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mod_dense_layer

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Implementation of a dense layer type for a neural network. author: Jonas Finkler, Alexander Knoll

Dependencies

Uses

module~~mod_dense_layer~~UsesGraph module~mod_dense_layer mod_dense_layer module~mod_activationfunctions mod_activationfunctions module~mod_dense_layer->module~mod_activationfunctions module~mod_constants mod_constants module~mod_dense_layer->module~mod_constants module~mod_decorations mod_decorations module~mod_dense_layer->module~mod_decorations module~mod_global_warning mod_global_warning module~mod_dense_layer->module~mod_global_warning module~mod_initialize_array mod_initialize_array module~mod_dense_layer->module~mod_initialize_array module~mod_io mod_io module~mod_dense_layer->module~mod_io module~mod_linalg mod_linalg module~mod_dense_layer->module~mod_linalg module~mod_nan_check mod_nan_check module~mod_dense_layer->module~mod_nan_check module~mod_optional_args mod_optional_args module~mod_dense_layer->module~mod_optional_args module~mod_precision mod_precision module~mod_dense_layer->module~mod_precision module~mod_random mod_random module~mod_dense_layer->module~mod_random module~mod_throw mod_throw module~mod_dense_layer->module~mod_throw module~mod_activationfunctions->module~mod_throw module~mod_activationfunction_abstract mod_activationfunction_abstract module~mod_activationfunctions->module~mod_activationfunction_abstract module~mod_activationfunction_linear mod_activationfunction_linear module~mod_activationfunctions->module~mod_activationfunction_linear module~mod_activationfunction_relu mod_activationfunction_relu module~mod_activationfunctions->module~mod_activationfunction_relu module~mod_activationfunction_sigmoid mod_activationfunction_sigmoid module~mod_activationfunctions->module~mod_activationfunction_sigmoid module~mod_activationfunction_softplus mod_activationfunction_softplus module~mod_activationfunctions->module~mod_activationfunction_softplus module~mod_activationfunction_square mod_activationfunction_square module~mod_activationfunctions->module~mod_activationfunction_square module~mod_activationfunction_stanh mod_activationfunction_stanh module~mod_activationfunctions->module~mod_activationfunction_stanh module~mod_activationfunction_tanh mod_activationfunction_tanh module~mod_activationfunctions->module~mod_activationfunction_tanh module~mod_constants->module~mod_precision iso_c_binding iso_c_binding module~mod_constants->iso_c_binding module~mod_decorations->module~mod_io module~mod_decorations->module~mod_precision module~mod_decorations->module~mod_throw module~mod_global_warning->module~mod_io module~mod_global_warning->module~mod_throw module~mod_abstract_warning mod_abstract_warning module~mod_global_warning->module~mod_abstract_warning module~mod_initialize_array->module~mod_io module~mod_initialize_array->module~mod_precision module~mod_initialize_array->module~mod_random module~mod_initialize_array->module~mod_throw module~mod_mpi mod_mpi module~mod_initialize_array->module~mod_mpi module~mod_parameter_file mod_parameter_file module~mod_initialize_array->module~mod_parameter_file module~mod_io->module~mod_optional_args module~mod_io->module~mod_precision iso_fortran_env iso_fortran_env module~mod_io->iso_fortran_env module~mod_linalg->module~mod_precision module~mod_linalg->module~mod_throw module~cublas_wrapper cublas_wrapper module~mod_linalg->module~cublas_wrapper module~mod_settings mod_settings module~mod_linalg->module~mod_settings module~mod_nan_check->module~mod_io module~mod_nan_check->module~mod_precision module~mod_nan_check->module~mod_throw ieee_arithmetic ieee_arithmetic module~mod_nan_check->ieee_arithmetic module~mod_precision->iso_c_binding module~mod_precision->iso_fortran_env module~mod_random->module~mod_constants module~mod_random->module~mod_precision module~mod_throw->module~mod_io module~mod_throw->module~mod_precision module~mod_throw->iso_c_binding module~mod_throw->iso_fortran_env module~mod_throw->module~mod_mpi module~mod_sort mod_sort module~mod_throw->module~mod_sort module~cublas_wrapper->iso_c_binding module~cublas_wrapper->module~mod_settings module~mod_activationfunction_linear->module~mod_precision module~mod_activationfunction_linear->module~mod_activationfunction_abstract module~mod_activationfunction_relu->module~mod_precision module~mod_activationfunction_relu->module~mod_activationfunction_abstract module~mod_activationfunction_sigmoid->module~mod_precision module~mod_activationfunction_sigmoid->module~mod_activationfunction_abstract module~mod_activationfunction_softplus->module~mod_precision module~mod_activationfunction_softplus->module~mod_activationfunction_abstract module~mod_activationfunction_square->module~mod_precision module~mod_activationfunction_square->module~mod_activationfunction_abstract module~mod_activationfunction_stanh->module~mod_precision module~mod_activationfunction_stanh->module~mod_activationfunction_abstract module~mod_activationfunction_tanh->module~mod_precision module~mod_activationfunction_tanh->module~mod_activationfunction_abstract module~mod_mpi->module~mod_precision mpi_f08 mpi_f08 module~mod_mpi->mpi_f08 module~mod_parameter_file->module~mod_io module~mod_parameter_file->module~mod_nan_check module~mod_parameter_file->module~mod_precision module~mod_parameter_file->module~mod_throw module~mod_abstract_broadcast_file mod_abstract_broadcast_file module~mod_parameter_file->module~mod_abstract_broadcast_file module~mod_settings->module~mod_constants module~mod_settings->module~mod_io module~mod_settings->module~mod_precision module~mod_settings->module~mod_throw module~mod_cpu_vendor mod_cpu_vendor module~mod_settings->module~mod_cpu_vendor module~mod_periodic_table mod_periodic_table module~mod_settings->module~mod_periodic_table module~mod_sort->module~mod_precision module~mod_abstract_broadcast_file->module~mod_io module~mod_abstract_broadcast_file->module~mod_throw module~mod_abstract_broadcast_file->module~mod_mpi module~mod_genericfile mod_genericfile module~mod_abstract_broadcast_file->module~mod_genericfile module~mod_sanitize mod_sanitize module~mod_abstract_broadcast_file->module~mod_sanitize module~mod_cpu_vendor->iso_c_binding module~mod_periodic_table->module~mod_constants module~mod_periodic_table->module~mod_io module~mod_periodic_table->module~mod_precision module~mod_periodic_table->module~mod_throw module~mod_array_helpers mod_array_helpers module~mod_periodic_table->module~mod_array_helpers module~mod_array_helpers->module~mod_precision module~mod_array_helpers->module~mod_throw module~mod_genericfile->module~mod_io module~mod_genericfile->module~mod_throw module~mod_genericfile->module~mod_mpi module~mod_sanitize->module~mod_throw

