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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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- DepthwiseConv2D
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- PReLU 2D
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- GRU
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- RNN (GRU)
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- Dense
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- BatchNormalization
- LayerNormalization
- PReLU 2D
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- PReLU 5D
- Bidirectional
- GRU
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- RNN (GRU)
- RNN (LSTM)
- RNN (SimpleRNN)
- SimpleRNN
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- Add
- AdditiveAttention
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- AlphaDropout
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- Conv2DTranspose
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- GRU
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- Resume
- Accuracy
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- SparseCategoricalCrossentropy
- SparseTopKCategoricalAccuracy
- Specificity
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- SquaredHinge
- Sum
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- Resume
- Constant
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Updated
Bidirectional
Description
Defines the weights of the Bidirectional layer selected by the index. Type : polymorphic.
Input parameters
Model in : model architecture.
index : integer, index of layer.
layer : variant, cluster from “gru_weights” or “lstm_weights” or “simplernn_weights“.
backward_layer : variant, cluster from “gru_weights” or “lstm_weights” or “simplernn_weights“.
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).
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