Operator Reference

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create_dl_layer_denseT_create_dl_layer_denseCreateDlLayerDenseCreateDlLayerDensecreate_dl_layer_dense (Operator)

create_dl_layer_denseT_create_dl_layer_denseCreateDlLayerDenseCreateDlLayerDensecreate_dl_layer_dense — Create a dense layer.

Signature

Herror T_create_dl_layer_dense(const Htuple DLLayerInput, const Htuple LayerName, const Htuple NumOut, const Htuple GenParamName, const Htuple GenParamValue, Htuple* DLLayerDense)

void CreateDlLayerDense(const HTuple& DLLayerInput, const HTuple& LayerName, const HTuple& NumOut, const HTuple& GenParamName, const HTuple& GenParamValue, HTuple* DLLayerDense)

HDlLayer HDlLayer::CreateDlLayerDense(const HString& LayerName, Hlong NumOut, const HTuple& GenParamName, const HTuple& GenParamValue) const

HDlLayer HDlLayer::CreateDlLayerDense(const HString& LayerName, Hlong NumOut, const HString& GenParamName, const HString& GenParamValue) const

HDlLayer HDlLayer::CreateDlLayerDense(const char* LayerName, Hlong NumOut, const char* GenParamName, const char* GenParamValue) const

HDlLayer HDlLayer::CreateDlLayerDense(const wchar_t* LayerName, Hlong NumOut, const wchar_t* GenParamName, const wchar_t* GenParamValue) const   ( Windows only)

static void HOperatorSet.CreateDlLayerDense(HTuple DLLayerInput, HTuple layerName, HTuple numOut, HTuple genParamName, HTuple genParamValue, out HTuple DLLayerDense)

HDlLayer HDlLayer.CreateDlLayerDense(string layerName, int numOut, HTuple genParamName, HTuple genParamValue)

HDlLayer HDlLayer.CreateDlLayerDense(string layerName, int numOut, string genParamName, string genParamValue)

def create_dl_layer_dense(dllayer_input: HHandle, layer_name: str, num_out: int, gen_param_name: MaybeSequence[str], gen_param_value: MaybeSequence[Union[int, float, str]]) -> HHandle

Description

The operator create_dl_layer_densecreate_dl_layer_denseCreateDlLayerDenseCreateDlLayerDensecreate_dl_layer_dense creates a dense or fully connected layer (sometimes also called gemm) with NumOutNumOutNumOutnumOutnum_out output neurons whose handle is returned in DLLayerDenseDLLayerDenseDLLayerDenseDLLayerDensedllayer_dense.

The parameter DLLayerInputDLLayerInputDLLayerInputDLLayerInputdllayer_input determines the feeding input layer and expects the layer handle as value.

The parameter LayerNameLayerNameLayerNamelayerNamelayer_name sets an individual layer name. Note that if creating a model using create_dl_modelcreate_dl_modelCreateDlModelCreateDlModelcreate_dl_model each layer of the created network must have a unique name.

The following generic parameters GenParamNameGenParamNameGenParamNamegenParamNamegen_param_name and the corresponding values GenParamValueGenParamValueGenParamValuegenParamValuegen_param_value are supported:

'bias_filler'"bias_filler""bias_filler""bias_filler""bias_filler":

See create_dl_layer_convolutioncreate_dl_layer_convolutionCreateDlLayerConvolutionCreateDlLayerConvolutioncreate_dl_layer_convolution for a detailed explanation of this parameter and its values.

List of values: 'xavier'"xavier""xavier""xavier""xavier", 'msra'"msra""msra""msra""msra", 'const'"const""const""const""const".

Default: 'const'"const""const""const""const"

'bias_filler_const_val'"bias_filler_const_val""bias_filler_const_val""bias_filler_const_val""bias_filler_const_val":

Constant value if 'bias_filler'"bias_filler""bias_filler""bias_filler""bias_filler" = 'const'"const""const""const""const".

Default: 0

'bias_filler_variance_norm'"bias_filler_variance_norm""bias_filler_variance_norm""bias_filler_variance_norm""bias_filler_variance_norm":

See create_dl_layer_convolutioncreate_dl_layer_convolutionCreateDlLayerConvolutionCreateDlLayerConvolutioncreate_dl_layer_convolution for a detailed explanation of this parameter and its values.

List of values: 'norm_out'"norm_out""norm_out""norm_out""norm_out", 'norm_in'"norm_in""norm_in""norm_in""norm_in", 'norm_average'"norm_average""norm_average""norm_average""norm_average", or constant value (in combination with 'bias_filler'"bias_filler""bias_filler""bias_filler""bias_filler" = 'msra'"msra""msra""msra""msra").

Default: 'norm_out'"norm_out""norm_out""norm_out""norm_out"

'bias_term'"bias_term""bias_term""bias_term""bias_term":

Determines whether the created dense layer has a bias term ('true'"true""true""true""true") or not ('false'"false""false""false""false").

