.25 ) { + _scatteringXS[idx_cell] = 0.0; + _totalXS[idx_cell] = 100.0; + } + } +} + +Hohlraum::~Hohlraum() {} + +std::vector Hohlraum::GetExternalSource( const Vector& energies ) { + VectorVector Q( _mesh->GetNumCells(), Vector( 1u, 0.0 ) ); + auto cellMids = _mesh->GetCellMidPoints(); +#pragma omp parallel for + for( unsigned j = 0; j < cellMids.size(); ++j ) { + // isotropic source at lef boundary region + if( cellMids[j][0] < 0.05 ) { + Q[j] = 0.0; //_settings->GetSourceMagnitude(); + } + } + return std::vector( 1u, Q ); +} + +VectorVector Hohlraum::SetupIC() { + VectorVector psi( _mesh->GetNumCells(), Vector( _settings->GetNQuadPoints(), 1e-10 ) ); + VectorVector cellMids = _mesh->GetCellMidPoints(); + + for( unsigned j = 0; j < cellMids.size(); ++j ) { + // boundary condition: Source on left side + if( cellMids[j][0] < 0.0 && ( cellMids[j][1] > 0.0 && cellMids[j][1] < 1.3 ) ) { // test case uses ghost cells + psi[j] = _settings->GetSourceMagnitude(); + _mesh->SetBoundaryType( j, DIRICHLET ); + } + else { + psi[j] = 1e-4; + } + } + return psi; +} + +VectorVector Hohlraum::GetScatteringXS( const Vector& energies ) { return VectorVector( energies.size(), _scatteringXS ); } + +VectorVector Hohlraum::GetTotalXS( const Vector& energies ) { return VectorVector( energies.size(), _totalXS ); } + +Hohlraum_Moment::Hohlraum_Moment( Config* settings, Mesh* mesh ) : Hohlraum( settings, mesh ) {} + +Hohlraum_Moment::~Hohlraum_Moment() {} + +std::vector Hohlraum_Moment::GetExternalSource( const Vector& energies ) { + // In case of PN, spherical basis is per default SPHERICAL_HARMONICS + + double integrationFactor = ( 4 * M_PI ); + if( _settings->GetDim() == 2 ) { + integrationFactor = M_PI; + } + SphericalBase* tempBase = SphericalBase::Create( _settings ); + unsigned ntotalEquations = tempBase->GetBasisSize(); + + VectorVector Q( _mesh->GetNumCells(), Vector( ntotalEquations, 0.0 ) ); // zero could lead to problems? + VectorVector cellMids = _mesh->GetCellMidPoints(); + + Vector uIC( ntotalEquations, 0 ); + + if( _settings->GetSphericalBasisName() == SPHERICAL_MONOMIALS ) { + QuadratureBase* quad = QuadratureBase::Create( _settings ); + VectorVector quadPointsSphere = quad->GetPointsSphere(); + Vector w = quad->GetWeights(); + + double my, phi; + VectorVector moments = VectorVector( quad->GetNq(), Vector( tempBase->GetBasisSize(), 0.0 ) ); + + for( unsigned idx_quad = 0; idx_quad < quad->GetNq(); idx_quad++ ) { + my = quadPointsSphere[idx_quad][0]; + phi = quadPointsSphere[idx_quad][1]; + moments[idx_quad] = tempBase->ComputeSphericalBasis( my, phi ); + } + // Integrate <1*m> to get factors for monomial basis in isotropic scattering + for( unsigned idx_quad = 0; idx_quad < quad->GetNq(); idx_quad++ ) { + uIC += w[idx_quad] * moments[idx_quad]; + } + delete quad; + } + double kinetic_density = 0.0; //_settings->GetSourceMagnitude(); + for( unsigned j = 0; j < cellMids.size(); ++j ) { + if( cellMids[j][0] < 0.05 ) { + if( _settings->GetSphericalBasisName() == SPHERICAL_MONOMIALS ) { + Q[j] = kinetic_density * uIC / uIC[0] / integrationFactor; // Remember scaling + } + if( _settings->GetSphericalBasisName() == SPHERICAL_HARMONICS ) { + Q[j][0] = kinetic_density / integrationFactor; // first bassis function is 1/ ( 4 * M_PI ) + } + } + } + delete tempBase; // Only temporally needed + return std::vector( 1u, Q ); +} + +VectorVector Hohlraum_Moment::SetupIC() { + double integrationFactor = ( 4 * M_PI ); + if( _settings->GetDim() == 2 ) { + integrationFactor = M_PI; + } + // In case of PN, spherical basis is per default SPHERICAL_HARMONICS + SphericalBase* tempBase = SphericalBase::Create( _settings ); + unsigned ntotalEquations = tempBase->GetBasisSize(); + + VectorVector initialSolution( _mesh->GetNumCells(), Vector( ntotalEquations, 0 ) ); // zero could lead to problems? + VectorVector cellMids = _mesh->GetCellMidPoints(); + + Vector tempIC( ntotalEquations, 0 ); + + if( _settings->GetSphericalBasisName() == SPHERICAL_MONOMIALS ) { + QuadratureBase* quad = QuadratureBase::Create( _settings ); + VectorVector quadPointsSphere = quad->GetPointsSphere(); + Vector w = quad->GetWeights(); + + double my, phi; + VectorVector moments = VectorVector( quad->GetNq(), Vector( tempBase->GetBasisSize(), 0.0 ) ); + + for( unsigned idx_quad = 0; idx_quad < quad->GetNq(); idx_quad++ ) { + my = quadPointsSphere[idx_quad][0]; + phi = quadPointsSphere[idx_quad][1]; + moments[idx_quad] = tempBase->ComputeSphericalBasis( my, phi ); + } + // Integrate <1*m> to get factors for monomial basis in isotropic scattering + for( unsigned idx_quad = 0; idx_quad < quad->GetNq(); idx_quad++ ) { + tempIC += w[idx_quad] * moments[idx_quad]; + } + delete quad; + } + // Initial condition is dirac impulse at (x,y) = (0,0) ==> constant in angle ==> all moments - exept first - are zero. + double kinetic_density = 1e-4; + // std::vector _boundaryCells; + for( unsigned j = 0; j < cellMids.size(); ++j ) { + + // boundary condition: Source on left side + if( cellMids[j][0] < 0.0 && ( cellMids[j][1] > 0.0 && cellMids[j][1] < 1.3 ) ) { // test case uses ghost cells + kinetic_density = _settings->GetSourceMagnitude(); + _mesh->SetBoundaryType( j, DIRICHLET ); + } + else { + kinetic_density = 1e-4; + } + + if( _settings->GetSphericalBasisName() == SPHERICAL_MONOMIALS ) { + initialSolution[j] = kinetic_density * tempIC / tempIC[0] / integrationFactor; // Remember scaling + } + if( _settings->GetSphericalBasisName() == SPHERICAL_HARMONICS ) { + initialSolution[j][0] = kinetic_density / integrationFactor; // first bassis function is 1/ ( 4 * M_PI ) + } + } + delete tempBase; // Only temporally needed + return initialSolution; +} diff --git a/src/problems/linesource.cpp b/src/problems/linesource.cpp index ffbcdc9f..56c49fc7 100644 --- a/src/problems/linesource.cpp +++ b/src/problems/linesource.cpp @@ -35,7 +35,7 @@ double LineSource::GetAnalyticalSolution( double x, double y, double t, double / } } - return ( 2 * M_PI ) * solution; // Scaling of soolution ( 4 * M_PI ) * + return M_PI * solution; // Scaling of soolution ( 4 * M_PI ) * } double LineSource::HelperIntRho_ptc( double R, double t ) { diff --git a/src/problems/problembase.cpp b/src/problems/problembase.cpp index 3012abe0..e570329d 100644 --- a/src/problems/problembase.cpp +++ b/src/problems/problembase.cpp @@ -2,6 +2,7 @@ #include "common/config.hpp" #include "problems/aircavity1d.hpp" #include "problems/checkerboard.hpp" +#include "problems/hohlraum.hpp" #include "problems/linesource.hpp" #include "problems/meltingcube.hpp" #include "problems/phantomimage.hpp" @@ -75,6 +76,12 @@ ProblemBase* ProblemBase::Create( Config* settings, Mesh* mesh ) { else return new MeltingCube_SN_1D( settings, mesh ); } break; + case PROBLEM_Hohlraum: { + if( settings->GetIsMomentSolver() ) + return new Hohlraum_Moment( settings, mesh ); + else + return new Hohlraum( settings, mesh ); + } break; default: ErrorMessages::Error( "No valid physical problem chosen. Please check your config file", CURRENT_FUNCTION ); return nullptr; } diff --git a/src/problems/radiationctimage.cpp b/src/problems/radiationctimage.cpp index 94e0c7fe..9bd4f0e1 100644 --- a/src/problems/radiationctimage.cpp +++ b/src/problems/radiationctimage.cpp @@ -2,15 +2,14 @@ #include "common/config.hpp" #include "common/io.hpp" #include "common/mesh.hpp" +#include "quadratures/qgausslegendretensorized.hpp" +#include "quadratures/quadraturebase.hpp" +#include "solvers/csdpn_starmap_constants.hpp" #include "toolboxes/errormessages.hpp" #include "toolboxes/interpolation.hpp" -#include "velocitybasis/sphericalbase.hpp" -#include "quadratures/quadraturebase.hpp" +#include "toolboxes/textprocessingtoolbox.hpp" #include "velocitybasis/sphericalbase.hpp" #include "velocitybasis/sphericalharmonics.hpp" -#include "quadratures/qgausslegendretensorized.hpp" -#include "toolboxes/textprocessingtoolbox.hpp" -#include "solvers/csdpn_starmap_constants.hpp" #include @@ -29,36 +28,38 @@ VectorVector RadiationCTImage::SetupIC() { VectorVector psi( _mesh->GetNumCells(), Vector( _settings->GetNQuadPoints(), 1e-10 ) ); VectorVector cellMids = _mesh->GetCellMidPoints(); double s = 0.1; - double enterPositionX = 2.5; // 0.0; - double enterPositionY = 5.8; - double meanDir = M_PI/2; - double s_ang = 0.1; - double epsilon = 1e-3; + double enterPositionX = 2.5; // 0.0; + double enterPositionY = 5.8; + double meanDir = M_PI / 2; + double s_ang = 0.1; + double epsilon = 1e-3; QuadratureBase* quad = QuadratureBase::Create( _settings ); VectorVector quadPointsSphere = quad->GetPointsSphere(); - + for( unsigned j = 0; j < cellMids.size(); ++j ) { - double x = cellMids[j][0] - enterPositionX; - double y = cellMids[j][1] - enterPositionY; + double x = cellMids[j][0] - enterPositionX; + double y = cellMids[j][1] - enterPositionY; // anisotropic inflow that concentrates all particles on the last quadrature point - // for( unsigned idx_quad = 0; idx_quad < _settings->GetNQuadPoints(); idx_quad++ ) { - // double theta = acos(sqrt(1-quadPointsSphere[idx_quad][0]*quadPointsSphere[idx_quad][0])*cos(quadPointsSphere[idx_quad][1])); - // if(theta > M_PI/3 && theta < 2*M_PI/3&&quadPointsSphere[idx_quad][0]<0) { - // psi[j][idx_quad] = std::max( 1.0 / ( s * sqrt( 2 * M_PI ) ) * std::exp( -( x * x + y * y ) / ( 2 * s * s ) ), epsilon ); - // } - // } - // normal distribution also in angle - + // for( unsigned idx_quad = 0; idx_quad < _settings->GetNQuadPoints(); idx_quad++ ) { + // double theta = acos(sqrt(1-quadPointsSphere[idx_quad][0]*quadPointsSphere[idx_quad][0])*cos(quadPointsSphere[idx_quad][1])); + // if(theta > M_PI/3 && theta < 2*M_PI/3&&quadPointsSphere[idx_quad][0]<0) { + // psi[j][idx_quad] = std::max( 1.0 / ( s * sqrt( 2 * M_PI ) ) * std::exp( -( x * x + y * y ) / ( 2 * s * s ) ), epsilon ); + // } + // } + // normal distribution also in angle + for( unsigned idx_quad = 0; idx_quad < _settings->GetNQuadPoints(); idx_quad++ ) { - if(quadPointsSphere[idx_quad][0]<0) { - double theta = acos(sqrt(1-quadPointsSphere[idx_quad][0]*quadPointsSphere[idx_quad][0])*cos(quadPointsSphere[idx_quad][1])); - double ang = theta - meanDir; - psi[j][idx_quad]= std::max( 1.0 / ( s * s_ang * sqrt( 8 * M_PI * M_PI * M_PI)) * std::exp( -( x * x + y * y ) / ( 2 * s * s ) )* std::exp( -( ang * ang ) / (2* s_ang ) ), epsilon ); + if( quadPointsSphere[idx_quad][0] < 0 ) { + double theta = + acos( sqrt( 1 - quadPointsSphere[idx_quad][0] * quadPointsSphere[idx_quad][0] ) * cos( quadPointsSphere[idx_quad][1] ) ); + double ang = theta - meanDir; + psi[j][idx_quad] = std::max( 1.0 / ( s * s_ang * sqrt( 8 * M_PI * M_PI * M_PI ) ) * std::exp( -( x * x + y * y ) / ( 2 * s * s ) ) * + std::exp( -( ang * ang ) / ( 2 * s_ang ) ), + epsilon ); } } } - delete quad; return psi; } @@ -87,7 +88,9 @@ std::vector RadiationCTImage::GetDensity( const VectorVector& /*cellMidP Interpolation