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- Dense
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- Dense
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- Dense
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- Dense
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- PReLU 2D
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- Dense
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- PReLU 2D
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- Dense
- Embedding
- AdditiveAttention
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- Conv1D
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- Conv1DTranspose
- Conv2DTranspose
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- DepthwiseConv2D
- SeparableConv1D
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- BatchNormalization
- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
- LSTM
- RNN (GRU)
- RNN (LSTM)
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- Dense
- Embedding
- AdditiveAttention
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- Conv1D
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- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
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- BatchNormalization
- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
- LSTM
- RNN (GRU)
- RNN (LSTM)
- RNN (SimpleRNN)
- SimpleRNN
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- Add
- AdditiveAttention
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- AlphaDropout
- AvgPool1D
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- Bidirectional
- Conv1D
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- Conv2DTranspose
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- Dense
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- Dropout
- Embedding
- Flatten
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- GlobalAvgPool1D
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- GlobalMaxPool1D
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- GRU
- LayerNormalization
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- Permute3D
- Reshape
- RNN
- SeparableConv1D
- SeparableConv2D
- SimpleRNN
- SpatialDropout
- UpSampling1D
- UpSampling2D
- UpSampling3D
- ZeroPadding1D
- ZeroPadding2D
- ZeroPadding3D
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- Resume
- Accuracy
- BinaryAccuracy
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- SparseCategoricalAccuracy
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- Specificity
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- SquaredHinge
- Sum
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- TrueNegatives
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- Resume
- Constant
- GlorotNormal
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- HeNormal
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- Identity
- LecunNormal
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- RandomNormal
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Updated
Advanced user’s Guide
In this section for advanced users, we will design, summarize, execute, train, dimensionize data and setup parameters more advanced design models.
How to design a model ?
This section will quick guide you to show the multi inputs/outputs model design system of the HAIBAL deep learning toolkit for LabVIEW.
Simple multiple inputs / outputs model design guide
This section explains how to design a simple multi inputs / outputs model (one branch).
Advanced multi-branch model with multiple inputs / outputs design guide
This section explains how to design advanced models with multiple inputs/outputs (multiple branches).
How to manage multi data ?
This section explains how the multi input data manager system is working
Multi inputs data guide
This section explains how to inject data with a multi inputs model layers
Multi outputs data guide
This section explains how to recover output data with a multi outputs model layers
Table of Contents