fivedreg-tf 0.1.0

TensorFlow-based 5D→1D regression trainer/tester with plotting.

fivedreg_tf (TensorFlow)

TensorFlow/Keras implementation of the 5D → 1D regressor with a simple training/testing API.

Modules

• tf_model.py: build_tf_model(hidden_sizes, Lambda) builds a Sequential model with L2 regularisation and He/Xavier init.
• trainer_tf.py: TrainerTF loads/validates data, trains a TF model, and saves model_tf.keras plus normalisation values. Optional early stopping, LR decay, batch size, and grad clipping.
• tester_tf.py: TesterTF loads the saved TF model and normalisation stats to make predictions on NumPy arrays or .pkl files.
• logger.py, utils.py: lightweight logging and decorators.
• Synthetic data examples use the separate interpy_synth package (installed automatically).

Installation (package)

From this backend/fivedreg_tf directory:

pip install -r requirements.lock  # pinned CPU-only deps
pip install .

Headless environments: plotting is configured with the Agg backend, so no display is required. GPU is not required or supported; the package depends on tensorflow-cpu. For reproducibility, install via the pinned requirements.lock in backend/.

Docker (whole app):

cd ../..
./scripts/docker_build.sh
./scripts/docker_up.sh   # backend on :8000 (includes TF if built with fivedreg_tf)

Usage

from fivedreg_tf.trainer_tf import TrainerTF
from fivedreg_tf.tester_tf import TesterTF
from interpy_synth import synthetic_5d_pickle
import os

out_dir = "outputs_tf"
os.makedirs(out_dir, exist_ok=True)
data_path = synthetic_5d_pickle(os.path.join(out_dir, "train.pkl"), n=1000, seed=42)

trainer = TrainerTF(
    directory=out_dir,
    hidden_sizes=[64, 32, 16],
    epochs=100,
    learning_rate=0.01,
    early_stop_patience=10,
    lr_decay=0.95,
    seed=42,
)
train_rmse, val_rmse = trainer.train(data_path)

tester = TesterTF(directory=out_dir)
y_pred = tester.predict([0.1, 0.2, 0.3, 0.4, 0.5])

Note: Ensure TensorFlow is installed in your environment to use this package. Training also saves plots (rmse_vs_epochs.png, ytrue_vs_ypred.png) to the directory. Metadata (tf_model_metadata.json) includes hidden sizes, Lambda, epochs run, best epoch, best train/val RMSE, baseline RMSE, and final train/val R².

Performance/ops tips:

• CPU-only build; choose modest hidden sizes/batch sizes for constrained CPUs.
• Batch size and grad clipping can help stabilise small datasets (see tests for small-batch config).
• Use requirements.lock for reproducibility; mount outputs_tf via Docker volumes in production.

FastAPI usage

• /train supports model_type=tf to train and save TF artifacts into backend/outputs_tf/ (including TF plots) when running the API.
• /predict accepts model_type=tf to run predictions using the TF model.
• /artifacts/{filename} serves TF artifacts (model_tf.keras, normalisation_values_tf.npz, tf_model_metadata.json) as well as NumPy ones.