Principle:Gretelai Gretel synthetics Training Configuration
| Knowledge Sources | |
|---|---|
| Domains | Synthetic_Data, Deep_Learning, Hyperparameter_Management |
| Last Updated | 2026-02-14 19:00 GMT |
Overview
Training configuration is the practice of centralizing all hyperparameters, file paths, and behavioral flags for a machine learning training pipeline into a single, validated data structure.
Description
In any neural network training workflow, dozens of parameters govern model architecture (embedding dimensions, hidden units, dropout rates), optimization (learning rate, batch size, epochs), data handling (sequence length, buffer size, validation split), and output behavior (generation temperature, checkpoint saving strategy). Training configuration addresses the problem of parameter sprawl by collecting these values into a structured, validated object that can be serialized, shared, and reproduced.
A well-designed training configuration provides:
- Default values that represent sensible starting points for most use cases.
- Validation logic that catches incompatible or invalid parameter combinations before training begins (for example, verifying that differential privacy settings are compatible with the installed framework version).
- Derived paths for artifacts such as training data files and checkpoint directories, computed automatically from a base directory.
- Extensibility hooks such as epoch callbacks and maximum training time limits.
- Serialization to JSON or equivalent formats for experiment tracking and reproducibility.
For LSTM text generation specifically, the configuration must capture both the training phase parameters (epochs, early stopping, LSTM layer dimensions) and the generation phase parameters (temperature, maximum characters per line, batch prediction size), since the same configuration object is reused when loading a trained model for inference.
Usage
Use a training configuration object whenever you need to:
- Set up a new synthetic text generation experiment with specific hyperparameters.
- Reproduce a previous training run by loading a saved configuration.
- Switch between standard and differentially private training modes.
- Control early stopping behavior and checkpoint management.
Theoretical Basis
The training configuration for an LSTM text generation model must capture parameters spanning several theoretical areas:
Architecture parameters define the model capacity. The embedding dimension d_emb maps each token in a vocabulary of size V to a dense vector. The LSTM hidden state size d_h (rnn_units) determines the capacity of each recurrent layer to capture sequential dependencies. Dropout rate p is applied between layers as a regularization technique to prevent overfitting:
h_dropped = Dropout(p) * h
Optimization parameters control convergence. The learning rate eta scales gradient updates. Batch size B determines how many samples contribute to each gradient estimate. Early stopping monitors a metric (typically validation loss) and halts training after patience epochs of no improvement exceeding min_delta:
if best_metric - current_metric < min_delta for patience epochs:
stop training
Differential privacy parameters optionally bound the influence of any single training example. The L2 norm clip C bounds per-example gradients, and the noise multiplier sigma controls Gaussian noise added to the clipped gradients:
clipped_grad = grad * min(1, C / ||grad||_2)
noisy_grad = clipped_grad + N(0, sigma^2 * C^2 * I)
Generation parameters control the inference phase. Temperature tau scales logits before softmax to control randomness:
P(token_i) = exp(z_i / tau) / sum_j(exp(z_j / tau))