create_dl_layer_transposed_convolutionT_create_dl_layer_transposed_convolutionCreateDlLayerTransposedConvolutionCreateDlLayerTransposedConvolutioncreate_dl_layer_transposed_convolution (Operator)

Name

create_dl_layer_transposed_convolutionT_create_dl_layer_transposed_convolutionCreateDlLayerTransposedConvolutionCreateDlLayerTransposedConvolutioncreate_dl_layer_transposed_convolution — Create a transposed convolution layer.

Signature

Description

The operator create_dl_layer_transposed_convolutioncreate_dl_layer_transposed_convolutionCreateDlLayerTransposedConvolutionCreateDlLayerTransposedConvolutionCreateDlLayerTransposedConvolutioncreate_dl_layer_transposed_convolution creates a transposed convolution layer whose handle is returned in DLLayerTransposedConvolutionDLLayerTransposedConvolutionDLLayerTransposedConvolutionDLLayerTransposedConvolutionDLLayerTransposedConvolutiondllayer_transposed_convolution.

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

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

The parameter KernelSizeKernelSizeKernelSizeKernelSizekernelSizekernel_size specifies the filter kernel in the dimensions width and height. So far, only quadratic kernels are supported.

Restriction: This value must be a tuple of length 1.

The parameter StrideStrideStrideStridestridestride determines how the filter is shifted in row and column direction.

Restriction: This value must be a tuple of length 1.

The parameter KernelDepthKernelDepthKernelDepthKernelDepthkernelDepthkernel_depth defines the depth of the output feature maps.

Restriction: This value must be a tuple of length 1.

The parameter GroupsGroupsGroupsGroupsgroupsgroups determines the amount of filter groups. So far, only a single filter group is supported.

Restriction: This value must be a tuple of length 1.

The parameter PaddingPaddingPaddingPaddingpaddingpadding determines the padding, thus how many pixels with value 0 are appended on the border of the processed input image. Supported values are:

• 'half_kernel_size'"half_kernel_size""half_kernel_size""half_kernel_size""half_kernel_size""half_kernel_size": The number of appended pixels depends on the specified KernelSizeKernelSizeKernelSizeKernelSizekernelSizekernel_size. More precisely it is calculated as , where for the padding on the left/right bound the value of KernelSizeKernelSizeKernelSizeKernelSizekernelSizekernel_size in dimension width is regarded and for the padding on the upper/lower bound the value of KernelSizeKernelSizeKernelSizeKernelSizekernelSizekernel_size in height.

• 'none'"none""none""none""none""none": No pixels are appended.

So far, the transposed convolution layer does not support bias terms.

The following generic parameters GenParamNameGenParamNameGenParamNameGenParamNamegenParamNamegen_param_name and the corresponding values GenParamValueGenParamValueGenParamValueGenParamValuegenParamValuegen_param_value are supported:

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

Determines, whether the layer has bias terms. As mentioned above, bias terms are not supported yet, so this value is only retrievable.

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

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

Determines whether apply_dl_modelapply_dl_modelApplyDlModelApplyDlModelApplyDlModelapply_dl_model will include the output of this layer in the dictionary DLResultBatchDLResultBatchDLResultBatchDLResultBatchDLResultBatchdlresult_batch even without specifying this layer in OutputsOutputsOutputsOutputsoutputsoutputs ('true'"true""true""true""true""true") or not ('false'"false""false""false""false""false").

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

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

Learning rate multiplier for this layer that is used during training. If 'learning_rate_multiplier'"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

'num_trainable_params'"num_trainable_params""num_trainable_params""num_trainable_params""num_trainable_params""num_trainable_params":

Number of trainable parameters (weights and biases) of the layer.

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

Defines the mode how the weights are initialized. See create_dl_layer_convolutioncreate_dl_layer_convolutionCreateDlLayerConvolutionCreateDlLayerConvolutionCreateDlLayerConvolutioncreate_dl_layer_convolution for a detailed explanation of this parameter and its values.

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

Default: 'xavier'"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""weight_filler_const_val":

See create_dl_layer_convolutioncreate_dl_layer_convolutionCreateDlLayerConvolutionCreateDlLayerConvolutionCreateDlLayerConvolutioncreate_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""weight_filler_variance_norm":

Value range for 'weight_filler'"weight_filler""weight_filler""weight_filler""weight_filler""weight_filler". See create_dl_layer_convolutioncreate_dl_layer_convolutionCreateDlLayerConvolutionCreateDlLayerConvolutionCreateDlLayerConvolutioncreate_dl_layer_convolution for a detailed explanation of this parameter and its values.

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

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

Certain parameters of layers created using this operator create_dl_layer_transposed_convolutioncreate_dl_layer_transposed_convolutionCreateDlLayerTransposedConvolutionCreateDlLayerTransposedConvolutionCreateDlLayerTransposedConvolutioncreate_dl_layer_transposed_convolution 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_paramSetDlModelLayerParamSetDlModelLayerParamSetDlModelLayerParamset_dl_model_layer_param and which ones can be retrieved using get_dl_model_layer_paramget_dl_model_layer_paramGetDlModelLayerParamGetDlModelLayerParamGetDlModelLayerParamget_dl_model_layer_param or get_dl_layer_paramget_dl_layer_paramGetDlLayerParamGetDlLayerParamGetDlLayerParamget_dl_layer_param. Note, the operators set_dl_model_layer_paramset_dl_model_layer_paramSetDlModelLayerParamSetDlModelLayerParamSetDlModelLayerParamset_dl_model_layer_param and get_dl_model_layer_paramget_dl_model_layer_paramGetDlModelLayerParamGetDlModelLayerParamGetDlModelLayerParamget_dl_model_layer_param require a model created by create_dl_modelcreate_dl_modelCreateDlModelCreateDlModelCreateDlModelcreate_dl_model.

Attention

This layer cannot be run by setting 'runtime'"runtime""runtime""runtime""runtime""runtime" to 'cpu'"cpu""cpu""cpu""cpu""cpu" for training. It is only applicable for training with a GPU by setting 'runtime'"runtime""runtime""runtime""runtime""runtime" to 'gpu'"gpu""gpu""gpu""gpu""gpu".

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

Module

Deep Learning Training