Used By

module~~mod_dense_layer~~UsedByGraph module~mod_dense_layer mod_dense_layer module~mod_dense_layer_with_single_activations mod_dense_layer_with_single_activations module~mod_dense_layer_with_single_activations->module~mod_dense_layer module~mod_layers mod_layers module~mod_layers->module~mod_dense_layer module~mod_layers->module~mod_dense_layer_with_single_activations module~mod_neural_network mod_neural_network module~mod_neural_network->module~mod_layers module~mod_hdnn mod_hdnn module~mod_hdnn->module~mod_neural_network module~mod_initialize mod_initialize module~mod_initialize->module~mod_hdnn module~mod_predict_mode mod_predict_mode module~mod_predict_mode->module~mod_initialize module~mod_write_outfiles mod_write_outfiles module~mod_predict_mode->module~mod_write_outfiles module~mod_runner_interface_bindings mod_runner_interface_bindings module~mod_runner_interface_bindings->module~mod_initialize module~mod_train_energy_multiple_structures mod_train_energy_multiple_structures module~mod_train_energy_multiple_structures->module~mod_initialize module~mod_train_forces_multiple_structures mod_train_forces_multiple_structures module~mod_train_forces_multiple_structures->module~mod_initialize module~mod_train_forces_multiple_structures->module~mod_train_energy_multiple_structures module~mod_train_loop_4g_charges mod_train_loop_4g_charges module~mod_train_loop_4g_charges->module~mod_initialize module~mod_cost_calculator mod_cost_calculator module~mod_train_loop_4g_charges->module~mod_cost_calculator module~mod_output_files_and_cost_4g_charges mod_output_files_and_cost_4g_charges module~mod_train_loop_4g_charges->module~mod_output_files_and_cost_4g_charges module~mod_train_loop_atomic_property mod_train_loop_atomic_property module~mod_train_loop_atomic_property->module~mod_initialize module~mod_train_loop_atomic_property->module~mod_cost_calculator module~mod_output_files_and_cost mod_output_files_and_cost module~mod_train_loop_atomic_property->module~mod_output_files_and_cost module~mod_train_loop_short_range mod_train_loop_short_range module~mod_train_loop_short_range->module~mod_initialize module~mod_train_loop_short_range->module~mod_train_energy_multiple_structures module~mod_train_loop_short_range->module~mod_train_forces_multiple_structures module~mod_train_loop_short_range->module~mod_cost_calculator module~mod_train_loop_short_range->module~mod_output_files_and_cost module~mod_output_files_and_cost_4g_short_range mod_output_files_and_cost_4g_short_range module~mod_train_loop_short_range->module~mod_output_files_and_cost_4g_short_range module~mod_train_mode mod_train_mode module~mod_train_mode->module~mod_initialize module~mod_train_mode->module~mod_train_loop_4g_charges module~mod_train_mode->module~mod_train_loop_atomic_property module~mod_train_mode->module~mod_train_loop_short_range module~mod_write_outfiles_training mod_write_outfiles_training module~mod_train_mode->module~mod_write_outfiles_training module~mod_write_outfiles->module~mod_initialize program~runner RuNNer program~runner->module~mod_initialize program~runner->module~mod_predict_mode program~runner->module~mod_train_mode module~mod_lammps_interface mod_lammps_interface module~mod_lammps_interface->module~mod_runner_interface_bindings module~mod_write_outfiles_training->module~mod_write_outfiles module~mod_cost_calculator->module~mod_write_outfiles_training module~mod_output_files_and_cost->module~mod_cost_calculator module~mod_output_files_and_cost_4g_charges->module~mod_cost_calculator module~mod_output_files_and_cost_4g_short_range->module~mod_cost_calculator

Derived Types Summary

Name Description
DenseLayer A dense (fully-connected) layer for a feed-forward neural network. In a dense layer, all nodes...

Procedures Summary

Name Type Description
init_denselayer Subroutine Construct a new DenseLayer object.
reset_denselayer Subroutine Reset a DenseLayer object. Should be called before any new backward pass.
reset_prediction_denselayer Subroutine
reallocate_denselayer Subroutine Change the dimensions of all internal arrays to accomodate batchsize input and output values.
print_denselayer Subroutine Print information about a DenseLayer object.
get_parameters_denselayer Subroutine Cast all free parameters of a DenseLayer object in one array of shape (1 x num_parameters).
set_parameters_denselayer Subroutine Set the free parameters of a DenseLayer object based on the flat array parameters.
get_gradients_wrt_parameters_denselayer Subroutine Cast the gradient of all parameters of a DenseLayer object in one array of shape (1 x...
forward_denselayer Subroutine Entry point for dense layer forward passes. This is the top-level routine for forward passes...
backward_denselayer Subroutine Entry point for dense layer backward passes. See <a...
forward_denselayer_omp_parallel Subroutine Calculate the output of the dense layer for a given batch of input samples. The output of a...
forward_denselayer_serial Subroutine Calculate the output of the dense layer for a given batch of input samples. The output of a...
set_biases_block Subroutine Helper to set biases for a block to avoid obscure array syntax in OMP regions
forward_gradient_denselayer Subroutine Perform a forward pass, storing the grad wrt the neural network input.
backward_denselayer_serial Subroutine Perform a batchwise backward pass for a dense layer.
backward_denselayer_omp_parallel Subroutine Perform an OpenMP-parallel, batchwise backward pass for a dense layer. This routine is very...
backward_gradient_denselayer Subroutine Perform a batchwise backward pass for a dense layer including the contributions of the input...
init_parameter_hidden_layer Subroutine Wrapper around the actual parameter initialization routines. See the documentation of the...
init_hidden_layer_legacy_xavier Subroutine Part of weight initialization as described in PHYSICAL REVIEW B 102, 174102 (2020) and J. Chem....
init_hidden_layer_lecun_normal Subroutine LeCun normal initialization. Very similar to the original legacy_xavier initialization. We could...
init_hidden_layer_lecun_uniform Subroutine The sqrt(3) is to give unit variance and is not dependent on the type of activation function...
init_hidden_layer_xavier Subroutine Real famous Xavier initialization as described by Glorot / Bengio in Proceedings of the...
init_hidden_layer_xavier_modified Subroutine Modification as described in J.Chem. Theory Comput. 2023, 19,3509−3525.
init_hidden_layer_nguyen_widrow Subroutine Original Nguyen-Widrow initialization scheme This is the original Nguyen-Widrow initialization...
init_hidden_layer_nguyen_widrow_runner1 Subroutine Modified Nguyen-Widrow initialization scheme similar to RuNNer 1 This is not exactly the same...
init_uniform_minus05_to_05 Subroutine Initialize the weights from -0.5 to 0.5. Wrapper around initialize_array. The same logical was...
init_parameter_last_layer Subroutine Wrapper around the actual parameter initialization routines. See the documentation of the...
init_last_layer_eckhoff Subroutine Part of weight initialization as described in PHYSICAL REVIEW B 102, 174102 (2020) and J. Chem....
init_last_layer_eckhoff_modified Subroutine
init_last_layer_precondition Subroutine Precondition weights and bias values.

