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Quick start
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API
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- Get All Grad
- Get Grad by index
- Get Grad by name
- Get All Store Grad
- Get Store Grad by index
- Get Store Grad by name
- Get All Index/Name
- Get Index by name
- Get Name by index
- Get All "lda_coeff"
- Get "lda_coeff" by index
- Get "lda_coeff" by name
- Get All Layer Params
- Get Layer Params by index
- Get Layer Params by name
- Get All Opti Params
- Get Opti Params by index
- Get Opti Params by name
- Get All Train Status
- Get Train Status by index
- Get Train Status by name
- Get All Loss Type
- Get Model Name
- Get Platform
- Warning Param
- Get All Input Layer Shape
- Get All Output Layer Shape
- Get All Input Shape
- Get Input Shape by index
- Get Input Shape by name
- Get All Output Shape
- Get Output Shape by index
- Get Output Shape by name
- Get All Init Weight
- Get Init Weight by index
- Get Init Weight by name
- Get All Weights
- Get Weights by index
- Get Weights by name
- Get All Weights Shape
- Get Weights Shape by index
- Get Weights Shape by name
- Get All Update Weights
- Get Update Weights by index
- Get Update Weights by name
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- Set All Store Grad
- Set Store Grad by index
- Set Store Grad by name
- Set All "lda_coeff"
- Set "lda_coeff" by index
- Set "lda_coeff" by name
- Set All Opti Params
- Set Opti Params by index
- Set Opti Params by name
- Set All Train Status
- Set Train Status by index
- Set Train Status by name
- Set All Loss Type
- Set Model Name
- Set Platform
- Warning Param
- Set All Update Weights
- Set Update Weights by index
- Set Update Weights by name
- Load All Weights
- Load All Weights Model
- Set All Random Weights
- Set Weights by index
- Set Weights by name
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- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- AdditiveAttention
- Attention
- MutiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- Dense
- Embedding
- BatchNormalization
- LayerNormalization
- Bidirectional
- GRU
- LSTM
- SimpleRNN
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- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- Dense
- Embedding
- BatchNormalization
- LayerNormalization
- Bidirectional
- GRU
- LSTM
- SimpleRNN
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- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- Dense
- Embedding
- BatchNormalization
- LayerNormalization
- Bidirectional
- GRU
- LSTM
- SimpleRNN
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- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- Dense
- Embedding
- BatchNormalization
- LayerNormalization
- Bidirectional
- GRU
- LSTM
- SimpleRNN
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- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- Dense
- Embedding
- BatchNormalization
- LayerNormalization
- Bidirectional
- GRU
- LSTM
- SimpleRNN
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- Add
- AdditiveAttention
- AlphaDropout
- Attention
- Average
- AvgPool1D
- AvgPool2D
- AvgPool3D
- BatchNormalization
- Bidirectional
- Concatenate
- Conv1D
- Conv1DTranspose
- Conv2D
- Conv2DTranspose
- Conv3D
- Conv3DTranspose
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Cropping1D
- Cropping2D
- Cropping3D
- Dense
- DepthwiseConv2D
- Dropout
- Embedding
- Flatten
- GaussianDropout
- GaussianNoise
- GlobalAvgPool1D
- GlobalAvgPool2D
- GlobalAvgPool3D
- GlobalMaxPool1D
- GlobalMaxPool2D
- GlobalMaxPool3D
- GRU
- Input
- LayerNormalization
- LSTM
- MaxPool1D
- MaxPool2D
- MaxPool3D
- MultiHeadAttention
- Multiply
- Permute3D
- Reshape
- RNN
- SeparableConv1D
- SeparableConv2D
- SimpleRNN
- SatialDropout
- Substract
- TimeDistributed
- UpSampling1D
- UpSampling2D
- UpSampling3D
- ZeroPadding1D
- ZeroPadding2D
- ZeroPadding3D
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- AlphaDropout
- AvgPool1D
- AvgPool2D
- AvgPool3D
- BatchNormalization
- Bidirectional
- Conv1D
- Conv1DTranspose
- Conv2D
- Conv2DTranspose
- Conv3D
- Conv3DTranspose
- Cropping1D
- Cropping2D
- Cropping3D
- Dense
- DepthwiseConv2D
- Dropout
- Embedding
- Flatten
- GaussianDropout
- GaussianNoise
- GlobalAvgPool1D
- GlobalAvgPool2D
- GlobalAvgPool3D
- GlobalMaxPool1D
- GlobalMaxPool2D
- GlobalMaxPool3D
- GRU
- LayerNormalization
- LSTM
- MaxPool1D
- MaxPool2D
- MaxPool3D
- Permute3D
- Reshape
- RNN
- SeparableConv1D
- SeparableConv2D
- SimpleRNN
- SpatialDropout
- UpSampling1D
- UpSampling2D
- UpSampling3D
- ZeroPadding1D
- ZeroPadding2D
- ZeroPadding3D
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Set All Random Weights
Description
Creates random weights for all layers in the model.

Input parameters
Model in : model architecture.
Output parameters
Model out : model architecture.
Example
All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install HAIBAL library to run it).
Using the “Set All Random Weights” function

1 – Define Graph
We define the graph with one input and two Dense layers named Dense1 and Dense2.
2 – Set Function
We use the “Set All Random Weights” function to create random weights for all layers which have weights in the model.
3 – Get Function
We use the “Get All Weights” function to get the weights of all layers that have them from the model.
4 – Index weight_array
Since the only layers that have weights are Dense1 and Dense2 we index the array returned by the get function to retrieve their weight.
5 – Convert variant to data
The get function returns the weights in a variant, so we use the “Variant To Data” function of LabVIEW to get the result in an array. For that, we use the polymorph which transmits us directly the typedef of the Dense layer.