iterative-hyperparameter-search

Guided Hyperparameter Search for Custom Deep Learning Models and Training Loops

a tensorflow-based pipeline for tuning deep learning model hyperparameters with a simple guided hyperparameter search strategy.

Features

• Custom Model (SuperbModel): A basic multi layer perceptron with batch normalization, dropout, and adaptive layer configuration.
• Guided Hyperparameter Search: Hyperparameter search algorithm based on iteratively perturbing the best set of hyperparameters. Tracks training progress with validation accuracy monitoring.

File Overview

• model.py: Defines SuperbModel and SuperbLayer with customizable hyperparameters. Can be modified to implement any deep neural network structure.
• train_utils.py: Contains utility functions for batch sampling, status display, and training logic. The logic can be modified to implement any custom training loop.
• hyperparameter_search.py: Implements the guided hyperparameter search algorithm.
• train.py: Loads the MNIST dataset by default and executes hyperparameter search.

Usage

1. Install dependencies:
   pip install tensorflow numpy

2. Run hyperparameter search: python train.py

3. Modify train.py, model.py, train_utils.py to implement custom architectures and use different datasets.

This site is open source. Improve this page.