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Raw Data Initializer

Description

Creates a graph initializer of any supported data type. The values are stored in a serialized byte buffer (raw_data) and interpreted according to the chosen dtype and shape.

 

Input parameters

 

 Parameters : cluster

raw_data : array, stores the initializer values in a serialized byte buffer. The data is always flattened into a one-dimensional sequence of bytes, regardless of the target tensor shape.
shape : array, defines the true shape of the initializer tensor.
dtype : enum, defines the actual element type stored in the raw_data buffer.
Training : cluster, these parameters only have an effect in a training graph.

type : enum, defines how the initializer behaves when the graph is used in training mode. It determines whether the tensor is treated as a constant, as trainable weights, or as frozen weights. During inference, it makes no difference whether the type is Constant, Train Weights, or Frozen Weights.
Regularizer : cluster, regularizer function applied to the weights matrix.

 name (optional) : string, name of the node.

 

 

Output parameters

 

 Graph out : object, ONNX model architecture.

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 Deep Learning library to run it).
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