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Inputs CPU Raw Data (set learning rate)

Description

Runs a full training step on the model with raw input data from the CPU. This includes the forward and backward pass, followed by the optimizer update and gradient reset (only if the weights are updated). The learning rate is provided as a parameter at each call, and the output buffer is allocated automatically.

Input parameters

 

 Training in : object, training session.

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).
update? : boolean, indicating whether to update the model weights at this step. If set to false, gradients are only accumulated without updating the weights.
learning rate : float, step size controlling how much to adjust weights during training.

 

Output parameters

 

 Training out : object, training session.

Losses Info : cluster

outputs_raw_data : array, contains the raw byte representation of the input tensor data, stored as a 1D flattened buffer.
output_shapes_array : 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.
output_strings_length_array : 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.
output_ranks_array : array, indicates the rank of the tensor, i.e. the number of dimensions (Scalar = 0, 1D = 1, 2D = 2, etc.).
output_types_array : array, defines the ONNX tensor type as an enumerated value (e.g. FLOAT, INT64, STRING).
losses_names : array, specifies which loss the data correspond to.

 

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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