Derived Types Details

type DenseLayer

A dense (fully-connected) layer for a feed-forward neural network. In a dense layer, all nodes have incoming and outgoing connections, i.e. weights, to all nodes in the previous and following layers.

Components

  • num_parameters : integer– The total number of free parameters in the model. This should always be set by the `constructor` procedure of the derived type.
  • num_parameters_mask : integer– The number of parameters that are not kept frozen by `mask`.
  • mask : logical (dim: (:))– A logical array specifying, for each free parameter, whether it should be optimized or not.
  • batchsize : integer– The number of samples that can pass concurrently through the layer.
  • num_forward_passes : integer– Inherited from DenseLayer
  • num_backward_passes : integer– Inherited from DenseLayer
  • num_nodes_input : integer– The number of input nodes to this layer.
  • num_nodes : integer– The number of nodes in this layer.
  • idx : integer– Layer index.
  • weights : real(DP) (dim: (:,:))– Connections to preceding layer nodes.
  • biases : real(DP) (dim: (:))– Bias values for shifting each node's offset.
  • dw : real(DP) (dim: (:,:))– Gradients of `weights`.
  • db : real(DP) (dim: (:))– Gradients of `biases`.
  • z : real(DP) (dim: (:,:))– Node values before activation. Required for backpropagation.
  • dz_dq : real(DP) (dim: (:,:))– Derivatives of the unactivated node values wrt a single coordinate.
  • da_dq : real(DP) (dim: (:,:))– Derivatives of the activated node values wrt a single coordinate.
  • x : real(DP) (dim: (:,:))– The input to the layer. Dimensions are inputsize x batchsize.
  • a : real(DP) (dim: (:,:))– Layer output.
  • grad : real(DP) (dim: (:,:))– doutput/dinput.
  • dgrad_dq : real(DP) (dim: (:,:))– doutput/dinput/dx
  • f_act : class(AbstractActivationFunction)– Activation function.
  • lsaturation_check : logical– Inherited from DenseLayer
  • forward_ptr : procedure– Pointer for delegating the forward pass to the correct implementation. We have an OpenMP-parallel and a non-parallel forward pass implementation. The serial implementation is more efficient for small batchsizes. We align the pointer whenever reallocate_inputlayer is called, which is usually only once.
  • backward_ptr : procedure– Pointer for delegating the backward pass. See forward_ptr for details.

Type-Bound Procedures

Name Attributes Binding
initialize public ⇒ init_denselayer
print public ⇒ print_denselayer
get_parameters public ⇒ get_parameters_denselayer
set_parameters public ⇒ set_parameters_denselayer
get_gradients_wrt_parameters public ⇒ get_gradients_wrt_parameters_denselayer
reset public ⇒ reset_denselayer
reset_prediction public ⇒ reset_prediction_denselayer
forward public ⇒ forward_denselayer
forward_gradient public ⇒ forward_gradient_denselayer
backward public ⇒ backward_denselayer
backward_gradient public ⇒ backward_gradient_denselayer
reallocate public ⇒ reallocate_denselayer
init_parameter_hidden_layer public ⇒ init_parameter_hidden_layer
init_parameter_last_layer public ⇒ init_parameter_last_layer
init_hidden_layer_legacy_xavier private ⇒ init_hidden_layer_legacy_xavier
init_hidden_layer_lecun_uniform private ⇒ init_hidden_layer_lecun_uniform
init_hidden_layer_lecun_normal private ⇒ init_hidden_layer_lecun_normal
init_hidden_layer_xavier private ⇒ init_hidden_layer_xavier
init_hidden_layer_xavier_modified private ⇒ init_hidden_layer_xavier_modified
init_hidden_layer_nguyen_widrow private ⇒ init_hidden_layer_nguyen_widrow
init_hidden_layer_nguyen_widrow_runner1 private ⇒ init_hidden_layer_nguyen_widrow_runner1
init_last_layer_eckhoff private ⇒ init_last_layer_eckhoff
init_last_layer_eckhoff_modified private ⇒ init_last_layer_eckhoff_modified
init_last_layer_precondition public ⇒ init_last_layer_precondition
init_uniform_minus05_to_05 private ⇒ init_uniform_minus05_to_05

Inherits

type~~denselayer~~InheritsGraph type~denselayer DenseLayer type~abstractactivationfunction AbstractActivationFunction type~denselayer->type~abstractactivationfunction f_act

Inherited By

type~~denselayer~~InheritedByGraph type~denselayer DenseLayer type~denselayerwithsingleactivations DenseLayerWithSingleActivations type~denselayerwithsingleactivations->type~denselayer type~neuralnetwork NeuralNetwork type~neuralnetwork->type~denselayer hidden_layers, output_layer type~hdnn HDNN type~hdnn->type~neuralnetwork atomic_nns

Procedure Details

subroutine init_denselayer

subroutine init_denselayer( &
    self, &
    num_nodes_input, &
    num_nodes, &
    activation, &
    batchsize, &
    idx, &
    lsaturation_check, &
    mask &
)

Construct a new DenseLayer object.

Arguments

  • self : class(DenseLayer) [inout]– The constructed layer.
  • num_nodes_input : integer [in]– The number of nodes in the preceeding layer.
  • num_nodes : integer [in]– The number of requested nodes in this layer.
  • activation : class(AbstractActivationFunction) [in]– The activation function for all nodes.
  • batchsize : integer [in]
  • idx : integer [in]
  • lsaturation_check : logical [in]
  • mask : logical (dim: (:)) [in]– logical mask

subroutine reset_denselayer

subroutine reset_denselayer( &
    self &
)

Reset a DenseLayer object. Should be called before any new backward pass.

