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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
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- 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
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- 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 Platform
Description
Sets the type of execution of the model and how it works. If you have a CUDA-compatible graphics card you can perform the calculation with your GPU.
Do not forget to install CUDA only with the HAIBAL CUDA installer before using this function to avoid any error (CUDA installation guide).

Input parameters
Model in : model architecture.
Memory Exec : cluster
Mode : enum, mode of operation for internal platform memory management. This involved only GPU device.
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- FreePtr : Dynamic create and free memory access after internal utilisation in individual forward/backward layer process.
- AvailablePtr : Creates memory without dynamic free process to permit reusability of it.
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These both way of memory management also require to use this function at the end of the whole process to free all GPU platform allocated memory.

Device : enum, device where the calculation will be made.
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 Platform” 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 function “Set Platform” to set the type of execution of the model and how it works.
3 – Get Function
We use the function “Get Platform” to get the type of execution of the model and how it works.