interp( x, y, gsImage ); std::vector result( _mesh->GetNumCells(), 0.0 ); for( unsigned i = 0; i < _mesh->GetNumCells(); ++i ) { - result[i] = std::clamp( interp( cellMidPoints[i][0], cellMidPoints[i][1] )*1.85, 0.05, 1.85 ); //Scale densities for CT to be between 0 (air) and 1.85 (bone) + result[i] = std::clamp( interp( cellMidPoints[i][0], cellMidPoints[i][1] ) * 1.85, + 0.05, + 1.85 ); // Scale densities for CT to be between 0 (air) and 1.85 (bone) } return result; } @@ -103,7 +106,8 @@ VectorVector RadiationCTImage::GetTotalXS( const Vector& /*energies*/ ) { // Specified in subclasses return VectorVector( 1, Vector( 1, 0.0 ) ); } -RadiationCTImage_Moment::RadiationCTImage_Moment( Config* settings, Mesh* mesh ) : ProblemBase( settings, mesh ) {} + +RadiationCTImage_Moment::RadiationCTImage_Moment( Config* settings, Mesh* mesh ) : RadiationCTImage( settings, mesh ) {} RadiationCTImage_Moment::~RadiationCTImage_Moment() {} @@ -119,14 +123,14 @@ std::vector RadiationCTImage_Moment::GetExternalSource( const Vect } VectorVector RadiationCTImage_Moment::SetupIC() { - if( _settings->GetSphericalBasisName() == SPHERICAL_HARMONICS ) { + if( _settings->GetSphericalBasisName() == SPHERICAL_HARMONICS ) { // In case of PN, spherical basis is per default SPHERICAL_HARMONICS in 3 velocity dimensions SphericalBase* tempBase = new SphericalHarmonics( _settings->GetMaxMomentDegree(), 3 ); unsigned ntotalEquations = tempBase->GetBasisSize(); double epsilon = 1e-3; - VectorVector initialSolution( _mesh->GetNumCells(), Vector( ntotalEquations, 0) ); + VectorVector initialSolution( _mesh->GetNumCells(), Vector( ntotalEquations, 0 ) ); VectorVector cellMids = _mesh->GetCellMidPoints(); QuadratureBase* quad = new QGaussLegendreTensorized( _settings ); @@ -144,11 +148,11 @@ VectorVector RadiationCTImage_Moment::SetupIC() { moments[idx_quad] = tempBase->ComputeSphericalBasis( my, phi ); } delete tempBase; - double s = 0.1; + double s = 0.1; double enterPositionX = 2.5; // 0.0; double enterPositionY = 5.8; - double meanDir = M_PI/2; - double s_ang = 0.1; + double meanDir = M_PI / 2; + double s_ang = 0.1; for( unsigned idx_cell = 0; idx_cell < cellMids.size(); ++idx_cell ) { double x = cellMids[idx_cell][0] - enterPositionX; @@ -156,28 +160,33 @@ VectorVector RadiationCTImage_Moment::SetupIC() { // anisotropic, forward-directed particle inflow // for( unsigned idx_quad = 0; idx_quad < _settings->GetNQuadPoints(); idx_quad++ ) { // double theta = acos(sqrt(1-quadPointsSphere[idx_quad][0]*quadPointsSphere[idx_quad][0])*cos(quadPointsSphere[idx_quad][1])); - // if(theta > M_PI/3 && theta < 2*M_PI/3&&quadPointsSphere[idx_quad][0]<0) { - // cellKineticDensity[idx_quad] = std::max( 1.0 / ( s * sqrt( 2 * M_PI ) ) * std::exp( -( x * x + y * y ) / ( 2 * s * s ) ), epsilon ); + // if(theta > M_PI/3 && theta < 2*M_PI/3&&quadPointsSphere[idx_quad][0]<0) { + // cellKineticDensity[idx_quad] = std::max( 1.0 / ( s * sqrt( 2 * M_PI ) ) * std::exp( -( x * x + y * y ) / ( 2 * s * s ) ), + // epsilon ); // } // } // normal distribution also in angle - + for( unsigned idx_quad = 0; idx_quad < _settings->GetNQuadPoints(); idx_quad++ ) { - if(quadPointsSphere[idx_quad][0]<0) { - double theta = acos(sqrt(1-quadPointsSphere[idx_quad][0]*quadPointsSphere[idx_quad][0])*cos(quadPointsSphere[idx_quad][1])); + if( quadPointsSphere[idx_quad][0] < 0 ) { + double theta = + acos( sqrt( 1 - quadPointsSphere[idx_quad][0] * quadPointsSphere[idx_quad][0] ) * cos( quadPointsSphere[idx_quad][1] ) ); double ang = theta - meanDir; - cellKineticDensity[idx_quad] = std::max( 1.0 / ( s * s_ang * sqrt( 8 * M_PI * M_PI * M_PI)) * std::exp( -( x * x + y * y ) / ( 2 * s * s ) )* std::exp( -( ang * ang ) / (2* s_ang ) ), epsilon ); + cellKineticDensity[idx_quad] = + std::max( 1.0 / ( s * s_ang * sqrt( 8 * M_PI * M_PI * M_PI ) ) * std::exp( -( x * x + y * y ) / ( 2 * s * s ) ) * + std::exp( -( ang * ang ) / ( 2 * s_ang ) ), + epsilon ); + } } - } - + // Compute moments of this kinetic density for( unsigned idx_quad = 0; idx_quad < quad->GetNq(); idx_quad++ ) { initialSolution[idx_cell] += cellKineticDensity[idx_quad] * w[idx_quad] * moments[idx_quad]; } } - //TextProcessingToolbox::PrintVectorVector(initialSolution); - //exit(0); + // TextProcessingToolbox::PrintVectorVector(initialSolution); + // exit(0); delete quad; return initialSolution; } @@ -205,11 +214,11 @@ VectorVector RadiationCTImage_Moment::SetupIC() { } delete tempBase; - double s = 0.1; + double s = 0.1; double enterPositionX = 2.5; // 0.0; double enterPositionY = 5.8; - double meanDir = M_PI/2; - double s_ang = 0.1; + double meanDir = M_PI / 2; + double s_ang = 0.1; for( unsigned idx_cell = 0; idx_cell < cellMids.size(); ++idx_cell ) { double x = cellMids[idx_cell][0] - enterPositionX; @@ -217,21 +226,26 @@ VectorVector RadiationCTImage_Moment::SetupIC() { // anisotropic, forward-directed particle inflow // for( unsigned idx_quad = 0; idx_quad < _settings->GetNQuadPoints(); idx_quad++ ) { // double theta = acos(sqrt(1-quadPointsSphere[idx_quad][0]*quadPointsSphere[idx_quad][0])*cos(quadPointsSphere[idx_quad][1])); - // if(theta > M_PI/3 && theta < 2*M_PI/3&&quadPointsSphere[idx_quad][0]<0) { - // cellKineticDensity[idx_quad] = std::max( 1.0 / ( s * sqrt( 2 * M_PI ) ) * std::exp( -( x * x + y * y ) / ( 2 * s * s ) ), epsilon ); + // if(theta > M_PI/3 && theta < 2*M_PI/3&&quadPointsSphere[idx_quad][0]<0) { + // cellKineticDensity[idx_quad] = std::max( 1.0 / ( s * sqrt( 2 * M_PI ) ) * std::exp( -( x * x + y * y ) / ( 2 * s * s ) ), + // epsilon ); // } // } // normal distribution also in angle - + for( unsigned idx_quad = 0; idx_quad < _settings->GetNQuadPoints(); idx_quad++ ) { - if(quadPointsSphere[idx_quad][0]<0) { - double theta = acos(sqrt(1-quadPointsSphere[idx_quad][0]*quadPointsSphere[idx_quad][0])*cos(quadPointsSphere[idx_quad][1])); + if( quadPointsSphere[idx_quad][0] < 0 ) { + double theta = + acos( sqrt( 1 - quadPointsSphere[idx_quad][0] * quadPointsSphere[idx_quad][0] ) * cos( quadPointsSphere[idx_quad][1] ) ); double ang = theta - meanDir; - cellKineticDensity[idx_quad] = std::max( 1.0 / ( s * s_ang * sqrt( 8 * M_PI * M_PI * M_PI)) * std::exp( -( x * x + y * y ) / ( 2 * s * s ) )* std::exp( -( ang * ang ) / (2* s_ang ) ), epsilon ); - } + cellKineticDensity[idx_quad] = + std::max( 1.0 / ( s * s_ang * sqrt( 8 * M_PI * M_PI * M_PI ) ) * std::exp( -( x * x + y * y ) / ( 2 * s * s ) ) * + std::exp( -( ang * ang ) / ( 2 * s_ang ) ), + epsilon ); + } } - + // Compute moments of this kinetic density for( unsigned idx_quad = 0; idx_quad < quad->GetNq(); idx_quad++ ) { initialSolution[idx_cell] += cellKineticDensity[idx_quad] * w[idx_quad] * moments[idx_quad]; @@ -241,46 +255,3 @@ VectorVector RadiationCTImage_Moment::SetupIC() { return initialSolution; } } - -std::vector RadiationCTImage_Moment::GetDensity( const VectorVector& /*cellMidPoints*/ ) { - std::string imageFile = _settings->GetCTFile(); - std::string meshFile = _settings->GetMeshFile(); - Matrix gsImage = createSU2MeshFromImage( imageFile, meshFile ); - auto bounds = _mesh->GetBounds(); - auto cellMidPoints = _mesh->GetCellMidPoints(); - - double xMin = bounds[0].first; - double xMax = bounds[0].second; - double yMin = bounds[1].first; - double yMax = bounds[1].second; - - unsigned m = gsImage.rows(); - unsigned n = gsImage.columns(); - - Vector x( m ), y( n ); - for( unsigned i = 0; i < m; ++i ) { - x[i] = xMin + static_cast( i ) / static_cast( m - 1 ) * ( xMax - xMin ); - } - for( unsigned i = 0; i < n; ++i ) y[i] = yMin + static_cast( i ) / static_cast( n - 1 ) * ( yMax - yMin ); - - Interpolation interp( x, y, gsImage ); - std::vector result( _mesh->GetNumCells(), 0.0 ); - for( unsigned i = 0; i < _mesh->GetNumCells(); ++i ) { - result[i] = std::clamp( interp( cellMidPoints[i][0], cellMidPoints[i][1] )*1.85, 0.05, 1.85 ); //Scale densities for CT to be between 0 (air) and 1.85 (bone) - } - return result; -} -VectorVector RadiationCTImage_Moment::GetScatteringXS( const Vector& /*energies*/ ) { - // @TODO - // Specified in subclasses - return VectorVector( 1, Vector( 1, 0.0 ) ); -} - -VectorVector RadiationCTImage_Moment::GetTotalXS( const Vector& /*energies*/ ) { - // @TODO - // Specified in subclasses - return VectorVector( 1, Vector( 1, 0.0 ) ); -} -double RadiationCTImage::NormPDF( double x, double mu, double sigma ) { - return INV_SQRT_2PI / sigma * std::exp( -( ( x - mu ) * ( x - mu ) ) / ( 2.0 * sigma * sigma ) ); -} \ No newline at end of file diff --git a/src/solvers/csdmnsolver.cpp b/src/solvers/csdmnsolver.cpp index 47ee0bab..563ed465 100644 --- a/src/solvers/csdmnsolver.cpp +++ b/src/solvers/csdmnsolver.cpp @@ -130,57 +130,55 @@ void CSDMNSolver::IterPostprocessing( unsigned idx_iter ) { } } -Vector CSDMNSolver::ConstructFlux( unsigned idx_cell ) { - //--- Integration of moments of flux --- - double solL, solR, kineticFlux; - Vector flux( _nSystem, 0.0 ); - - for( unsigned idx_quad = 0; idx_quad < _nq; idx_quad++ ) { - kineticFlux = 0.0; // reset temorary flux - - for( unsigned idx_nbr = 0; idx_nbr < _neighbors[idx_cell].size(); idx_nbr++ ) { - if( _boundaryCells[idx_cell] == BOUNDARY_TYPE::NEUMANN && _neighbors[idx_cell][idx_nbr] == _nCells ) { - // Boundary cells are first order and mirror ghost cells - solL = _kineticDensity[idx_cell][idx_quad] / _density[idx_cell]; - solR = solL; - } - else { - // interior cell - unsigned int nbr_glob = _neighbors[idx_cell][idx_nbr]; // global idx of neighbor cell - if( _reconsOrder == 1 ) { +void CSDMNSolver::FluxUpdate() { +// Loop over the grid cells +#pragma omp parallel for + for( unsigned idx_cell = 0; idx_cell < _nCells; idx_cell++ ) { + // Dirichlet Boundaries stayd + if( _boundaryCells[idx_cell] == BOUNDARY_TYPE::DIRICHLET ) continue; + + //--- Integration of moments of flux --- + double solL, solR, kineticFlux; + Vector flux( _nSystem, 0.0 ); + + for( unsigned idx_quad = 0; idx_quad < _nq; idx_quad++ ) { + kineticFlux = 0.0; // reset temorary flux + + for( unsigned idx_nbr = 0; idx_nbr < _neighbors[idx_cell].size(); idx_nbr++ ) { + if( _boundaryCells[idx_cell] == BOUNDARY_TYPE::NEUMANN && _neighbors[idx_cell][idx_nbr] == _nCells ) { + // Boundary cells are first order and mirror ghost cells solL = _kineticDensity[idx_cell][idx_quad] / _density[idx_cell]; - solR = _kineticDensity[nbr_glob][idx_quad] / _density[nbr_glob]; - } - else if( _reconsOrder == 2 ) { - solL = _kineticDensity[idx_cell][idx_quad] / _density[idx_cell] + - _limiter[idx_cell][idx_quad] * - ( _solDx[idx_cell][idx_quad] * ( _interfaceMidPoints[idx_cell][idx_nbr][0] - _cellMidPoints[idx_cell][0] ) + - _solDy[idx_cell][idx_quad] * ( _interfaceMidPoints[idx_cell][idx_nbr][1] - _cellMidPoints[idx_cell][1] ) ); - solR = _kineticDensity[nbr_glob][idx_quad] / _density[nbr_glob] + - _limiter[nbr_glob][idx_quad] * - ( _solDx[nbr_glob][idx_quad] * ( _interfaceMidPoints[idx_cell][idx_nbr][0] - _cellMidPoints[nbr_glob][0] ) + - _solDy[nbr_glob][idx_quad] * ( _interfaceMidPoints[idx_cell][idx_nbr][1] - _cellMidPoints[nbr_glob][1] ) ); + solR = solL; } else { - ErrorMessages::Error( "Reconstruction order not supported.", CURRENT_FUNCTION ); + // interior cell + unsigned int nbr_glob = _neighbors[idx_cell][idx_nbr]; // global idx of neighbor cell + if( _reconsOrder == 1 ) { + solL = _kineticDensity[idx_cell][idx_quad] / _density[idx_cell]; + solR = _kineticDensity[nbr_glob][idx_quad] / _density[nbr_glob]; + } + else if( _reconsOrder == 2 ) { + solL = _kineticDensity[idx_cell][idx_quad] / _density[idx_cell] + + _limiter[idx_cell][idx_quad] * + ( _solDx[idx_cell][idx_quad] * ( _interfaceMidPoints[idx_cell][idx_nbr][0] - _cellMidPoints[idx_cell][0] ) + + _solDy[idx_cell][idx_quad] * ( _interfaceMidPoints[idx_cell][idx_nbr][1] - _cellMidPoints[idx_cell][1] ) ); + solR = _kineticDensity[nbr_glob][idx_quad] / _density[nbr_glob] + + _limiter[nbr_glob][idx_quad] * + ( _solDx[nbr_glob][idx_quad] * ( _interfaceMidPoints[idx_cell][idx_nbr][0] - _cellMidPoints[nbr_glob][0] ) + + _solDy[nbr_glob][idx_quad] * ( _interfaceMidPoints[idx_cell][idx_nbr][1] - _cellMidPoints[nbr_glob][1] ) ); + } + else { + ErrorMessages::Error( "Reconstruction order not supported.", CURRENT_FUNCTION ); + } } + // Kinetic flux + kineticFlux += _g->FluxXZ( _quadPoints[idx_quad], solL, solR, _normals[idx_cell][idx_nbr] ); } - // Kinetic flux - kineticFlux += _g->FluxXZ( _quadPoints[idx_quad], solL, solR, _normals[idx_cell][idx_nbr] ); + // Solution flux + flux += _momentBasis[idx_quad] * ( _weights[idx_quad] * kineticFlux ); } - // Solution flux - flux += _momentBasis[idx_quad] * ( _weights[idx_quad] * kineticFlux ); - } - return flux; -} -void CSDMNSolver::FluxUpdate() { -// Loop over the grid cells -#pragma omp parallel for - for( unsigned idx_cell = 0; idx_cell < _nCells; idx_cell++ ) { - // Dirichlet Boundaries stayd - if( _boundaryCells[idx_cell] == BOUNDARY_TYPE::DIRICHLET ) continue; - _solNew[idx_cell] = ConstructFlux( idx_cell ); + _solNew[idx_cell] = flux; // ConstructFlux( idx_cell ); } } diff --git a/src/solvers/mnsolver.cpp b/src/solvers/mnsolver.cpp index 0e89b007..9c502185 100644 --- a/src/solvers/mnsolver.cpp +++ b/src/solvers/mnsolver.cpp @@ -98,11 +98,6 @@ void MNSolver::ComputeMoments() { } } -Vector MNSolver::ConstructFlux( unsigned idx_cell ) { - //--- Integration of moments of flux --- - // Delete here and in csdmn solver -} - void MNSolver::ComputeRealizableSolution( unsigned idx_cell ) { // double entropyReconstruction = 0.0; diff --git a/src/solvers/mnsolver_normalized.cpp b/src/solvers/mnsolver_normalized.cpp index 85b7d92b..2743c415 100644 --- a/src/solvers/mnsolver_normalized.cpp +++ b/src/solvers/mnsolver_normalized.cpp @@ -40,8 +40,8 @@ void MNSolverNormalized::IterPreprocessing( unsigned /*idx_pseudotime*/ ) { _kineticDensity[idx_cell][idx_quad] = _u0[idx_cell] * _entropy->EntropyPrimeDual( blaze::dot( _alpha[idx_cell], _momentBasis[idx_quad] ) ); } - _sol[idx_cell] *= _u0[idx_cell]; if( _settings->GetRealizabilityReconstruction() ) ComputeRealizableSolution( idx_cell ); + _sol[idx_cell] *= _u0[idx_cell]; } // ------ Compute slope limiters and cell gradients --- diff --git a/src/solvers/solverbase.cpp b/src/solvers/solverbase.cpp index 8be02ecc..43d94c28 100644 --- a/src/solvers/solverbase.cpp +++ b/src/solvers/solverbase.cpp @@ -15,8 +15,6 @@ #include "solvers/snsolver.hpp" #include "toolboxes/textprocessingtoolbox.hpp" -#include -#include #include SolverBase::SolverBase( Config* settings ) { @@ -106,6 +104,7 @@ SolverBase* SolverBase::Create( Config* settings ) { case CSD_SN_SOLVER: return new CSDSNSolver( settings ); case CSD_PN_SOLVER: return new CSDPNSolver( settings ); case CSD_MN_SOLVER: return new CSDMNSolver( settings ); + default: ErrorMessages::Error( "Creator for the chosen solver does not yet exist. This is is the fault of the coder!", CURRENT_FUNCTION ); } ErrorMessages::Error( "Creator for the chosen solver does not yet exist. This is is the fault of the coder!", CURRENT_FUNCTION ); @@ -163,6 +162,7 @@ void SolverBase::Solve() { } // --- Postprocessing --- + DrawPostSolverOutput(); } diff --git a/src/velicitybasis/sphericalbase.cpp b/src/velocitybasis/sphericalbase.cpp similarity index 100% rename from src/velicitybasis/sphericalbase.cpp rename to src/velocitybasis/sphericalbase.cpp diff --git a/src/velicitybasis/sphericalharmonics.cpp b/src/velocitybasis/sphericalharmonics.cpp similarity index 100% rename from src/velicitybasis/sphericalharmonics.cpp rename to src/velocitybasis/sphericalharmonics.cpp diff --git a/src/velicitybasis/sphericalmonomials.cpp b/src/velocitybasis/sphericalmonomials.cpp similarity index 100% rename from src/velicitybasis/sphericalmonomials.cpp rename to src/velocitybasis/sphericalmonomials.cpp diff --git a/tfmodels/Monomial_Mk11_M1_2D/saved_model.pb b/tfmodels/Monomial_Mk11_M1_2D/best_model/saved_model.pb similarity index 100% rename from tfmodels/Monomial_Mk11_M1_2D/saved_model.pb rename to tfmodels/Monomial_Mk11_M1_2D/best_model/saved_model.pb diff --git a/tfmodels/Monomial_Mk11_M1_2D/variables/variables.data-00000-of-00001 b/tfmodels/Monomial_Mk11_M1_2D/best_model/variables/variables.data-00000-of-00001 similarity index 100% rename from tfmodels/Monomial_Mk11_M1_2D/variables/variables.data-00000-of-00001 rename to tfmodels/Monomial_Mk11_M1_2D/best_model/variables/variables.data-00000-of-00001 diff --git a/tfmodels/Monomial_Mk11_M1_2D/variables/variables.index b/tfmodels/Monomial_Mk11_M1_2D/best_model/variables/variables.index similarity index 100% rename from tfmodels/Monomial_Mk11_M1_2D/variables/variables.index rename to tfmodels/Monomial_Mk11_M1_2D/best_model/variables/variables.index diff --git a/tfmodels/Monomial_Mk11_M2_2D/best_model/keras_metadata.pb b/tfmodels/Monomial_Mk11_M2_2D/best_model/keras_metadata.pb new file mode 100644 index 00000000..e137a9ad --- /dev/null +++ b/tfmodels/Monomial_Mk11_M2_2D/best_model/keras_metadata.pb @@ -0,0 +1,33 @@ + 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+--training=1 \ +--verbosity=1 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M2_2D/information/runScript_002_.sh b/tfmodels/Monomial_Mk11_M2_2D/information/runScript_002_.sh new file mode 100644 index 00000000..d0de6792 --- /dev/null +++ b/tfmodels/Monomial_Mk11_M2_2D/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=2 \ +--epoch=2000 \ +--folder=mk11_m2_2d_g0 \ +--loadModel=1 \ +--model=11 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M2_2D/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk11_M2_2D/information/scaling_data/min_max_scaler.csv new file mode 100644 index 00000000..4b855abf --- /dev/null +++ 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+--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M2_2D_gamma1/information/runScript_002_.sh b/tfmodels/Monomial_Mk11_M2_2D_gamma1/information/runScript_002_.sh new file mode 100644 index 00000000..a109ecf8 --- /dev/null +++ b/tfmodels/Monomial_Mk11_M2_2D_gamma1/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=2 \ +--epoch=2000 \ +--folder=mk11_m2_2d_g1_v2 \ +--loadModel=1 \ +--model=11 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M2_2D_gamma1/information/scaling_data/min_max_scaler.csv 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+--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M2_2D_gamma2/information/runScript_002_.sh b/tfmodels/Monomial_Mk11_M2_2D_gamma2/information/runScript_002_.sh new file mode 100644 index 00000000..33c05e19 --- /dev/null +++ b/tfmodels/Monomial_Mk11_M2_2D_gamma2/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=2 \ +--epoch=2000 \ +--folder=mk11_m2_2d_g2 \ +--loadModel=1 \ +--model=11 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M2_2D_gamma2/information/scaling_data/min_max_scaler.csv 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+--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M2_2D_gamma3/information/runScript_002_.sh b/tfmodels/Monomial_Mk11_M2_2D_gamma3/information/runScript_002_.sh new file mode 100644 index 00000000..fbbade86 --- /dev/null +++ b/tfmodels/Monomial_Mk11_M2_2D_gamma3/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=2 \ +--epoch=2000 \ +--folder=mk11_m2_2d_g3 \ +--loadModel=1 \ +--model=11 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M2_2D_gamma3/information/scaling_data/min_max_scaler.csv 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+--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M3_2D/information/runScript_002_.sh b/tfmodels/Monomial_Mk11_M3_2D/information/runScript_002_.sh new file mode 100644 index 00000000..e6098da9 --- /dev/null +++ b/tfmodels/Monomial_Mk11_M3_2D/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=3 \ +--epoch=2000 \ +--folder=mk11_M3_2D_g0 \ +--loadModel=1 \ +--model=11 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M3_2D/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk11_M3_2D/information/scaling_data/min_max_scaler.csv new file mode 100644 index 00000000..4b855abf --- /dev/null +++ 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+--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M3_2D_gamma1/information/runScript_002_.sh b/tfmodels/Monomial_Mk11_M3_2D_gamma1/information/runScript_002_.sh new file mode 100644 index 00000000..1185ae13 --- /dev/null +++ b/tfmodels/Monomial_Mk11_M3_2D_gamma1/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=3 \ +--epoch=2000 \ +--folder=mk11_M3_2D_g1 \ +--loadModel=1 \ +--model=11 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M3_2D_gamma1/information/scaling_data/min_max_scaler.csv 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+--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M3_2D_gamma2/information/runScript_002_.sh b/tfmodels/Monomial_Mk11_M3_2D_gamma2/information/runScript_002_.sh new file mode 100644 index 00000000..4034c5e2 --- /dev/null +++ b/tfmodels/Monomial_Mk11_M3_2D_gamma2/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=3 \ +--epoch=2000 \ +--folder=mk11_M3_2D_g2 \ +--loadModel=1 \ +--model=11 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M3_2D_gamma2/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk11_M3_2D_gamma2/information/scaling_data/min_max_scaler.csv new file mode 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+--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M3_2D_gamma3/information/runScript_002_.sh b/tfmodels/Monomial_Mk11_M3_2D_gamma3/information/runScript_002_.sh new file mode 100644 index 00000000..71d48335 --- /dev/null +++ b/tfmodels/Monomial_Mk11_M3_2D_gamma3/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=3 \ +--epoch=2000 \ +--folder=mk11_M3_2D_g3 \ +--loadModel=1 \ +--model=11 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M3_2D_gamma3/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk11_M3_2D_gamma3/information/scaling_data/min_max_scaler.csv