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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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- Conv1DTranspose
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- Embedding
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- Dense
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- Conv1D
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- Dense
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- Conv1D
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- Embedding
- PReLU 2D
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- Dense
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- DepthwiseConv2D
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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
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- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- 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
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- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- 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
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- Conv1D
- Conv2D
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- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- 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
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- Cropping1D
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- Dense
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- Dropout
- Embedding
- Flatten
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- GlobalMaxPool3D
- GRU
- Input
- LayerNormalization
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- MaxPool1D
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- MultiHeadAttention
- Multiply
- Permute3D
- Reshape
- RNN
- SeparableConv1D
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- SimpleRNN
- SpatialDropout
- Substract
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- UpSampling1D
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- ZeroPadding1D
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- AlphaDropout
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- Conv1D
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- Conv2D
- Conv2DTranspose
- Conv3D
- Conv3DTranspose
- Cropping1D
- Cropping2D
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- Dense
- DepthwiseConv2D
- Dropout
- Embedding
- Flatten
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- GlobalAvgPool1D
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- GRU
- LayerNormalization
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- Permute3D
- Reshape
- RNN
- SeparableConv1D
- SeparableConv2D
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- SpatialDropout
- UpSampling1D
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- ZeroPadding2D
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- Resume
- Accuracy
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- Poisson
- Precision
- PrecisionAtRecall
- Recall
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- RootMeanSquaredError
- SensitivityAtSpecificity
- SparseCategoricalAccuracy
- SparseCategoricalCrossentropy
- SparseTopKCategoricalAccuracy
- Specificity
- SpecificityAtSensitivity
- SquaredHinge
- Sum
- TopKCategoricalAccuracy
- TrueNegatives
- TruePositives
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- Resume
- Constant
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- HeNormal
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- Identity
- LecunNormal
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- Ones
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Get input shape by name
Description
Gets the input size of the layer selected by the name given as input.
Input parameters
Model in : model architecture.
name : string, layer name.
Output parameters
Model out : model architecture.
input : cluster
name : string, name of layer.
index : integer, index of layer.
input_order : integer, order of entry.
input_shape : array, size of the input.
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 “Get Input Shape by name” function
1 – Define Graph
We define two graphs with an input of different size and a Dense layer. We merge the two graphs to have only one and we add a last Dense to the graph. Each Dense layer is parameterized differently.
2 – Merge Function
We use the “Merge” function to merge the two graphs.
3 – Get Function
We use the “Get Input Shape by name” function to get input size of layer named Dense2.