Welcome to our Support Center
-
Quick start
-
API
-
-
-
-
-
-
- Dense
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- AdditiveAttention
- Attention
- MutiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- Embedding
- BatchNormalization
- LayerNormalization
- Bidirectional
- GRU
- LSTM
- SimpleRNN
- Show All Articles ( 12 ) Collapse Articles
-
- Dense
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- Embedding
- BatchNormalization
- LayerNormalization
- Bidirectional
- GRU
- LSTM
- SimpleRNN
- Show All Articles ( 12 ) Collapse Articles
-
-
- Dense
- AdditiveAttention
- Attention
- MultiHeadAttention
- BatchNormalization
- LayerNormalization
- Bidirectional
- GRU
- LSTM
- SimpleRNN
- Conv1D
- Conv2D
- Conv3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- Embedding
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Show All Articles ( 12 ) Collapse Articles
-
-
- 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
- Show All Articles ( 15 ) Collapse Articles
-
- 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
- Show All Articles ( 15 ) Collapse Articles
-
-
-
- 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
- Show All Articles ( 15 ) Collapse Articles
-
- 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
- Show All Articles ( 15 ) Collapse Articles
-
-
-
-
- Add
- AdditiveAttention
- AlphaDropout
- Attention
- Average
- AvgPool1D
- AvgPool2D
- AvgPool3D
- 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
- SpatialDropout
- Substract
- TimeDistributed
- UpSampling1D
- UpSampling2D
- UpSampling3D
- ZeroPadding1D
- ZeroPadding2D
- ZeroPadding3D
- Show All Articles ( 45 ) Collapse Articles
-
- 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
- Show All Articles ( 32 ) Collapse Articles
-
-
-
- Resume
- Accuracy
- BinaryAccuracy
- BinaryCrossentropy
- BinaryIoU
- CategoricalAccuracy
- CategoricalCrossentropy
- CategoricalHinge
- CosineSimilarity
- FalseNegatives
- FalsePositives
- Hinge
- Huber
- IoU
- KLDivergence
- LogCoshError
- Mean
- MeanAbsoluteError
- MeanAbsolutePercentageError
- MeanIoU
- MeanRelativeError
- MeanSquaredError
- MeanSquaredLogarithmicError
- MeanTensor
- OneHotIoU
- OneHotMeanIoU
- Poisson
- Precision
- PrecisionAtRecall
- Recall
- RecallAtPrecision
- RootMeanSquaredError
- SensitivityAtSpecificity
- SparseCategoricalAccuracy
- SparseCategoricalCrossentropy
- SparseTopKCategoricalAccuracy
- Specificity
- SpecificityAtSensitivity
- SquaredHinge
- Sum
- TopKCategoricalAccuracy
- TrueNegatives
- TruePositives
- Show All Articles ( 28 ) Collapse Articles
-
- Resume
- Constant
- GlorotNormal
- GlorotUniform
- HeNormal
- HeUniform
- Identity
- LecunNormal
- LecunUniform
- Ones
- Orthogonal
- RandomNormal
- RandomUnifom
- TruncatedNormal
- VarianceScaling
- Zeros
- Show All Articles ( 1 ) Collapse Articles
-
Updated
Get “lda_coeff” by index
Description
Gets the loss derivative attenuation coefficient of layer selected by the index given as input.
Input parameters
Model in : model architecture.
index : integer, layer index.
Output parameters
Model out : model architecture.
lda_coeff : cluster
index : integer, index of layer.
name : string, name of layer.
lda_coeff : float, loss derivative attenuation coefficient 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 lda_coeff by index” function
1 – Define Graph
We define the graph with one input and two Dense layers named Dense1 and Dense2. We set the Dense1 layer with a “lda_coeff” equal to 2 and the Dense2 layer with a “lda_coeff” equal to 5.
2 – Get Function
We use the “Get lda_coeff by index” function to get the value of the parameter of layer at index 2.
Table of Contents