Welcome to our Support Center
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Quick start
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API
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- Dense
- PReLU 2D
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- PReLU 5D
- AdditiveAttention
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- Conv1D
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- Dense
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- Conv1D
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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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- BatchNormalization
- LayerNormalization
- PReLU 2D
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- PReLU 4D
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- Bidirectional
- GRU
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- RNN (GRU)
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- SimpleRNN
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- Dense
- Embedding
- AdditiveAttention
- Attention
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- Conv1D
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- ConvLSTM2D
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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
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- Conv1D
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- 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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- Conv1D
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- Dense
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- Dropout
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- Flatten
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- GRU
- Input
- LayerNormalization
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- MaxPool1D
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- MultiHeadAttention
- Multiply
- Permute3D
- Reshape
- RNN
- SeparableConv1D
- SeparableConv2D
- SimpleRNN
- SpatialDropout
- Substract
- TimeDistributed
- UpSampling1D
- UpSampling2D
- UpSampling3D
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- AlphaDropout
- AvgPool1D
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- 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
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- Resume
- Accuracy
- BinaryAccuracy
- BinaryCrossentropy
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- CategoricalAccuracy
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- CosineSimilarity
- FalseNegatives
- FalsePositives
- Hinge
- Huber
- IoU
- KLDivergence
- LogCoshError
- Mean
- MeanAbsoluteError
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- MeanIoU
- MeanRelativeError
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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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Updated
Metrics 5D
Description
Compute the metric according to the one(s) selected during initialization.
Input parameters
Model in : model architecture.
y_true_data : array, 5D true data(s) of the model.
Output parameters
Model out : model architecture.
layer_metrics : cluster
layer_name : string, name of layer.
index : integer, index of layer.
metrics : array
name : string, name of metric.
value : float, result of metric.
model_informations : cluster
Model Name : string, name of model.
Execution Time : integer, execution time of the backward.
Device : enum, device where the process is executed.
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- Native LabVIEW
- GPU
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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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