Default: 'true'"true""true""true""true"

'is_inference_output'"is_inference_output""is_inference_output""is_inference_output""is_inference_output":

Determines whether apply_dl_modelapply_dl_modelApplyDlModelApplyDlModelapply_dl_model will include the output of this layer in the dictionary DLResultBatchDLResultBatchDLResultBatchDLResultBatchdlresult_batch even without specifying this layer in OutputsOutputsOutputsoutputsoutputs ('true'"true""true""true""true") or not ('false'"false""false""false""false").

Default: 'false'"false""false""false""false"

'learning_rate_multiplier'"learning_rate_multiplier""learning_rate_multiplier""learning_rate_multiplier""learning_rate_multiplier":

Multiplier for the learning rate for this layer that is used during training. If 'learning_rate_multiplier'"learning_rate_multiplier""learning_rate_multiplier""learning_rate_multiplier""learning_rate_multiplier" is set to 0.0, the layer is skipped during training.

Default: 1.0

'learning_rate_multiplier_bias'"learning_rate_multiplier_bias""learning_rate_multiplier_bias""learning_rate_multiplier_bias""learning_rate_multiplier_bias":

Multiplier for the learning rate of the bias term. The total bias learning rate is the product of 'learning_rate_multiplier_bias'"learning_rate_multiplier_bias""learning_rate_multiplier_bias""learning_rate_multiplier_bias""learning_rate_multiplier_bias" and 'learning_rate_multiplier'"learning_rate_multiplier""learning_rate_multiplier""learning_rate_multiplier""learning_rate_multiplier".

Default: 1.0

'weight_filler'"weight_filler""weight_filler""weight_filler""weight_filler":

See create_dl_layer_convolutioncreate_dl_layer_convolutionCreateDlLayerConvolutionCreateDlLayerConvolutioncreate_dl_layer_convolution for a detailed explanation of this parameter and its values.

List of values: 'xavier'"xavier""xavier""xavier""xavier", 'msra'"msra""msra""msra""msra", 'const'"const""const""const""const".

Default: 'xavier'"xavier""xavier""xavier""xavier"

'weight_filler_const_val'"weight_filler_const_val""weight_filler_const_val""weight_filler_const_val""weight_filler_const_val":

See create_dl_layer_convolutioncreate_dl_layer_convolutionCreateDlLayerConvolutionCreateDlLayerConvolutioncreate_dl_layer_convolution for a detailed explanation of this parameter and its values.

Default: 0.5

'weight_filler_variance_norm'"weight_filler_variance_norm""weight_filler_variance_norm""weight_filler_variance_norm""weight_filler_variance_norm":

See create_dl_layer_convolutioncreate_dl_layer_convolutionCreateDlLayerConvolutionCreateDlLayerConvolutioncreate_dl_layer_convolution for a detailed explanation of this parameter and its values.

List of values: 'norm_in'"norm_in""norm_in""norm_in""norm_in", 'norm_out'"norm_out""norm_out""norm_out""norm_out", 'norm_average'"norm_average""norm_average""norm_average""norm_average", or constant value (in combination with 'bias_filler'"bias_filler""bias_filler""bias_filler""bias_filler" = 'msra'"msra""msra""msra""msra").

Default: 'norm_in'"norm_in""norm_in""norm_in""norm_in"

Certain parameters of layers created using create_dl_layer_densecreate_dl_layer_denseCreateDlLayerDenseCreateDlLayerDensecreate_dl_layer_dense can be set and retrieved using further operators. The following tables give an overview, which parameters can be set using set_dl_model_layer_paramset_dl_model_layer_paramSetDlModelLayerParamSetDlModelLayerParamset_dl_model_layer_param and which ones can be retrieved using get_dl_model_layer_paramget_dl_model_layer_paramGetDlModelLayerParamGetDlModelLayerParamget_dl_model_layer_param or get_dl_layer_paramget_dl_layer_paramGetDlLayerParamGetDlLayerParamget_dl_layer_param. Note, the operators set_dl_model_layer_paramset_dl_model_layer_paramSetDlModelLayerParamSetDlModelLayerParamset_dl_model_layer_param and get_dl_model_layer_paramget_dl_model_layer_paramGetDlModelLayerParamGetDlModelLayerParamget_dl_model_layer_param require a model created by create_dl_modelcreate_dl_modelCreateDlModelCreateDlModelcreate_dl_model.