new file mode 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+--training=1 \ +--verbosity=2 \ +--networkwidth=600 \ +--networkdepth=6 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M4_2D/information/runScript_002_.sh b/tfmodels/Monomial_Mk11_M4_2D/information/runScript_002_.sh new file mode 100644 index 00000000..efd7257e --- /dev/null +++ b/tfmodels/Monomial_Mk11_M4_2D/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=4 \ +--epoch=20 \ +--folder=mk11_m4_2d_g0 \ +--loadModel=1 \ +--model=11 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=1 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=1 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M4_2D/information/runScript_003_.sh b/tfmodels/Monomial_Mk11_M4_2D/information/runScript_003_.sh new file mode 100644 index 00000000..50a8e796 --- /dev/null +++ b/tfmodels/Monomial_Mk11_M4_2D/information/runScript_003_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=4 \ +--epoch=2000 \ +--folder=mk11_m4_2d_g0 \ +--loadModel=1 \ +--model=11 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M4_2D/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk11_M4_2D/information/scaling_data/min_max_scaler.csv new file mode 100644 index 00000000..4b855abf --- /dev/null +++ b/tfmodels/Monomial_Mk11_M4_2D/information/scaling_data/min_max_scaler.csv @@ -0,0 +1 @@ +0.0,1.0 diff --git a/tfmodels/Monomial_Mk11_M4_2D_gamma1/best_model/keras_metadata.pb b/tfmodels/Monomial_Mk11_M4_2D_gamma1/best_model/keras_metadata.pb new file mode 100644 index 00000000..3af2cf03 --- /dev/null +++ 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+--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M4_2D_gamma1/information/runScript_002_.sh b/tfmodels/Monomial_Mk11_M4_2D_gamma1/information/runScript_002_.sh new file mode 100644 index 00000000..6c69eaa3 --- /dev/null +++ b/tfmodels/Monomial_Mk11_M4_2D_gamma1/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=4 \ +--epoch=2000 \ +--folder=mk11_m4_2d_g1 \ +--loadModel=1 \ +--model=11 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M4_2D_gamma1/information/scaling_data/min_max_scaler.csv 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+--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M4_2D_gamma2/information/runScript_002_.sh b/tfmodels/Monomial_Mk11_M4_2D_gamma2/information/runScript_002_.sh new file mode 100644 index 00000000..22a6b4c5 --- /dev/null +++ b/tfmodels/Monomial_Mk11_M4_2D_gamma2/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=4 \ +--epoch=2000 \ +--folder=mk11_m4_2d_g2 \ +--loadModel=1 \ +--model=11 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M4_2D_gamma2/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk11_M4_2D_gamma2/information/scaling_data/min_max_scaler.csv new file mode 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+--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M4_2D_gamma3/information/runScript_002_.sh b/tfmodels/Monomial_Mk11_M4_2D_gamma3/information/runScript_002_.sh new file mode 100644 index 00000000..0443cf4b --- /dev/null +++ b/tfmodels/Monomial_Mk11_M4_2D_gamma3/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=4 \ +--epoch=2000 \ +--folder=mk11_m4_2d_g3_v2 \ +--loadModel=1 \ +--model=11 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk11_M4_2D_gamma3/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk11_M4_2D_gamma3/information/scaling_data/min_max_scaler.csv new file mode 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"gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["add", 0, 0, {}]]], "shared_object_id": 32, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 82}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +root.core_model.layer-12"_tf_keras_layer*{"name": "tf.nn.relu_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_2", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_2", 0, 0, {"name": null}]], "shared_object_id": 33}2 + $root.core_model.layer_with_weights-8"_tf_keras_layer* {"name": "block_1_layer_0", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_1_layer_0", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_2", 0, 0, {}]]], "shared_object_id": 34, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 83}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + $root.core_model.layer_with_weights-9"_tf_keras_layer* {"name": "batch_normalization_3", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_3", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 35}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 36}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 37}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 38}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["block_1_layer_0", 0, 0, {}]]], "shared_object_id": 39, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 84}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +root.core_model.layer-15"_tf_keras_layer*{"name": "tf.nn.relu_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_3", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_3", 0, 0, {"name": null}]], "shared_object_id": 40}2 + %root.core_model.layer_with_weights-10"_tf_keras_layer* {"name": "block_1_layer_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_1_layer_1", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_3", 0, 0, {}]]], "shared_object_id": 41, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 85}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +root.core_model.layer-17"_tf_keras_layer*{"name": "add_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_1", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["add", 0, 0, {}], ["block_1_layer_1", 0, 0, {}]]], "shared_object_id": 42, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 300]}, {"class_name": "TensorShape", "items": [null, 300]}]}2 + %root.core_model.layer_with_weights-11"_tf_keras_layer* {"name": "batch_normalization_4", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_4", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 43}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 44}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 45}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 46}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["add_1", 0, 0, {}]]], "shared_object_id": 47, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 86}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +root.core_model.layer-19"_tf_keras_layer*{"name": "tf.nn.relu_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_4", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_4", 0, 0, {"name": null}]], "shared_object_id": 48}2 + %root.core_model.layer_with_weights-12"_tf_keras_layer* {"name": "block_2_layer_0", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_2_layer_0", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_4", 0, 0, {}]]], "shared_object_id": 49, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 87}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + %root.core_model.layer_with_weights-13"_tf_keras_layer* {"name": "batch_normalization_5", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_5", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 50}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 51}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 52}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 53}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["block_2_layer_0", 0, 0, {}]]], "shared_object_id": 54, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 88}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +root.core_model.layer-22"_tf_keras_layer*{"name": "tf.nn.relu_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_5", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_5", 0, 0, {"name": null}]], "shared_object_id": 55}2 +  %root.core_model.layer_with_weights-14"_tf_keras_layer* {"name": "block_2_layer_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_2_layer_1", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_5", 0, 0, {}]]], "shared_object_id": 56, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 89}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +!root.core_model.layer-24"_tf_keras_layer*{"name": "add_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_2", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["add_1", 0, 0, {}], ["block_2_layer_1", 0, 0, {}]]], "shared_object_id": 57, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 300]}, {"class_name": "TensorShape", "items": [null, 300]}]}2 + "%root.core_model.layer_with_weights-15"_tf_keras_layer* {"name": "batch_normalization_6", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_6", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 58}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 59}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 60}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 61}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["add_2", 0, 0, {}]]], "shared_object_id": 62, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 90}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +#root.core_model.layer-26"_tf_keras_layer*{"name": "tf.nn.relu_6", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_6", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_6", 0, 0, {"name": null}]], "shared_object_id": 63}2 + $%root.core_model.layer_with_weights-16"_tf_keras_layer* {"name": "block_3_layer_0", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_3_layer_0", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_6", 0, 0, {}]]], "shared_object_id": 64, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 91}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + %%root.core_model.layer_with_weights-17"_tf_keras_layer* {"name": "batch_normalization_7", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_7", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 65}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 66}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 67}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 68}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["block_3_layer_0", 0, 0, {}]]], "shared_object_id": 69, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 92}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +&root.core_model.layer-29"_tf_keras_layer*{"name": "tf.nn.relu_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_7", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_7", 0, 0, {"name": null}]], "shared_object_id": 70}2 + '%root.core_model.layer_with_weights-18"_tf_keras_layer* {"name": "block_3_layer_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_3_layer_1", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_7", 0, 0, {}]]], "shared_object_id": 71, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 