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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
- MutiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
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- Conv1DTranspose
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- DepthwiseConv2D
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- Embedding
- BatchNormalization
- LayerNormalization
- Bidirectional
- GRU
- LSTM
- SimpleRNN
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- Dense
- PReLU 2D
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- AdditiveAttention
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- MultiHeadAttention
- Conv1D
- Conv2D
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- ConvLSTM1D
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- Conv1DTranspose
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- DepthwiseConv2D
- SeparableConv1D
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- Embedding
- BatchNormalization
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- Bidirectional
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- Dense
- AdditiveAttention
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- Conv1D
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- ConvLSTM1D
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- DepthwiseConv2D
- SeparableConv1D
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- Embedding
- PReLU 2D
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- Dense
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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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- Dense
- Embedding
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- 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
- MultiHeadAttention
- Conv1D
- Conv2D
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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
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- 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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- Add
- AdditiveAttention
- AlphaDropout
- Attention
- Average
- AvgPool1D
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- Concatenate
- Conv1D
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- ConvLSTM1D
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- ConvLSTM3D
- Cropping1D
- Cropping2D
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- Dense
- DepthwiseConv2D
- Dropout
- Embedding
- Flatten
- GaussianDropout
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- GlobalAvgPool1D
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- GlobalMaxPool1D
- GlobalMaxPool2D
- GlobalMaxPool3D
- GRU
- Input
- LayerNormalization
- LSTM
- MaxPool1D
- MaxPool2D
- MaxPool3D
- MultiHeadAttention
- Multiply
- Permute3D
- Reshape
- RNN
- SeparableConv1D
- SeparableConv2D
- SimpleRNN
- SpatialDropout
- Substract
- TimeDistributed
- UpSampling1D
- UpSampling2D
- UpSampling3D
- ZeroPadding1D
- ZeroPadding2D
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- AlphaDropout
- AvgPool1D
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- 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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- Resume
- Accuracy
- BinaryAccuracy
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- CosineSimilarity
- FalseNegatives
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- IoU
- KLDivergence
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- Mean
- MeanAbsoluteError
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- MeanRelativeError
- MeanSquaredError
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- MeanTensor
- OneHotIoU
- OneHotMeanIoU
- 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
- LecunUniform
- Ones
- Orthogonal
- RandomNormal
- RandomUnifom
- TruncatedNormal
- VarianceScaling
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Get all weights
Description
Gets the weights of all layers contained in the model.
Input parameters
Model in : model architecture.
Output parameters
Model out : model architecture.
weight_array : array
index : integer, index of layer.
name : string, name of layer.
weight : variant, weight value.
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 All Weights” 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 “Set All Random Weights” function to create random weights for all layers which have weights in the model.
3 – Get Function
We use the “Get All Weights” function to get the weights of all layers that have them from the model.
4 – Index weight_array
Since the only layers that have weights are Dense1 and Dense2 we index the array returned by the get function to retrieve their weight.
5 – Convert variant to data
The get function returns the weights in a variant, so we use the “Variant To Data” function of LabVIEW to get the result in an array. For that, we use the polymorph which transmits us directly the typedef of the Dense layer.