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Quick start
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API
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- Dense
- PReLU 2D
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- PReLU 4D
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- AdditiveAttention
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- Conv1D
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- Dense
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- Dense
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- Dense
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- LayerNormalization
- PReLU 2D
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- PReLU 4D
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- Bidirectional
- GRU
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- Dense
- Embedding
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- Conv1DTranspose
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- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
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- BatchNormalization
- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
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- RNN (GRU)
- RNN (LSTM)
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- SimpleRNN
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- Dense
- Embedding
- AdditiveAttention
- Attention
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- Conv1D
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- Conv1DTranspose
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- DepthwiseConv2D
- SeparableConv1D
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- BatchNormalization
- LayerNormalization
- PReLU 2D
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- PReLU 4D
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- Dense
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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 (LSTM)
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- SimpleRNN
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- Add
- AdditiveAttention
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- Dense
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- GRU
- Input
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- MultiHeadAttention
- Multiply
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- SeparableConv1D
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- SimpleRNN
- SpatialDropout
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- AlphaDropout
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- Bidirectional
- Conv1D
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- Conv2D
- Conv2DTranspose
- Conv3D
- Conv3DTranspose
- Cropping1D
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- Dense
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- Dropout
- Embedding
- Flatten
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- GRU
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- Reshape
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- SeparableConv1D
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- SimpleRNN
- SpatialDropout
- UpSampling1D
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- ZeroPadding1D
- ZeroPadding2D
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- Resume
- Accuracy
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- Poisson
- Precision
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- Recall
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- RootMeanSquaredError
- SensitivityAtSpecificity
- SparseCategoricalAccuracy
- SparseCategoricalCrossentropy
- SparseTopKCategoricalAccuracy
- Specificity
- SpecificityAtSensitivity
- SquaredHinge
- Sum
- TopKCategoricalAccuracy
- TrueNegatives
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- Resume
- Constant
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- HeNormal
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- Identity
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DepthwiseConv2D
Description
Adds the weights of the DepthwiseConv2D layer to the weights table. Type : polymorphic.
Input parameters
Weights in : array
name : string, name of layer.
weights : variant, weights values.
name : string, name of layer.
filters_depthwise : array, 4D values. filters_depthwise = [channels, 1, size[0], size[1]].
biases : array, 1D values. biases = [channels].
Output parameters
Weights out : array
name : string, name of layer.
weights : variant, weights values.
Dimension
- filters_depthwise = [channels, 1, size[0], size[1]]
The size of filters_depthwise depends on the input of the DepthwiseConv2D layer and the parameters size.
For example if the input of the layer has a size of [batch_size = 10, channels = 8, rows = 5, cols = 5] and size the value [3, 3] then filters will have a size of [channels = 8, 1, size[0] = 3, size[1] = 3].
- biases = [channels]
The size of biases depends on the parameter size of the DepthwiseConv2D layer.
For example, if the input of the layer has a size of [batch_size = 10, channels = 8, rows = 5, cols = 5] then biases will have a size of [channels = 8].
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).