Layer Parameters set get
'input_layer'"input_layer""input_layer""input_layer""input_layer" (DLLayerInputDLLayerInputDLLayerInputDLLayerInputdllayer_input) x
'name'"name""name""name""name" (LayerNameLayerNameLayerNamelayerNamelayer_name) x x
'neurons_in'"neurons_in""neurons_in""neurons_in""neurons_in" x
'neurons_out'"neurons_out""neurons_out""neurons_out""neurons_out" (NumOutNumOutNumOutnumOutnum_out) x
'output_layer'"output_layer""output_layer""output_layer""output_layer" (DLLayerDenseDLLayerDenseDLLayerDenseDLLayerDensedllayer_dense) x
'shape'"shape""shape""shape""shape" x
'type'"type""type""type""type" x
Generic Layer Parameters set get
'bias_filler'"bias_filler""bias_filler""bias_filler""bias_filler" x x
'bias_filler_const_val'"bias_filler_const_val""bias_filler_const_val""bias_filler_const_val""bias_filler_const_val" x x
'bias_filler_variance_norm'"bias_filler_variance_norm""bias_filler_variance_norm""bias_filler_variance_norm""bias_filler_variance_norm" x x
'bias_term'"bias_term""bias_term""bias_term""bias_term" x
'is_inference_output'"is_inference_output""is_inference_output""is_inference_output""is_inference_output" x x
'learning_rate_multiplier'"learning_rate_multiplier""learning_rate_multiplier""learning_rate_multiplier""learning_rate_multiplier" x x
'learning_rate_multiplier_bias'"learning_rate_multiplier_bias""learning_rate_multiplier_bias""learning_rate_multiplier_bias""learning_rate_multiplier_bias" x x
'num_trainable_params'"num_trainable_params""num_trainable_params""num_trainable_params""num_trainable_params" x
'weight_filler'"weight_filler""weight_filler""weight_filler""weight_filler" x x
'weight_filler_const_val'"weight_filler_const_val""weight_filler_const_val""weight_filler_const_val""weight_filler_const_val" x x
'weight_filler_variance_norm'"weight_filler_variance_norm""weight_filler_variance_norm""weight_filler_variance_norm""weight_filler_variance_norm" x x

Execution Information

  • Multithreading type: reentrant (runs in parallel with non-exclusive operators).
  • Multithreading scope: global (may be called from any thread).
  • Processed without parallelization.

Parameters

DLLayerInputDLLayerInputDLLayerInputDLLayerInputdllayer_input (input_control)  dl_layer → HDlLayer, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

Feeding layer.

LayerNameLayerNameLayerNamelayerNamelayer_name (input_control)  string → HTuplestrHTupleHtuple (string) (string) (HString) (char*)

Name of the output layer.

NumOutNumOutNumOutnumOutnum_out (input_control)  number → HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Number of output neurons.

Default: 100

GenParamNameGenParamNameGenParamNamegenParamNamegen_param_name (input_control)  attribute.name(-array) → HTupleMaybeSequence[str]HTupleHtuple (string) (string) (HString) (char*)

Generic input parameter names.

Default: []

List of values: 'bias_filler'"bias_filler""bias_filler""bias_filler""bias_filler", 'bias_filler_const_val'"bias_filler_const_val""bias_filler_const_val""bias_filler_const_val""bias_filler_const_val", 'bias_filler_variance_norm'"bias_filler_variance_norm""bias_filler_variance_norm""bias_filler_variance_norm""bias_filler_variance_norm", 'bias_term'"bias_term""bias_term""bias_term""bias_term", 'is_inference_output'"is_inference_output""is_inference_output""is_inference_output""is_inference_output", 'learning_rate_multiplier'"learning_rate_multiplier""learning_rate_multiplier""learning_rate_multiplier""learning_rate_multiplier", 'learning_rate_multiplier_bias'"learning_rate_multiplier_bias""learning_rate_multiplier_bias""learning_rate_multiplier_bias""learning_rate_multiplier_bias", 'weight_filler'"weight_filler""weight_filler""weight_filler""weight_filler", 'weight_filler_const_val'"weight_filler_const_val""weight_filler_const_val""weight_filler_const_val""weight_filler_const_val", 'weight_filler_variance_norm'"weight_filler_variance_norm""weight_filler_variance_norm""weight_filler_variance_norm""weight_filler_variance_norm"

GenParamValueGenParamValueGenParamValuegenParamValuegen_param_value (input_control)  attribute.value(-array) → HTupleMaybeSequence[Union[int, float, str]]HTupleHtuple (string / integer / real) (string / int / long / double) (HString / Hlong / double) (char* / Hlong / double)

Generic input parameter values.

Default: []

Suggested values: 'xavier'"xavier""xavier""xavier""xavier", 'msra'"msra""msra""msra""msra", 'const'"const""const""const""const", 'nearest_neighbor'"nearest_neighbor""nearest_neighbor""nearest_neighbor""nearest_neighbor", 'bilinear'"bilinear""bilinear""bilinear""bilinear", 'norm_in'"norm_in""norm_in""norm_in""norm_in", 'norm_out'"norm_out""norm_out""norm_out""norm_out", 'norm_average'"norm_average""norm_average""norm_average""norm_average", 'true'"true""true""true""true", 'false'"false""false""false""false", 1.0, 0.9, 0.0

DLLayerDenseDLLayerDenseDLLayerDenseDLLayerDensedllayer_dense (output_control)  dl_layer → HDlLayer, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

Dense layer.

Module

Deep Learning Professional

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