Arguments

Called By

proc~~reset_denselayer~~CalledByGraph proc~reset_denselayer mod_dense_layer::DenseLayer%reset_denselayer proc~reallocate_denselayer mod_dense_layer::DenseLayer%reallocate_denselayer proc~reallocate_denselayer->proc~reset_denselayer proc~init_denselayer mod_dense_layer::DenseLayer%init_denselayer proc~init_denselayer->proc~reallocate_denselayer proc~init_denselayer_with_single_act mod_dense_layer_with_single_activations::DenseLayerWithSingleActivations%init_denselayer_with_single_act proc~init_denselayer_with_single_act->proc~reallocate_denselayer proc~initialize_nn_from_architecture mod_neural_network::NeuralNetwork%initialize_nn_from_architecture proc~initialize_nn_from_architecture->proc~init_denselayer_with_single_act none~initialize mod_neural_network::NeuralNetwork%initialize none~initialize->proc~initialize_nn_from_architecture proc~initialize_from_architecture mod_hdnn::HDNN%initialize_from_architecture proc~initialize_from_architecture->none~initialize proc~initialize_from_file_hdnn mod_hdnn::HDNN%initialize_from_file_hdnn proc~initialize_from_file_hdnn->proc~initialize_from_architecture

subroutine reset_prediction_denselayer

subroutine reset_prediction_denselayer( &
    self &
)

Arguments

Called By

proc~~reset_prediction_denselayer~~CalledByGraph proc~reset_prediction_denselayer mod_dense_layer::DenseLayer%reset_prediction_denselayer proc~reset_denselayer mod_dense_layer::DenseLayer%reset_denselayer proc~reset_denselayer->proc~reset_prediction_denselayer proc~reallocate_denselayer mod_dense_layer::DenseLayer%reallocate_denselayer proc~reallocate_denselayer->proc~reset_denselayer proc~init_denselayer mod_dense_layer::DenseLayer%init_denselayer proc~init_denselayer->proc~reallocate_denselayer proc~init_denselayer_with_single_act mod_dense_layer_with_single_activations::DenseLayerWithSingleActivations%init_denselayer_with_single_act proc~init_denselayer_with_single_act->proc~reallocate_denselayer proc~initialize_nn_from_architecture mod_neural_network::NeuralNetwork%initialize_nn_from_architecture proc~initialize_nn_from_architecture->proc~init_denselayer_with_single_act none~initialize mod_neural_network::NeuralNetwork%initialize none~initialize->proc~initialize_nn_from_architecture proc~initialize_from_architecture mod_hdnn::HDNN%initialize_from_architecture proc~initialize_from_architecture->none~initialize proc~initialize_from_file_hdnn mod_hdnn::HDNN%initialize_from_file_hdnn proc~initialize_from_file_hdnn->proc~initialize_from_architecture

subroutine reallocate_denselayer

subroutine reallocate_denselayer( &
    self, &
    batchsize &
)

Change the dimensions of all internal arrays to accomodate batchsize input and output values.

Arguments

  • self : class(DenseLayer) [inout]
  • batchsize : integer [in]

Called By

proc~~reallocate_denselayer~~CalledByGraph proc~reallocate_denselayer mod_dense_layer::DenseLayer%reallocate_denselayer proc~init_denselayer mod_dense_layer::DenseLayer%init_denselayer proc~init_denselayer->proc~reallocate_denselayer proc~init_denselayer_with_single_act mod_dense_layer_with_single_activations::DenseLayerWithSingleActivations%init_denselayer_with_single_act proc~init_denselayer_with_single_act->proc~reallocate_denselayer proc~initialize_nn_from_architecture mod_neural_network::NeuralNetwork%initialize_nn_from_architecture proc~initialize_nn_from_architecture->proc~init_denselayer_with_single_act none~initialize mod_neural_network::NeuralNetwork%initialize none~initialize->proc~initialize_nn_from_architecture proc~initialize_from_architecture mod_hdnn::HDNN%initialize_from_architecture proc~initialize_from_architecture->none~initialize proc~initialize_from_file_hdnn mod_hdnn::HDNN%initialize_from_file_hdnn proc~initialize_from_file_hdnn->proc~initialize_from_architecture

subroutine print_denselayer

subroutine print_denselayer( &
    self &
)

Print information about a DenseLayer object.

Arguments

subroutine get_parameters_denselayer

subroutine get_parameters_denselayer( &
    self, &
    parameters, &
    luse_mask &
)

Cast all free parameters of a DenseLayer object in one array of shape (1 x num_parameters).

Arguments

  • self : class(DenseLayer) [in]
  • parameters : real(DP) (dim: (:)) [inout]– The parameters array to be filled.
  • luse_mask : logical [in]– Gets parameters with or without mask.

Called By

proc~~get_parameters_denselayer~~CalledByGraph proc~get_parameters_denselayer mod_dense_layer::DenseLayer%get_parameters_denselayer proc~get_parameters_neuralnetwork mod_neural_network::NeuralNetwork%get_parameters_neuralnetwork proc~get_parameters_neuralnetwork->proc~get_parameters_denselayer proc~get_parameters_hdnn mod_hdnn::HDNN%get_parameters_hdnn proc~get_parameters_hdnn->proc~get_parameters_neuralnetwork proc~write_parameters_to_file_hdnn mod_hdnn::HDNN%write_parameters_to_file_hdnn proc~write_parameters_to_file_hdnn->proc~get_parameters_neuralnetwork

subroutine set_parameters_denselayer

subroutine set_parameters_denselayer( &
    self, &
    parameters, &
    luse_mask &
)

Set the free parameters of a DenseLayer object based on the flat array parameters.

Arguments

  • self : class(DenseLayer) [inout]
  • parameters : real(DP) (dim: (:)) [in]– The filled parameters array.
  • luse_mask : logical [in]– Sets parameters with or without mask.

Called By

proc~~set_parameters_denselayer~~CalledByGraph proc~set_parameters_denselayer mod_dense_layer::DenseLayer%set_parameters_denselayer proc~set_parameters_neuralnetwork mod_neural_network::NeuralNetwork%set_parameters_neuralnetwork proc~set_parameters_neuralnetwork->proc~set_parameters_denselayer proc~set_parameters_hdnn mod_hdnn::HDNN%set_parameters_hdnn proc~set_parameters_hdnn->proc~set_parameters_neuralnetwork

subroutine get_gradients_wrt_parameters_denselayer

subroutine get_gradients_wrt_parameters_denselayer( &
    self, &
    gradients, &
    luse_mask &
)

Cast the gradient of all parameters of a DenseLayer object in one array of shape (1 x num_parameters).

