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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
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- Get All Layer Params
- Get Layer Params by index
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- Warning Param
- Get All Input Layer Shape
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- Get Init Weight by index
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- Get All Weights
- Get Weights by index
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- Get Weights Shape by index
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- 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
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- PReLU 2D
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- DepthwiseConv2D
- SeparableConv1D
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- Dense
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- PReLU 2D
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- AdditiveAttention
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- Conv3D
- ConvLSTM1D
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- DepthwiseConv2D
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- Dense
- Embedding
- BatchNormalization
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- Bidirectional
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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
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- Dense
- Embedding
- BatchNormalization
- LayerNormalization
- Bidirectional
- GRU
- LSTM
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- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
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- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- Dense
- Embedding
- BatchNormalization
- LayerNormalization
- Bidirectional
- GRU
- LSTM
- SimpleRNN
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- PReLU 2D
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- PReLU 5D
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- Dense
- Embedding
- BatchNormalization
- LayerNormalization
- Bidirectional
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- Add
- AdditiveAttention
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- Conv1D
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- Conv2D
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- ConvLSTM1D
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- Cropping1D
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- Dense
- DepthwiseConv2D
- Dropout
- Embedding
- Flatten
- GaussianDropout
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- GlobalAvgPool1D
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- GlobalMaxPool1D
- GlobalMaxPool2D
- GlobalMaxPool3D
- GRU
- Input
- LayerNormalization
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- MaxPool1D
- MaxPool2D
- MaxPool3D
- MultiHeadAttention
- Multiply
- Permute3D
- Reshape
- RNN
- SeparableConv1D
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- SimpleRNN
- SatialDropout
- Substract
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- UpSampling1D
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- AlphaDropout
- AvgPool1D
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- 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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Open Log Folder
Description
When an error occurs during the execution of the model it is recorded in a temporary file. The “Open Log Folder” function allows you to open the folder that contains these temporary files.

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).
Opens the folder that contains the errors related to the model

1 – Define Graph
We define the graph with one input, one dense layer and one convolution layer.
2 – Clear Errors
We cause a dimensional error between the dense and convolution layer. The output of the dense layer is incompatible with the input of the convolution layer. Indeed, in output of the dense layer we have data in 2D and in input of the convolution data in 3D.
This error is temporarily recorded in a log file.
We use the “Clear Errors” function of LabVIEW to execute our “Open Log Folder.
3 – Log Folder
We use the “Open Log Folder” function to open the folder that contains the error files related to the execution of the model.