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Quick start
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API
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- Dense
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- AdditiveAttention
- Attention
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- Conv1D
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- Embedding
- BatchNormalization
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- Bidirectional
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- Dense
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- AdditiveAttention
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- Conv1D
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- Dense
- AdditiveAttention
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- Dense
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- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
- LSTM
- RNN (GRU)
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- Dense
- Embedding
- AdditiveAttention
- Attention
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- Conv1D
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- Conv1DTranspose
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- DepthwiseConv2D
- SeparableConv1D
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- BatchNormalization
- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
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- RNN (GRU)
- RNN (LSTM)
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- SimpleRNN
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- Dense
- Embedding
- AdditiveAttention
- Attention
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- Conv1D
- Conv2D
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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)
- RNN (SimpleRNN)
- SimpleRNN
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- Dense
- Embedding
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
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- Conv3DTranspose
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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
- AlphaDropout
- Attention
- Average
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- Cropping1D
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- Dense
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- Dropout
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- Flatten
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- GlobalMaxPool2D
- GlobalMaxPool3D
- GRU
- Input
- LayerNormalization
- LSTM
- MaxPool1D
- MaxPool2D
- MaxPool3D
- MultiHeadAttention
- Multiply
- Permute3D
- Reshape
- RNN
- SeparableConv1D
- SeparableConv2D
- SimpleRNN
- SpatialDropout
- Substract
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- UpSampling1D
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- AlphaDropout
- AvgPool1D
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- Bidirectional
- Conv1D
- Conv1DTranspose
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- Conv2DTranspose
- Conv3D
- Conv3DTranspose
- Cropping1D
- Cropping2D
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- DepthwiseConv2D
- Dropout
- Embedding
- Flatten
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- GlobalMaxPool1D
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- GRU
- LayerNormalization
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- Permute3D
- Reshape
- RNN
- SeparableConv1D
- SeparableConv2D
- SimpleRNN
- SpatialDropout
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- UpSampling3D
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- Resume
- Accuracy
- BinaryAccuracy
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- CosineSimilarity
- FalseNegatives
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- MeanTensor
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- Poisson
- Precision
- PrecisionAtRecall
- Recall
- RecallAtPrecision
- RootMeanSquaredError
- SensitivityAtSpecificity
- SparseCategoricalAccuracy
- SparseCategoricalCrossentropy
- SparseTopKCategoricalAccuracy
- Specificity
- SpecificityAtSensitivity
- SquaredHinge
- Sum
- TopKCategoricalAccuracy
- TrueNegatives
- TruePositives
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- Resume
- Constant
- GlorotNormal
- GlorotUniform
- HeNormal
- HeUniform
- Identity
- LecunNormal
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- Ones
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- RandomNormal
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AdditiveAttention
Description
Adds the weight of the AdditiveAttention layer to the weights table. Type : polymorphic.
Input parameters
Weights in : array
index : integer, index of layer.
weights : variant, weights values.
![weights_in_by_index](https://haibal.com/wp-content/uploads/2023/01/weights_in_by_index.png)
index : integer, index of layer.
scale : array, 1D values. scale = query[2] = value[2] = key[2].
Output parameters
Weights out : array
index : integer, index of layer.
weights : variant, weights values.
![weights_out_by_index](https://haibal.com/wp-content/uploads/2023/01/weights_out_by_index.png)
Dimension
- scale = query[2] = value[2] = key[2]
The size of scale depends on the size of the query, value and key entries in the AdditiveAttention layer.
For example, if query has a size of [batch_size = 5, Tq = 3, dim = 1], value a size of [batch_size = 10, Tv = 4, dim = 1] and key a size of [batch_size = 8, Tv = 6, dim = 1] then the size of scale is [dim = 1].
Another example, if query has a size of [batch_size = 10, Tq = 9, dim = 5], value a size of [batch_size = 15, Tv = 10, dim = 5] and key a size of [batch_size = 9, Tv = 7, dim = 5] then the size of scale is [dim = 5].
query, value and key will always have the same value at index 2 of their size, which will be the size of scale.
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).