Arguments

  • self : class(DenseLayer) [inout]
  • gradients : real(DP) (dim: (:)) [inout]– The list of gradients, one for each free parameter in `self`.
  • luse_mask : logical [in]– Gets gradients with or without mask.

Called By

proc~~get_gradients_wrt_parameters_denselayer~~CalledByGraph proc~get_gradients_wrt_parameters_denselayer mod_dense_layer::DenseLayer%get_gradients_wrt_parameters_denselayer proc~get_gradients_wrt_parameters_neuralnetwork mod_neural_network::NeuralNetwork%get_gradients_wrt_parameters_neuralnetwork proc~get_gradients_wrt_parameters_neuralnetwork->proc~get_gradients_wrt_parameters_denselayer proc~get_gradients_wrt_parameters_hdnn mod_hdnn::HDNN%get_gradients_wrt_parameters_hdnn proc~get_gradients_wrt_parameters_hdnn->proc~get_gradients_wrt_parameters_neuralnetwork

subroutine forward_denselayer

subroutine forward_denselayer( &
    self, &
    x &
)

Entry point for dense layer forward passes.

This is the top-level routine for forward passes that is exposed as the public interface of this type. It is a wrapper routine that delegates the forward call to one of the two forward implementations. See forward_ptr for details.

Arguments

  • self : class(DenseLayer) [inout]
  • x : real(DP) (dim: (self%num_nodes_input,self%batchsize)) [in]

Called By

proc~~forward_denselayer~~CalledByGraph proc~forward_denselayer mod_dense_layer::DenseLayer%forward_denselayer proc~forward_gradient_denselayer mod_dense_layer::DenseLayer%forward_gradient_denselayer proc~forward_gradient_denselayer->proc~forward_denselayer

subroutine backward_denselayer

subroutine backward_denselayer( &
    self, &
    grad_follow, &
    calc_gradient_wrt_parameters &
)

Entry point for dense layer backward passes.

See forward_denselayer for details.

Arguments

  • self : class(DenseLayer) [inout]
  • grad_follow : real(DP) (dim: (self%num_nodes,self%batchsize)) [in]
  • calc_gradient_wrt_parameters : logical [in]

Called By

proc~~backward_denselayer~~CalledByGraph proc~backward_denselayer mod_dense_layer::DenseLayer%backward_denselayer proc~backward_neuralnetwork mod_neural_network::NeuralNetwork%backward_neuralnetwork proc~backward_neuralnetwork->proc~backward_denselayer proc~calc_jacobian_neuralnetwork mod_neural_network::NeuralNetwork%calc_jacobian_neuralnetwork proc~calc_jacobian_neuralnetwork->proc~backward_neuralnetwork proc~calc_jacobian_hdnn mod_hdnn::HDNN%calc_jacobian_hdnn proc~calc_jacobian_hdnn->proc~calc_jacobian_neuralnetwork

subroutine forward_denselayer_omp_parallel

subroutine forward_denselayer_omp_parallel( &
    self, &
    x &
)

Calculate the output of the dense layer for a given batch of input samples.

The output of a dense layer is calculated according to output = activation(weights * input + biases) We parallelize the Batch Dimension (N). Each thread handles a subset of the columns (samples).

Arguments

  • self : class(DenseLayer) [inout]
  • x : real(DP) (dim: (self%num_nodes_input,self%batchsize)) [in]

subroutine forward_denselayer_serial

subroutine forward_denselayer_serial( &
    self, &
    x &
)

Calculate the output of the dense layer for a given batch of input samples.

The output of a dense layer is calculated according to output = activation(weights * input + biases) This routine is not OMP parallelized. For small batches, the overhead associated with thread initialization becomes too large.

Arguments

  • self : class(DenseLayer) [inout]
  • x : real(DP) (dim: (self%num_nodes_input,self%batchsize)) [in]

subroutine set_biases_block

subroutine set_biases_block( &
    z, &
    b, &
    istart, &
    iend, &
    n_nodes &
)

Helper to set biases for a block to avoid obscure array syntax in OMP regions

Arguments

  • z : real(DP) (dim: (n_nodes,*)) [inout]
  • b : real(DP) (dim: (n_nodes)) [in]
  • istart : integer [in]
  • iend : integer [in]
  • n_nodes : integer [in]

Called By

proc~~set_biases_block~~CalledByGraph proc~set_biases_block mod_dense_layer::set_biases_block proc~forward_denselayer_omp_parallel mod_dense_layer::forward_denselayer_omp_parallel proc~forward_denselayer_omp_parallel->proc~set_biases_block

subroutine forward_gradient_denselayer

subroutine forward_gradient_denselayer( &
    self, &
    x, &
    dx_dq &
)

Perform a forward pass, storing the grad wrt the neural network input.

Arguments

  • self : class(DenseLayer) [inout]
  • x : real(DP) (dim: (self%num_nodes_input,self%batchsize)) [in]– The activations of the preceeding layer.
  • dx_dq : real(DP) (dim: (self%num_nodes_input,self%batchsize)) [in]– Derivative of the preceeding layer activations `x` with respect to some variable q.

subroutine backward_denselayer_serial

subroutine backward_denselayer_serial( &
    self, &
    grad_follow, &
    calc_gradient_wrt_parameters &
)

Perform a batchwise backward pass for a dense layer.

Arguments

  • self : class(DenseLayer) [inout]
  • grad_follow : real(DP) (dim: (self%num_nodes,self%batchsize)) [in]
  • calc_gradient_wrt_parameters : logical [in]

subroutine backward_denselayer_omp_parallel

subroutine backward_denselayer_omp_parallel( &
    self, &
    grad_follow, &
    calc_gradient_wrt_parameters &
)

Perform an OpenMP-parallel, batchwise backward pass for a dense layer.

