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Multi Input Data by name

Description

Runs asynchronous loop-based training of the model with a fixed learning rate, using multi-input data specified by name within a training session. In streaming mode, this VI updates the latest training outputs, which can be accessed with Read Asynchronous Fit Data to monitor and stop the training.

Input parameters

 

 Training in : object, training session.

Input Data in : array, is an array of clusters, where each cluster represents a single model input. Each cluster contains metadata and raw data required to describe and pass an input tensor to the model.

 input_name : string, specifies the identifier of the input. It corresponds to the name given to the input during its creation (via the optional name parameter).
Inputs Info : cluster

inputs_data : array, contains the raw byte representation of the input tensor data, stored as a 1D flattened buffer.
inputs_shapes : array, specifies the shape of the input tensor. Since the data is stored as a flattened 1D buffer, this shape is necessary to reconstruct the original dimensions.
inputs string length : array, used when the tensor type is string. If the tensor has shape [5,3], this field contains 15 values, each representing the length of a corresponding string element. This ensures that the actual size of inputs_data is known despite variable string lengths.
inputs_ranks : array, indicates the rank of the tensor, i.e. the number of dimensions (Scalar = 0, 1D = 1, 2D = 2, etc.).
inputs_types : array, defines the ONNX tensor type as an enumerated value (e.g. FLOAT, INT64, STRING).

Loss Input Data in : array, is an array of clusters, where each cluster represents a single model input. Each cluster contains metadata and raw data required to describe and pass an input tensor to the model.

 y_true_name : string, specifies the identifier of the input. It corresponds to the name given to the input during its creation (via the optional name parameter).
Inputs Info : cluster

inputs_data : array, contains the raw byte representation of the input tensor data, stored as a 1D flattened buffer.
inputs_shapes : array, specifies the shape of the input tensor. Since the data is stored as a flattened 1D buffer, this shape is necessary to reconstruct the original dimensions.
inputs string length : array, used when the tensor type is string. If the tensor has shape [5,3], this field contains 15 values, each representing the length of a corresponding string element. This ensures that the actual size of inputs_data is known despite variable string lengths.
inputs_ranks : array, indicates the rank of the tensor, i.e. the number of dimensions (Scalar = 0, 1D = 1, 2D = 2, etc.).
inputs_types : array, defines the ONNX tensor type as an enumerated value (e.g. FLOAT, INT64, STRING).

Fit Parameters : cluster

train_batch_size : integer, number of samples processed per batch during training.
num_train_epochs : integer, total number of passes over the entire dataset.
gradient_accumulation_steps : integer, number of steps to accumulate gradients before updating the weights.
learning_rate : float, learning rate used to update the model weights.

 

Output parameters

 

 Training out : object, training session.

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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