93}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +(root.core_model.layer-31"_tf_keras_layer*{"name": "add_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_3", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["add_2", 0, 0, 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"shared_object_id": 29}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 30}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 31}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["add", 0, 0, {}]]], "shared_object_id": 32, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 82}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +root.core_model.layer-12"_tf_keras_layer*{"name": "tf.nn.relu_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_2", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_2", 0, 0, {"name": null}]], "shared_object_id": 33}2 + $root.core_model.layer_with_weights-8"_tf_keras_layer* {"name": "block_1_layer_0", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_1_layer_0", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_2", 0, 0, {}]]], "shared_object_id": 34, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 83}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + $root.core_model.layer_with_weights-9"_tf_keras_layer* {"name": "batch_normalization_3", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_3", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 35}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 36}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 37}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 38}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["block_1_layer_0", 0, 0, {}]]], "shared_object_id": 39, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 84}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +root.core_model.layer-15"_tf_keras_layer*{"name": "tf.nn.relu_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_3", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_3", 0, 0, {"name": null}]], "shared_object_id": 40}2 + %root.core_model.layer_with_weights-10"_tf_keras_layer* {"name": "block_1_layer_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_1_layer_1", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_3", 0, 0, {}]]], "shared_object_id": 41, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 85}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +root.core_model.layer-17"_tf_keras_layer*{"name": "add_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_1", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["add", 0, 0, {}], ["block_1_layer_1", 0, 0, {}]]], "shared_object_id": 42, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 300]}, {"class_name": "TensorShape", "items": [null, 300]}]}2 + %root.core_model.layer_with_weights-11"_tf_keras_layer* {"name": "batch_normalization_4", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_4", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 43}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 44}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 45}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 46}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["add_1", 0, 0, {}]]], "shared_object_id": 47, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 86}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +root.core_model.layer-19"_tf_keras_layer*{"name": "tf.nn.relu_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_4", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_4", 0, 0, {"name": null}]], "shared_object_id": 48}2 + %root.core_model.layer_with_weights-12"_tf_keras_layer* {"name": "block_2_layer_0", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_2_layer_0", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_4", 0, 0, {}]]], "shared_object_id": 49, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 87}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + %root.core_model.layer_with_weights-13"_tf_keras_layer* {"name": "batch_normalization_5", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_5", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 50}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 51}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 52}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 53}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["block_2_layer_0", 0, 0, {}]]], "shared_object_id": 54, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 88}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + root.core_model.layer-22"_tf_keras_layer*{"name": "tf.nn.relu_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_5", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_5", 0, 0, {"name": null}]], "shared_object_id": 55}2 + !%root.core_model.layer_with_weights-14"_tf_keras_layer* {"name": "block_2_layer_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_2_layer_1", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_5", 0, 0, {}]]], "shared_object_id": 56, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 89}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +"root.core_model.layer-24"_tf_keras_layer*{"name": "add_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_2", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["add_1", 0, 0, {}], ["block_2_layer_1", 0, 0, {}]]], "shared_object_id": 57, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 300]}, {"class_name": "TensorShape", "items": [null, 300]}]}2 + #%root.core_model.layer_with_weights-15"_tf_keras_layer* {"name": "batch_normalization_6", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_6", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 58}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 59}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 60}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 61}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["add_2", 0, 0, {}]]], "shared_object_id": 62, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 90}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +$root.core_model.layer-26"_tf_keras_layer*{"name": "tf.nn.relu_6", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_6", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_6", 0, 0, {"name": null}]], "shared_object_id": 63}2 + %%root.core_model.layer_with_weights-16"_tf_keras_layer* {"name": "block_3_layer_0", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_3_layer_0", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_6", 0, 0, {}]]], "shared_object_id": 64, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 91}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + &%root.core_model.layer_with_weights-17"_tf_keras_layer* {"name": "batch_normalization_7", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_7", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 65}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 66}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 67}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 68}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["block_3_layer_0", 0, 0, {}]]], "shared_object_id": 69, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 92}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +'root.core_model.layer-29"_tf_keras_layer*{"name": "tf.nn.relu_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_7", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_7", 0, 0, {"name": null}]], "shared_object_id": 70}2 + (%root.core_model.layer_with_weights-18"_tf_keras_layer* {"name": "block_3_layer_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_3_layer_1", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_7", 0, 0, {}]]], "shared_object_id": 71, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 93}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +)root.core_model.layer-31"_tf_keras_layer*{"name": "add_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": 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"build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + %root.core_model.layer_with_weights-13"_tf_keras_layer* {"name": "batch_normalization_5", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_5", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 50}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 51}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 52}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 53}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["block_2_layer_0", 0, 0, {}]]], "shared_object_id": 54, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 88}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + root.core_model.layer-22"_tf_keras_layer*{"name": "tf.nn.relu_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_5", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_5", 0, 0, {"name": null}]], "shared_object_id": 55}2 + !%root.core_model.layer_with_weights-14"_tf_keras_layer* {"name": "block_2_layer_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": 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{"class_name": "Zeros", "config": {}, "shared_object_id": 58}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 59}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 60}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 61}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["add_2", 0, 0, {}]]], "shared_object_id": 62, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 90}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +$root.core_model.layer-26"_tf_keras_layer*{"name": "tf.nn.relu_6", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_6", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_6", 0, 0, {"name": null}]], "shared_object_id": 63}2 + %%root.core_model.layer_with_weights-16"_tf_keras_layer* {"name": "block_3_layer_0", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_3_layer_0", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_6", 0, 0, {}]]], "shared_object_id": 64, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 91}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + &%root.core_model.layer_with_weights-17"_tf_keras_layer* {"name": "batch_normalization_7", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_7", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 65}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 66}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 67}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 68}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["block_3_layer_0", 0, 0, {}]]], "shared_object_id": 69, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 92}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +'root.core_model.layer-29"_tf_keras_layer*{"name": "tf.nn.relu_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_7", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_7", 0, 0, {"name": null}]], "shared_object_id": 