This routine is very efficient for large batch sizes. For small batches, use backward_denselayer_serial. @todo manual sum may be faster than sum(..., dim=2) for short arrays

Arguments

  • self : class(DenseLayer) [inout]
  • grad_follow : real(DP) (dim: (self%num_nodes,self%batchsize)) [in]
  • calc_gradient_wrt_parameters : logical [in]

subroutine backward_gradient_denselayer

subroutine backward_gradient_denselayer( &
    self, &
    dx_dq, &
    grad_follow, &
    dgrad_dq_follow &
)

Perform a batchwise backward pass for a dense layer including the contributions of the input gradient. All math is documented at the relevant places. Notation: * = Hadamard (element-wise) product . = Dot product (matrix multiplication) = (d^2a3/da2dx * f'(z2) ) . a1 + (da3/da2 * f''(z2) * dz2/dx) . a1 + (da3/da2 * f'(z2) ) . da1/dx

Arguments

  • self : class(DenseLayer) [inout]
  • dx_dq : real(DP) (dim: (self%num_nodes_input,self%batchsize)) [in]
  • grad_follow : real(DP) (dim: (self%num_nodes,self%batchsize)) [in]
  • dgrad_dq_follow : real(DP) (dim: (self%num_nodes,self%batchsize)) [in]

Called By

proc~~backward_gradient_denselayer~~CalledByGraph proc~backward_gradient_denselayer mod_dense_layer::DenseLayer%backward_gradient_denselayer proc~backward_gradient_neuralnetwork mod_neural_network::NeuralNetwork%backward_gradient_neuralnetwork proc~backward_gradient_neuralnetwork->proc~backward_gradient_denselayer

subroutine init_parameter_hidden_layer

subroutine init_parameter_hidden_layer( &
    self, &
    init_method, &
    rng, &
    min_weight, &
    max_weight &
)

Wrapper around the actual parameter initialization routines.

See the documentation of the actual routines for detailed information.

Arguments

  • self : class(DenseLayer) [inout]
  • init_method : character [in]– The initialization method to be used. Can be one of: - 'legacy_xavier' or 'legacy_glorot': Initialization as described PHYSICAL REVIEW B 102, 174102 (2020) and J. Chem. Phys. 153, 164107 (2020). This is not the famous Xavier initialization - 'xavier' or 'glorot': The famous Xavier initialization - 'xavier_modified' or 'glorot_modified': Modified Xavier initialization - 'lecun_normal': LeCun initialization based on normal distribution. The same as legacy_xavier, but with normal distribution instead of uniform. - 'lecun_uniform', 'legacy_xavier_modified', 'legacy_glorot_modified': LeCun initialization based on uniform distribution. - 'nguyen_widrow': Nguyen-Widrow initialization - 'nguyen_widrow_legacy' or 'nguyen_widrow_runner1': Nguyen-Widrow initialization as in RuNNer 1 - 'uniform': Uniform initialization - 'normal' or 'gaussian': Normal initialization - 'zero': Initialize all weights and biases to zero See their respective documentation for more information. For all other values an error will be thrown.
  • rng : class(RandomNumberGenerator) [inout]– The random number generator to be used for initialization.
  • min_weight : real(DP) [in]– The minimum value for the weights. Only used for 'uniform', 'normal', and 'gaussian' initialization.
  • max_weight : real(DP) [in]– The maximum value for the weights. Only used for 'uniform', 'normal', and 'gaussian' initialization.

Called By

proc~~init_parameter_hidden_layer~~CalledByGraph proc~init_parameter_hidden_layer mod_dense_layer::DenseLayer%init_parameter_hidden_layer proc~init_parameters mod_neural_network::NeuralNetwork%init_parameters proc~init_parameters->proc~init_parameter_hidden_layer proc~initialize_parameters_hdnn mod_hdnn::HDNN%initialize_parameters_hdnn proc~initialize_parameters_hdnn->proc~init_parameters

subroutine init_hidden_layer_legacy_xavier

subroutine init_hidden_layer_legacy_xavier( &
    self, &
    rng &
)

Part of weight initialization as described in PHYSICAL REVIEW B 102, 174102 (2020) and J. Chem. Phys. 153, 164107 (2020). This is not the famous Xavier initialization, but an initialization that is also mentioned in the Xavier / Bengio paper, but it was used for a long time under the name Xavier initialization in RuNNer.

Arguments

Called By

proc~~init_hidden_layer_legacy_xavier~~CalledByGraph proc~init_hidden_layer_legacy_xavier mod_dense_layer::DenseLayer%init_hidden_layer_legacy_xavier proc~init_parameter_hidden_layer mod_dense_layer::DenseLayer%init_parameter_hidden_layer proc~init_parameter_hidden_layer->proc~init_hidden_layer_legacy_xavier proc~init_parameter_last_layer mod_dense_layer::DenseLayer%init_parameter_last_layer proc~init_parameter_last_layer->proc~init_hidden_layer_legacy_xavier proc~init_parameters mod_neural_network::NeuralNetwork%init_parameters proc~init_parameters->proc~init_parameter_hidden_layer proc~init_parameters->proc~init_parameter_last_layer proc~initialize_parameters_hdnn mod_hdnn::HDNN%initialize_parameters_hdnn proc~initialize_parameters_hdnn->proc~init_parameters

subroutine init_hidden_layer_lecun_normal

subroutine init_hidden_layer_lecun_normal( &
    self, &
    rng &
)

LeCun normal initialization. Very similar to the original legacy_xavier initialization. We could think of getting rid of legacy_xavier for this (more correct) version. Standard deviations of the normal distribution are adapted to yield approximately unit variance at all nodes.

Arguments

Called By

proc~~init_hidden_layer_lecun_normal~~CalledByGraph proc~init_hidden_layer_lecun_normal mod_dense_layer::DenseLayer%init_hidden_layer_lecun_normal proc~init_parameter_hidden_layer mod_dense_layer::DenseLayer%init_parameter_hidden_layer proc~init_parameter_hidden_layer->proc~init_hidden_layer_lecun_normal proc~init_parameter_last_layer mod_dense_layer::DenseLayer%init_parameter_last_layer proc~init_parameter_last_layer->proc~init_hidden_layer_lecun_normal proc~init_parameters mod_neural_network::NeuralNetwork%init_parameters proc~init_parameters->proc~init_parameter_hidden_layer proc~init_parameters->proc~init_parameter_last_layer proc~initialize_parameters_hdnn mod_hdnn::HDNN%initialize_parameters_hdnn proc~initialize_parameters_hdnn->proc~init_parameters

subroutine init_hidden_layer_lecun_uniform

subroutine init_hidden_layer_lecun_uniform( &
    self, &
    rng &
)

The sqrt(3) is to give unit variance and is not dependent on the type of activation function that is used.

