Multi Input Data by index

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_order : integer, defines the position of the input within the data array. It corresponds to the index assigned to the input when it is created (via the index 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_order : integer, defines the position of the input within the data array. It corresponds to the index assigned to the input when it is created (via the index 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).

Parameters : cluster

Fit : 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.

Scheduler : cluster

warmup_step_count : integer, number of steps during which the learning rate increases linearly from 0 up to the initial_learning_rate.
num_train_epochs : integer, total number of training steps for the scheduler. After reaching the initial_learning_rate, the learning rate linearly decays to 0 over the remaining steps.
learning_rate : float, maximum learning rate reached at the end of the warm‑up phase, before the linear decay begins.