70}2 + (%root.core_model.layer_with_weights-18"_tf_keras_layer* {"name": "block_3_layer_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_3_layer_1", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": 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b/tfmodels/Monomial_Mk12_M1_2D_gamma1/information/best_model/variables/variables.data-00000-of-00001 differ diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma1/information/best_model/variables/variables.index b/tfmodels/Monomial_Mk12_M1_2D_gamma1/information/best_model/variables/variables.index new file mode 100644 index 00000000..3c13782a Binary files /dev/null and b/tfmodels/Monomial_Mk12_M1_2D_gamma1/information/best_model/variables/variables.index differ diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma1/information/config_001_.csv b/tfmodels/Monomial_Mk12_M1_2D_gamma1/information/config_001_.csv new file mode 100644 index 00000000..abd5d6b8 --- /dev/null +++ b/tfmodels/Monomial_Mk12_M1_2D_gamma1/information/config_001_.csv @@ -0,0 +1,20 @@ +date;2022-03-23 +git_version;6c85097d0506cf64edad439190f68fc3f8616467 +sampling;2 +batch;1000 +curriculum;1 +degree;1 +epoch;3000 +folder;mk12_M1_2D_gamma1 +loadModel;1 +model;12 +normalized moments;True +decorrelate inputs;True +scaled outputs;True 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/dev/null +++ b/tfmodels/Monomial_Mk12_M1_2D_gamma1/information/runScript_001_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=1000 \ +--curriculum=1 \ +--degree=1 \ +--epoch=3000 \ +--folder=mk12_M1_2D_gamma1 \ +--loadModel=1 \ +--model=12 \ +--normalized=1 \ +--scaledOutput=1 \ +--decorrInput=1 \ +--objective=1 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=300 \ +--networkdepth=4 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma1/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk12_M1_2D_gamma1/information/scaling_data/min_max_scaler.csv new file mode 100644 index 00000000..500fb89e --- /dev/null +++ b/tfmodels/Monomial_Mk12_M1_2D_gamma1/information/scaling_data/min_max_scaler.csv @@ -0,0 +1 @@ +-2.144639039141,4.674838860309 diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma2/best_model/keras_metadata.pb 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null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["add_1", 0, 0, {}]]], "shared_object_id": 47, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 86}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +root.core_model.layer-19"_tf_keras_layer*{"name": "tf.nn.relu_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_4", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_4", 0, 0, {"name": null}]], "shared_object_id": 48}2 + %root.core_model.layer_with_weights-12"_tf_keras_layer* {"name": "block_2_layer_0", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_2_layer_0", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_4", 0, 0, {}]]], "shared_object_id": 49, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 87}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + %root.core_model.layer_with_weights-13"_tf_keras_layer* {"name": "batch_normalization_5", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_5", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 50}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 51}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 52}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 53}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["block_2_layer_0", 0, 0, {}]]], "shared_object_id": 54, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 88}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + root.core_model.layer-22"_tf_keras_layer*{"name": "tf.nn.relu_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_5", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_5", 0, 0, {"name": null}]], "shared_object_id": 55}2 + !%root.core_model.layer_with_weights-14"_tf_keras_layer* {"name": "block_2_layer_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_2_layer_1", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_5", 0, 0, {}]]], "shared_object_id": 56, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 89}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +"root.core_model.layer-24"_tf_keras_layer*{"name": "add_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_2", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["add_1", 0, 0, {}], ["block_2_layer_1", 0, 0, {}]]], "shared_object_id": 57, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 300]}, {"class_name": "TensorShape", "items": [null, 300]}]}2 + #%root.core_model.layer_with_weights-15"_tf_keras_layer* {"name": "batch_normalization_6", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_6", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 58}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 59}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 60}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 61}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["add_2", 0, 0, {}]]], "shared_object_id": 62, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 90}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +$root.core_model.layer-26"_tf_keras_layer*{"name": "tf.nn.relu_6", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_6", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_6", 0, 0, {"name": null}]], "shared_object_id": 63}2 + %%root.core_model.layer_with_weights-16"_tf_keras_layer* {"name": "block_3_layer_0", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_3_layer_0", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_6", 0, 0, {}]]], "shared_object_id": 64, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 91}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + &%root.core_model.layer_with_weights-17"_tf_keras_layer* {"name": "batch_normalization_7", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_7", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 65}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 66}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 67}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 68}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["block_3_layer_0", 0, 0, {}]]], "shared_object_id": 69, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 92}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +'root.core_model.layer-29"_tf_keras_layer*{"name": "tf.nn.relu_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_7", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_7", 0, 0, {"name": null}]], "shared_object_id": 70}2 + (%root.core_model.layer_with_weights-18"_tf_keras_layer* {"name": "block_3_layer_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_3_layer_1", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_7", 0, 0, {}]]], "shared_object_id": 71, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 93}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +)root.core_model.layer-31"_tf_keras_layer*{"name": "add_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_3", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["add_2", 0, 0, {}], ["block_3_layer_1", 0, 0, {}]]], "shared_object_id": 72, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 300]}, {"class_name": "TensorShape", "items": [null, 300]}]}2 +*%root.core_model.layer_with_weights-19"_tf_keras_layer*{"name": "dense_output", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_output", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 73}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["add_3", 0, 0, {}]]], "shared_object_id": 74, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 94}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +root.keras_api.metrics.0"_tf_keras_metric*{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 95}2 +root.keras_api.metrics.1"_tf_keras_metric*{"class_name": "Mean", "name": "output_1_loss", "dtype": "float32", "config": {"name": "output_1_loss", "dtype": "float32"}, "shared_object_id": 96}2 +root.keras_api.metrics.2"_tf_keras_metric*{"class_name": "Mean", "name": "output_2_loss", "dtype": "float32", "config": {"name": "output_2_loss", "dtype": "float32"}, "shared_object_id": 97}2 +root.keras_api.metrics.3"_tf_keras_metric*{"class_name": "Mean", "name": "output_3_loss", "dtype": "float32", "config": {"name": "output_3_loss", "dtype": "float32"}, "shared_object_id": 98}2 +root.keras_api.metrics.4"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "output_1_mean_absolute_error", "dtype": "float32", "config": {"name": "output_1_mean_absolute_error", "dtype": "float32", "fn": "mean_absolute_error"}, "shared_object_id": 3}2 +root.keras_api.metrics.5"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "output_1_mean_squared_error", "dtype": "float32", "config": {"name": "output_1_mean_squared_error", "dtype": "float32", "fn": "mean_squared_error"}, "shared_object_id": 4}2 +root.keras_api.metrics.6"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "output_2_mean_absolute_error", "dtype": "float32", "config": {"name": "output_2_mean_absolute_error", "dtype": "float32", "fn": "mean_absolute_error"}, "shared_object_id": 5}2 +root.keras_api.metrics.7"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "output_2_mean_squared_error", "dtype": "float32", "config": {"name": "output_2_mean_squared_error", "dtype": "float32", "fn": "mean_squared_error"}, "shared_object_id": 6}2 +root.keras_api.metrics.8"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "output_3_mean_absolute_error", "dtype": "float32", "config": {"name": "output_3_mean_absolute_error", "dtype": "float32", "fn": "mean_absolute_error"}, "shared_object_id": 7}2 +root.keras_api.metrics.9"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "output_3_mean_squared_error", "dtype": "float32", "config": {"name": "output_3_mean_squared_error", "dtype": "float32", "fn": "mean_squared_error"}, "shared_object_id": 8}2 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma2/best_model/saved_model.pb b/tfmodels/Monomial_Mk12_M1_2D_gamma2/best_model/saved_model.pb new file mode 100644 index 00000000..ca15b896 Binary files /dev/null and b/tfmodels/Monomial_Mk12_M1_2D_gamma2/best_model/saved_model.pb differ diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma2/best_model/variables/variables.data-00000-of-00001 b/tfmodels/Monomial_Mk12_M1_2D_gamma2/best_model/variables/variables.data-00000-of-00001 new file mode 100644 index 00000000..e14d17fe Binary files /dev/null and b/tfmodels/Monomial_Mk12_M1_2D_gamma2/best_model/variables/variables.data-00000-of-00001 differ diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma2/best_model/variables/variables.index b/tfmodels/Monomial_Mk12_M1_2D_gamma2/best_model/variables/variables.index new file mode 100644 index 00000000..8630cfe7 Binary files /dev/null and b/tfmodels/Monomial_Mk12_M1_2D_gamma2/best_model/variables/variables.index