Arguments

Called By

proc~~init_hidden_layer_lecun_uniform~~CalledByGraph proc~init_hidden_layer_lecun_uniform mod_dense_layer::DenseLayer%init_hidden_layer_lecun_uniform proc~init_parameter_hidden_layer mod_dense_layer::DenseLayer%init_parameter_hidden_layer proc~init_parameter_hidden_layer->proc~init_hidden_layer_lecun_uniform proc~init_parameter_last_layer mod_dense_layer::DenseLayer%init_parameter_last_layer proc~init_parameter_last_layer->proc~init_hidden_layer_lecun_uniform proc~init_parameters mod_neural_network::NeuralNetwork%init_parameters proc~init_parameters->proc~init_parameter_hidden_layer proc~init_parameters->proc~init_parameter_last_layer proc~initialize_parameters_hdnn mod_hdnn::HDNN%initialize_parameters_hdnn proc~initialize_parameters_hdnn->proc~init_parameters

subroutine init_hidden_layer_xavier

subroutine init_hidden_layer_xavier( &
    self, &
    rng &
)

Real famous Xavier initialization as described by Glorot / Bengio in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:249-256, 2010. Note that for the complete Xavier initialization also the output layers weights have to be initialized with Xavier.

Arguments

Called By

proc~~init_hidden_layer_xavier~~CalledByGraph proc~init_hidden_layer_xavier mod_dense_layer::DenseLayer%init_hidden_layer_xavier proc~init_hidden_layer_xavier_modified mod_dense_layer::DenseLayer%init_hidden_layer_xavier_modified proc~init_hidden_layer_xavier_modified->proc~init_hidden_layer_xavier proc~init_parameter_hidden_layer mod_dense_layer::DenseLayer%init_parameter_hidden_layer proc~init_parameter_hidden_layer->proc~init_hidden_layer_xavier proc~init_parameter_hidden_layer->proc~init_hidden_layer_xavier_modified proc~init_parameter_last_layer mod_dense_layer::DenseLayer%init_parameter_last_layer proc~init_parameter_last_layer->proc~init_hidden_layer_xavier proc~init_parameter_last_layer->proc~init_hidden_layer_xavier_modified proc~init_parameters mod_neural_network::NeuralNetwork%init_parameters proc~init_parameters->proc~init_parameter_hidden_layer proc~init_parameters->proc~init_parameter_last_layer proc~initialize_parameters_hdnn mod_hdnn::HDNN%initialize_parameters_hdnn proc~initialize_parameters_hdnn->proc~init_parameters

subroutine init_hidden_layer_xavier_modified

subroutine init_hidden_layer_xavier_modified( &
    self, &
    rng &
)

Modification as described in J.Chem. Theory Comput. 2023, 19,3509−3525.

Arguments

Called By

proc~~init_hidden_layer_xavier_modified~~CalledByGraph proc~init_hidden_layer_xavier_modified mod_dense_layer::DenseLayer%init_hidden_layer_xavier_modified proc~init_parameter_hidden_layer mod_dense_layer::DenseLayer%init_parameter_hidden_layer proc~init_parameter_hidden_layer->proc~init_hidden_layer_xavier_modified proc~init_parameter_last_layer mod_dense_layer::DenseLayer%init_parameter_last_layer proc~init_parameter_last_layer->proc~init_hidden_layer_xavier_modified proc~init_parameters mod_neural_network::NeuralNetwork%init_parameters proc~init_parameters->proc~init_parameter_hidden_layer proc~init_parameters->proc~init_parameter_last_layer proc~initialize_parameters_hdnn mod_hdnn::HDNN%initialize_parameters_hdnn proc~initialize_parameters_hdnn->proc~init_parameters

subroutine init_hidden_layer_nguyen_widrow

subroutine init_hidden_layer_nguyen_widrow( &
    self, &
    rng &
)

Original Nguyen-Widrow initialization scheme

This is the original Nguyen-Widrow initialization scheme as proposed in the literature (D. Nguyen, B. Widrow in 1990 IJCNN international joint conference on neural networks, IEEE, 1990, pp. 21–26.)

Caveat: - they only applied it to the first (only) hidden layer. - they only investigated sigmoid activation functions. - assumes that the input layer gives values in the range [-1, 1] this is not the case in RuNNer even if the features maps are scaled and centered since this does not lead to a uniform distribution in the range [-1, 1].

Arguments

Called By

proc~~init_hidden_layer_nguyen_widrow~~CalledByGraph proc~init_hidden_layer_nguyen_widrow mod_dense_layer::DenseLayer%init_hidden_layer_nguyen_widrow proc~init_hidden_layer_nguyen_widrow_runner1 mod_dense_layer::DenseLayer%init_hidden_layer_nguyen_widrow_runner1 proc~init_hidden_layer_nguyen_widrow_runner1->proc~init_hidden_layer_nguyen_widrow proc~init_parameter_hidden_layer mod_dense_layer::DenseLayer%init_parameter_hidden_layer proc~init_parameter_hidden_layer->proc~init_hidden_layer_nguyen_widrow proc~init_parameter_hidden_layer->proc~init_hidden_layer_nguyen_widrow_runner1 proc~init_parameters mod_neural_network::NeuralNetwork%init_parameters proc~init_parameters->proc~init_parameter_hidden_layer proc~initialize_parameters_hdnn mod_hdnn::HDNN%initialize_parameters_hdnn proc~initialize_parameters_hdnn->proc~init_parameters

subroutine init_hidden_layer_nguyen_widrow_runner1

subroutine init_hidden_layer_nguyen_widrow_runner1( &
    self, &
    rng &
)

Modified Nguyen-Widrow initialization scheme similar to RuNNer 1

This is not exactly the same scheme that was implemented in RuNNer 1. RuNNer 1 applied Nguyen Widrow schme only to the first two hidden layers. The algorithm tries to account for a diffrent range of activation functions by applying a scaling factor. However, RuNNer 1 scaled ALL weights and biases of the whole network by a factor of 2.0 if the activation function of the first node (!) of the FIRST hidden layer was a hyperbolic tangent function. We check each layer individually and apply the scaling factor only to the biases and weights, if the activation function of THIS layer is a hyperbolic tangent function. RuNNer 1 also allowed to set the min/max weights to other values than -1 and 1 even if, we forbid this here since it is not part of the original scheme and was rarly (if ever) used in RuNNer 1. RuNNer 1 also applied a reinitialized the weights from -0.5 to 0.5 to the output layer if Nguyen Widrow was applied. To do this, call init_minus05_to_05 on the last layer.

Arguments

Called By

proc~~init_hidden_layer_nguyen_widrow_runner1~~CalledByGraph proc~init_hidden_layer_nguyen_widrow_runner1 mod_dense_layer::DenseLayer%init_hidden_layer_nguyen_widrow_runner1 proc~init_parameter_hidden_layer mod_dense_layer::DenseLayer%init_parameter_hidden_layer proc~init_parameter_hidden_layer->proc~init_hidden_layer_nguyen_widrow_runner1 proc~init_parameters mod_neural_network::NeuralNetwork%init_parameters proc~init_parameters->proc~init_parameter_hidden_layer proc~initialize_parameters_hdnn mod_hdnn::HDNN%initialize_parameters_hdnn proc~initialize_parameters_hdnn->proc~init_parameters

subroutine init_uniform_minus05_to_05

subroutine init_uniform_minus05_to_05( &
    self, &
    rng &
)

Initialize the weights from -0.5 to 0.5.