differ diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma2/information/config_001_.csv b/tfmodels/Monomial_Mk12_M1_2D_gamma2/information/config_001_.csv new file mode 100644 index 00000000..14886b49 --- /dev/null +++ b/tfmodels/Monomial_Mk12_M1_2D_gamma2/information/config_001_.csv @@ -0,0 +1,20 @@ +date;2022-03-23 +git_version;6c85097d0506cf64edad439190f68fc3f8616467 +sampling;2 +batch;1000 +curriculum;1 +degree;1 +epoch;3000 +folder;mk12_M1_2D_gamma2 +loadModel;1 +model;12 +normalized moments;True +decorrelate inputs;True +scaled outputs;True +objective;1 +processingmode;1 +spatial Dimension;2 +verbosity;2 +training;1 +network width;300 +network depth;4 diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma2/information/history_001_.csv b/tfmodels/Monomial_Mk12_M1_2D_gamma2/information/history_001_.csv new file mode 100644 index 00000000..3a756c49 --- /dev/null +++ b/tfmodels/Monomial_Mk12_M1_2D_gamma2/information/history_001_.csv @@ -0,0 +1,3001 @@ +epoch,loss,output_1_loss,output_1_mean_absolute_error,output_1_mean_squared_error,output_2_loss,output_2_mean_absolute_error,output_2_mean_squared_error,output_3_loss,output_3_mean_absolute_error,output_3_mean_squared_error,val_loss,val_output_1_loss,val_output_1_mean_absolute_error,val_output_1_mean_squared_error,val_output_2_loss,val_output_2_mean_absolute_error,val_output_2_mean_squared_error,val_output_3_loss,val_output_3_mean_absolute_error,val_output_3_mean_squared_error 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/dev/null +++ b/tfmodels/Monomial_Mk12_M1_2D_gamma2/information/runScript_001_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=1000 \ +--curriculum=1 \ +--degree=1 \ +--epoch=3000 \ +--folder=mk12_M1_2D_gamma2 \ +--loadModel=1 \ +--model=12 \ +--normalized=1 \ +--scaledOutput=1 \ +--decorrInput=1 \ +--objective=1 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=300 \ +--networkdepth=4 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma2/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk12_M1_2D_gamma2/information/scaling_data/min_max_scaler.csv new file mode 100644 index 00000000..500fb89e --- /dev/null +++ b/tfmodels/Monomial_Mk12_M1_2D_gamma2/information/scaling_data/min_max_scaler.csv @@ -0,0 +1 @@ +-2.144639039141,4.674838860309 diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma3/best_model/keras_metadata.pb 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["block_1_layer_1", 0, 0, {}]]], "shared_object_id": 42, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 300]}, {"class_name": "TensorShape", "items": [null, 300]}]}2 + %root.core_model.layer_with_weights-11"_tf_keras_layer* {"name": "batch_normalization_4", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_4", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 43}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 44}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 45}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 46}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["add_1", 0, 0, {}]]], "shared_object_id": 47, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 86}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +root.core_model.layer-19"_tf_keras_layer*{"name": "tf.nn.relu_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_4", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_4", 0, 0, {"name": null}]], "shared_object_id": 48}2 + %root.core_model.layer_with_weights-12"_tf_keras_layer* {"name": "block_2_layer_0", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_2_layer_0", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_4", 0, 0, {}]]], "shared_object_id": 49, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 87}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + %root.core_model.layer_with_weights-13"_tf_keras_layer* {"name": "batch_normalization_5", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_5", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 50}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 51}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 52}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 53}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["block_2_layer_0", 0, 0, {}]]], "shared_object_id": 54, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 88}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +root.core_model.layer-22"_tf_keras_layer*{"name": "tf.nn.relu_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_5", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_5", 0, 0, {"name": null}]], "shared_object_id": 55}2 +  %root.core_model.layer_with_weights-14"_tf_keras_layer* {"name": "block_2_layer_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_2_layer_1", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_5", 0, 0, {}]]], "shared_object_id": 56, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 89}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +!root.core_model.layer-24"_tf_keras_layer*{"name": "add_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_2", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["add_1", 0, 0, {}], ["block_2_layer_1", 0, 0, {}]]], "shared_object_id": 57, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 300]}, {"class_name": "TensorShape", "items": [null, 300]}]}2 + "%root.core_model.layer_with_weights-15"_tf_keras_layer* {"name": "batch_normalization_6", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_6", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 58}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 59}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 60}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 61}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["add_2", 0, 0, {}]]], "shared_object_id": 62, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 90}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +#root.core_model.layer-26"_tf_keras_layer*{"name": "tf.nn.relu_6", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_6", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_6", 0, 0, {"name": null}]], "shared_object_id": 63}2 + $%root.core_model.layer_with_weights-16"_tf_keras_layer* {"name": "block_3_layer_0", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_3_layer_0", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_6", 0, 0, {}]]], "shared_object_id": 64, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 91}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 + %%root.core_model.layer_with_weights-17"_tf_keras_layer* {"name": "batch_normalization_7", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_7", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 65}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 66}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 67}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 68}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["block_3_layer_0", 0, 0, {}]]], "shared_object_id": 69, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 300}}, "shared_object_id": 92}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +&root.core_model.layer-29"_tf_keras_layer*{"name": "tf.nn.relu_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TFOpLambda", "config": {"name": "tf.nn.relu_7", "trainable": true, "dtype": "float32", "function": "nn.relu"}, "inbound_nodes": [["batch_normalization_7", 0, 0, {"name": null}]], "shared_object_id": 70}2 + '%root.core_model.layer_with_weights-18"_tf_keras_layer* {"name": "block_3_layer_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "block_3_layer_1", "trainable": true, "dtype": "float32", "units": 300, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["tf.nn.relu_7", 0, 0, {}]]], "shared_object_id": 71, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 93}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +(root.core_model.layer-31"_tf_keras_layer*{"name": "add_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_3", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["add_2", 0, 0, {}], ["block_3_layer_1", 0, 0, {}]]], "shared_object_id": 72, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 300]}, {"class_name": "TensorShape", "items": [null, 300]}]}2 +)%root.core_model.layer_with_weights-19"_tf_keras_layer*{"name": "dense_output", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_output", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "LecunNormal", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 73}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}, "shared_object_id": 11}, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["add_3", 0, 0, {}]]], "shared_object_id": 74, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 300}}, "shared_object_id": 94}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 300]}}2 +root.keras_api.metrics.0"_tf_keras_metric*{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 95}2 +root.keras_api.metrics.1"_tf_keras_metric*{"class_name": "Mean", "name": "output_1_loss", "dtype": "float32", "config": {"name": "output_1_loss", "dtype": "float32"}, "shared_object_id": 96}2 +root.keras_api.metrics.2"_tf_keras_metric*{"class_name": "Mean", "name": "output_2_loss", "dtype": "float32", "config": {"name": "output_2_loss", "dtype": "float32"}, "shared_object_id": 97}2 +root.keras_api.metrics.3"_tf_keras_metric*{"class_name": "Mean", "name": "output_3_loss", "dtype": "float32", "config": {"name": "output_3_loss", "dtype": "float32"}, "shared_object_id": 98}2 +root.keras_api.metrics.4"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "output_1_mean_absolute_error", "dtype": "float32", "config": {"name": "output_1_mean_absolute_error", "dtype": "float32", "fn": "mean_absolute_error"}, "shared_object_id": 3}2 +root.keras_api.metrics.5"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "output_1_mean_squared_error", "dtype": "float32", "config": {"name": "output_1_mean_squared_error", "dtype": "float32", "fn": "mean_squared_error"}, "shared_object_id": 4}2 +root.keras_api.metrics.6"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "output_2_mean_absolute_error", "dtype": "float32", "config": {"name": "output_2_mean_absolute_error", "dtype": "float32", "fn": "mean_absolute_error"}, "shared_object_id": 5}2 +root.keras_api.metrics.7"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "output_2_mean_squared_error", "dtype": "float32", "config": {"name": "output_2_mean_squared_error", "dtype": "float32", "fn": "mean_squared_error"}, "shared_object_id": 6}2 +root.keras_api.metrics.8"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "output_3_mean_absolute_error", "dtype": "float32", "config": {"name": "output_3_mean_absolute_error", "dtype": "float32", "fn": "mean_absolute_error"}, "shared_object_id": 7}2 +root.keras_api.metrics.9"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "output_3_mean_squared_error", "dtype": "float32", "config": {"name": "output_3_mean_squared_error", "dtype": "float32", "fn": "mean_squared_error"}, "shared_object_id": 8}2 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma3/best_model/saved_model.pb b/tfmodels/Monomial_Mk12_M1_2D_gamma3/best_model/saved_model.pb new file mode 100644 index 00000000..b8f411f3 Binary files /dev/null and b/tfmodels/Monomial_Mk12_M1_2D_gamma3/best_model/saved_model.pb differ diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma3/best_model/variables/variables.data-00000-of-00001 b/tfmodels/Monomial_Mk12_M1_2D_gamma3/best_model/variables/variables.data-00000-of-00001 new file mode 100644 index 00000000..982e0aca Binary files /dev/null and b/tfmodels/Monomial_Mk12_M1_2D_gamma3/best_model/variables/variables.data-00000-of-00001 differ diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma3/best_model/variables/variables.index b/tfmodels/Monomial_Mk12_M1_2D_gamma3/best_model/variables/variables.index new file mode 100644 index 00000000..d6cca41b Binary files /dev/null and b/tfmodels/Monomial_Mk12_M1_2D_gamma3/best_model/variables/variables.index differ diff --git a/tfmodels/Monomial_Mk12_M1_2D_gamma3/information/config_001_.csv b/tfmodels/Monomial_Mk12_M1_2D_gamma3/information/config_001_.csv new file mode 100644 index 00000000..4b568b75 --- /dev/null +++ b/tfmodels/Monomial_Mk12_M1_2D_gamma3/information/config_001_.csv @@ -0,0 +1,20 @@ +date;2022-03-23 +git_version;6c85097d0506cf64edad439190f68fc3f8616467 +sampling;2 +batch;1000 +curriculum;1 +degree;1 +epoch;3000 +folder;mk12_M1_2D_gamma3 +loadModel;0 +model;12 +normalized moments;True +decorrelate inputs;True +scaled outputs;True +objective;1 +processingmode;1 +spatial Dimension;2 +verbosity;2 +training;1 +network width;300 +network depth;4 diff --git 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file mode 100644 index 00000000..464527c4 --- /dev/null +++ b/tfmodels/Monomial_Mk12_M2_2D/information/runScript_001_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=2 \ +--epoch=2000 \ +--folder=mk12_m2_2d_g0 \ +--loadModel=1 \ +--model=12 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=0 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=300 \ +--networkdepth=6 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk12_M2_2D/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk12_M2_2D/information/scaling_data/min_max_scaler.csv new file mode 100644 index 00000000..4b855abf --- /dev/null +++ b/tfmodels/Monomial_Mk12_M2_2D/information/scaling_data/min_max_scaler.csv @@ -0,0 +1 @@ +0.0,1.0 diff --git a/tfmodels/Monomial_Mk12_M2_2D_gamma1/best_model/keras_metadata.pb b/tfmodels/Monomial_Mk12_M2_2D_gamma1/best_model/keras_metadata.pb new 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b/tfmodels/Monomial_Mk12_M2_2D_gamma1/information/runScript_001_.sh new file mode 100644 index 00000000..ccdc7e3e --- /dev/null +++ b/tfmodels/Monomial_Mk12_M2_2D_gamma1/information/runScript_001_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=2 \ +--epoch=2000 \ +--folder=mk12_m2_2d_g1 \ +--loadModel=1 \ +--model=12 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=0 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=300 \ +--networkdepth=6 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk12_M2_2D_gamma1/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk12_M2_2D_gamma1/information/scaling_data/min_max_scaler.csv new file mode 100644 index 00000000..4b855abf --- /dev/null +++ b/tfmodels/Monomial_Mk12_M2_2D_gamma1/information/scaling_data/min_max_scaler.csv @@ -0,0 +1 @@ +0.0,1.0 diff --git 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+--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=400 \ +--networkdepth=6 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk12_M3_2D_gamma2/information/runScript_002_.sh b/tfmodels/Monomial_Mk12_M3_2D_gamma2/information/runScript_002_.sh new file mode 100644 index 00000000..5eaa5677 --- /dev/null +++ b/tfmodels/Monomial_Mk12_M3_2D_gamma2/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=3 \ +--epoch=2000 \ +--folder=mk12_M3_2D_g2 \ +--loadModel=1 \ +--model=12 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=400 \ +--networkdepth=6 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk12_M3_2D_gamma2/information/runScript_003_.sh b/tfmodels/Monomial_Mk12_M3_2D_gamma2/information/runScript_003_.sh new file mode 100644 index 00000000..5eaa5677 --- /dev/null +++ b/tfmodels/Monomial_Mk12_M3_2D_gamma2/information/runScript_003_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=3 \ +--epoch=2000 \ +--folder=mk12_M3_2D_g2 \ +--loadModel=1 \ +--model=12 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=400 \ +--networkdepth=6 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk12_M3_2D_gamma2/information/runScript_004_.sh b/tfmodels/Monomial_Mk12_M3_2D_gamma2/information/runScript_004_.sh new file mode 100644 index 00000000..5eaa5677 --- /dev/null +++ b/tfmodels/Monomial_Mk12_M3_2D_gamma2/information/runScript_004_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=3 \ +--epoch=2000 \ +--folder=mk12_M3_2D_g2 \ +--loadModel=1 \ +--model=12 \ +--normalized=1 \ 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+--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=400 \ +--networkdepth=6 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk12_M3_2D_gamma3/information/runScript_002_.sh b/tfmodels/Monomial_Mk12_M3_2D_gamma3/information/runScript_002_.sh new file mode 100644 index 00000000..91471e2b --- /dev/null +++ b/tfmodels/Monomial_Mk12_M3_2D_gamma3/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=3 \ +--epoch=2000 \ +--folder=mk12_M3_2D_g3 \ +--loadModel=1 \ +--model=12 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=400 \ +--networkdepth=6 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk12_M3_2D_gamma3/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk12_M3_2D_gamma3/information/scaling_data/min_max_scaler.csv new file mode 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+--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=600 \ +--networkdepth=6 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk12_M4_2D_gamma2/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk12_M4_2D_gamma2/information/scaling_data/min_max_scaler.csv new file mode 100644 index 00000000..4b855abf --- /dev/null +++ b/tfmodels/Monomial_Mk12_M4_2D_gamma2/information/scaling_data/min_max_scaler.csv @@ -0,0 +1 @@ +0.0,1.0 diff --git a/tfmodels/Monomial_Mk12_M4_2D_gamma3/best_model/keras_metadata.pb b/tfmodels/Monomial_Mk12_M4_2D_gamma3/best_model/keras_metadata.pb new file mode 100644 index 00000000..9810b092 --- /dev/null +++ b/tfmodels/Monomial_Mk12_M4_2D_gamma3/best_model/keras_metadata.pb @@ -0,0 +1,35 @@ + +root"_tf_keras_model*{"name": "sobolev_model", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "SobolevModel", "config": {"layer was saved 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+--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk13_M3_2D/information/runScript_002_.sh b/tfmodels/Monomial_Mk13_M3_2D/information/runScript_002_.sh new file mode 100644 index 00000000..cfbc495c --- /dev/null +++ b/tfmodels/Monomial_Mk13_M3_2D/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=3 \ +--epoch=2000 \ +--folder=mk13_M3_2D_g0 \ +--loadModel=1 \ +--model=13 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk13_M3_2D/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk13_M3_2D/information/scaling_data/min_max_scaler.csv new file mode 100644 index 00000000..4b855abf --- /dev/null +++ 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+--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk13_M3_2D_gamma1/information/runScript_002_.sh b/tfmodels/Monomial_Mk13_M3_2D_gamma1/information/runScript_002_.sh new file mode 100644 index 00000000..cf7d8d2c --- /dev/null +++ b/tfmodels/Monomial_Mk13_M3_2D_gamma1/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=3 \ +--epoch=2000 \ +--folder=mk13_M3_2D_g1 \ +--loadModel=1 \ +--model=13 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk13_M3_2D_gamma1/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk13_M3_2D_gamma1/information/scaling_data/min_max_scaler.csv new file mode 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+--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk13_M3_2D_gamma2/information/runScript_002_.sh b/tfmodels/Monomial_Mk13_M3_2D_gamma2/information/runScript_002_.sh new file mode 100644 index 00000000..b033d916 --- /dev/null +++ b/tfmodels/Monomial_Mk13_M3_2D_gamma2/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=3 \ +--epoch=2000 \ +--folder=mk13_M3_2D_g2 \ +--loadModel=1 \ +--model=13 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk13_M3_2D_gamma2/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk13_M3_2D_gamma2/information/scaling_data/min_max_scaler.csv new file mode 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+--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk13_M3_2D_gamma3/information/runScript_002_.sh b/tfmodels/Monomial_Mk13_M3_2D_gamma3/information/runScript_002_.sh new file mode 100644 index 00000000..0a9d07cf --- /dev/null +++ b/tfmodels/Monomial_Mk13_M3_2D_gamma3/information/runScript_002_.sh @@ -0,0 +1,19 @@ +python callNeuralClosure.py \ +--sampling=2 \ +--batch=256 \ +--curriculum=1 \ +--degree=3 \ +--epoch=2000 \ +--folder=mk13_M3_2D_g3 \ +--loadModel=1 \ +--model=13 \ +--normalized=1 \ +--scaledOutput=0 \ +--decorrInput=1 \ +--objective=2 \ +--processingmode=1 \ +--spatialDimension=2 \ +--training=1 \ +--verbosity=2 \ +--networkwidth=100 \ +--networkdepth=3 \ No newline at end of file diff --git a/tfmodels/Monomial_Mk13_M3_2D_gamma3/information/scaling_data/min_max_scaler.csv b/tfmodels/Monomial_Mk13_M3_2D_gamma3/information/scaling_data/min_max_scaler.csv new file mode 100644 index 00000000..4b855abf --- /dev/null +++ b/tfmodels/Monomial_Mk13_M3_2D_gamma3/information/scaling_data/min_max_scaler.csv @@ -0,0 +1 @@ +0.0,1.0 diff --git a/tools/CI/docker_run_interactive_specify_cores.sh b/tools/CI/docker_run_interactive_specify_cores.sh index c47d89f5..94c11cb5 100644 --- a/tools/CI/docker_run_interactive_specify_cores.sh +++ b/tools/CI/docker_run_interactive_specify_cores.sh @@ -1 +1 @@ -docker run -i -t --rm --cpuset-cpus=0-9 -v $(pwd)/../..:/mnt kitrt/test_ml:latest /bin/bash +docker run -i -t --rm --cpuset-cpus=0-8 -v $(pwd)/../..:/mnt kitrt/test_ml:latest /bin/bash diff --git a/tools/CI/install_kitrt.sh b/tools/CI/install_kitrt.sh index cced0a90..6b96e342 100755 --- a/tools/CI/install_kitrt.sh +++ b/tools/CI/install_kitrt.sh @@ -8,5 +8,5 @@ rm -rf * cmake -G Ninja -DCMAKE_BUILD_UNITY=True -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTING=False -DBUILD_ML=True ../ ninja cd ../examples -../build/KiT-RT checkerboard_PN.cfg -ls +../build/KiT-RT checkerboard_MN.cfg +cp -r /home/result /mnt/result