Wrapper around initialize_array. The same logical was applied to the last layer in RuNNer 1 if Nguyne Widrow was set. So we put it in it's own routine so that we can call it conveniently.

Arguments

Called By

proc~~init_uniform_minus05_to_05~~CalledByGraph proc~init_uniform_minus05_to_05 mod_dense_layer::DenseLayer%init_uniform_minus05_to_05 proc~init_last_layer_eckhoff mod_dense_layer::DenseLayer%init_last_layer_eckhoff proc~init_last_layer_eckhoff->proc~init_uniform_minus05_to_05 proc~init_parameter_last_layer mod_dense_layer::DenseLayer%init_parameter_last_layer proc~init_parameter_last_layer->proc~init_uniform_minus05_to_05 proc~init_parameter_last_layer->proc~init_last_layer_eckhoff proc~init_parameters mod_neural_network::NeuralNetwork%init_parameters proc~init_parameters->proc~init_parameter_last_layer proc~initialize_parameters_hdnn mod_hdnn::HDNN%initialize_parameters_hdnn proc~initialize_parameters_hdnn->proc~init_parameters

subroutine init_parameter_last_layer

subroutine init_parameter_last_layer( &
    self, &
    init_method, &
    rng, &
    min_weight, &
    max_weight, &
    target_mean, &
    target_range, &
    target_std &
)

Wrapper around the actual parameter initialization routines.

See the documentation of the actual routines for detailed information.

Arguments

  • self : class(DenseLayer) [inout]
  • init_method : character [in]– The initialization method to be used. Can be one of: - 'eckhoff': As described in J. Chem. Phys. 153, 164107 (2020) - 'eckhoff_modified': As described in J. Chem. Phys. 153, 164107 (2020) - 'uniform': Draw values from a uniform distribution. - 'normal' or 'gaussian': Draw values from a normal distribution. - 'nw_last_layer_runner1', 'uniform05' or 'nw_last_layer_legacy': Uniform initialization from -0.5 to 0.5 as it was done in RuNNer 1 if Nguyen-Widrow was applied. - 'zero': Initialize all weights and biases to zero See their respective documentation for more information. For all other values an error will be thrown.
  • rng : class(RandomNumberGenerator) [inout]– The random number generator to be used for initialization.
  • min_weight : real(DP) [in]– The minimum value for the weights. Only used for 'uniform', 'normal', and 'gaussian' initialization.
  • max_weight : real(DP) [in]– The maximum value for the weights. Only used for 'uniform', 'normal', and 'gaussian' initialization.
  • target_mean : real(DP) [in]– The target mean value for the output of the layer. Only used for 'eckhoff' and 'eckhoff_modified' initialization.
  • target_range : real(DP) [in]– The target range for the output of the layer. Only used for 'eckhoff' initialization.
  • target_std : real(DP) [in]– The target standard deviation for the output of the layer. Only used for 'eckhoff_modified' initialization.

Called By

proc~~init_parameter_last_layer~~CalledByGraph proc~init_parameter_last_layer mod_dense_layer::DenseLayer%init_parameter_last_layer proc~init_parameters mod_neural_network::NeuralNetwork%init_parameters proc~init_parameters->proc~init_parameter_last_layer proc~initialize_parameters_hdnn mod_hdnn::HDNN%initialize_parameters_hdnn proc~initialize_parameters_hdnn->proc~init_parameters

subroutine init_last_layer_eckhoff

subroutine init_last_layer_eckhoff( &
    self, &
    rng, &
    target_mean, &
    target_range &
)

Part of weight initialization as described in PHYSICAL REVIEW B 102, 174102 (2020) and J. Chem. Phys. 153, 164107 (2020).

Arguments

Called By

proc~~init_last_layer_eckhoff~~CalledByGraph proc~init_last_layer_eckhoff mod_dense_layer::DenseLayer%init_last_layer_eckhoff proc~init_parameter_last_layer mod_dense_layer::DenseLayer%init_parameter_last_layer proc~init_parameter_last_layer->proc~init_last_layer_eckhoff proc~init_parameters mod_neural_network::NeuralNetwork%init_parameters proc~init_parameters->proc~init_parameter_last_layer proc~initialize_parameters_hdnn mod_hdnn::HDNN%initialize_parameters_hdnn proc~initialize_parameters_hdnn->proc~init_parameters

subroutine init_last_layer_eckhoff_modified

subroutine init_last_layer_eckhoff_modified( &
    self, &
    rng, &
    target_mean, &
    target_std &
)

Arguments

Called By

proc~~init_last_layer_eckhoff_modified~~CalledByGraph proc~init_last_layer_eckhoff_modified mod_dense_layer::DenseLayer%init_last_layer_eckhoff_modified proc~init_parameter_last_layer mod_dense_layer::DenseLayer%init_parameter_last_layer proc~init_parameter_last_layer->proc~init_last_layer_eckhoff_modified proc~init_parameters mod_neural_network::NeuralNetwork%init_parameters proc~init_parameters->proc~init_parameter_last_layer proc~initialize_parameters_hdnn mod_hdnn::HDNN%initialize_parameters_hdnn proc~initialize_parameters_hdnn->proc~init_parameters

subroutine init_last_layer_precondition

subroutine init_last_layer_precondition( &
    self, &
    target_mean, &
    prediction_mean, &
    target_std, &
    prediction_std &
)

Precondition weights and bias values.

Arguments

  • self : class(DenseLayer) [inout]
  • target_mean : real(DP) [in]
  • prediction_mean : real(DP) [in]
  • target_std : real(DP) [in]
  • prediction_std : real(DP) [in]

Called By

proc~~init_last_layer_precondition~~CalledByGraph proc~init_last_layer_precondition mod_dense_layer::DenseLayer%init_last_layer_precondition proc~precondition_parameters_neuralnetwork mod_neural_network::NeuralNetwork%precondition_parameters_neuralnetwork proc~precondition_parameters_neuralnetwork->proc~init_last_layer_precondition proc~precondition_parameters_hdnn mod_hdnn::HDNN%precondition_parameters_hdnn proc~precondition_parameters_hdnn->proc~precondition_parameters_neuralnetwork