ga-optimizer 0.1.1

Gradient Accumulation Optimizer Wrapper for Keras/TensorFlow

GA Optimizer

Info

GA Optimizer is a wrapper for Keras/TensorFlow optimizers to enable gradient accumulation, allowing you to simulate larger batch sizes than your hardware can handle.

Getting Started

Installation

Pip

You can install the package using pip:

pip install ga-optimizer

Clone repo

Alternatively, you can clone the repository directly from GitHub and install it manually:

git clone https://github.com/kimjansheden/GAOptimizer.git
cd GAOptimizer
pip install .

Usage

Here's an example of how to use GA Optimizer in your TensorFlow/Keras project:

import tensorflow as tf
from tensorflow.keras import layers
from ga_optimizer import make_ga_optimizer

# Define a simple model
model = tf.keras.Sequential([
    layers.Dense(64, activation='relu', input_shape=(784,)),
    layers.Dense(10, activation='softmax')
])

# Define loss and metrics
loss = tf.keras.losses.SparseCategoricalCrossentropy()
metrics = [tf.keras.metrics.SparseCategoricalAccuracy()]

# Define base optimizer
base_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)

# Wrap the optimizer with GA Optimizer
ga_optimizer = make_ga_optimizer(
    desired_batch_size=64,
    batch_size=8,
    base_optimizer=base_optimizer,
    log_level=Ga_Optimizer.LOG_PARANOID
)

# Compile your model with GA optimizer
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

# Generate some dummy data
import numpy as np
x_train = np.random.random((1000, 784))
y_train = np.random.randint(10, size=(1000,))

# Train the model
model.fit(x_train, y_train, epochs=10, batch_size=batch_size)

Functions and Classes

make_ga_optimizer

The make_ga_optimizer function wraps a given Keras/TensorFlow optimizer to enable gradient accumulation.

Arguments

• desired_batch_size (int): The effective batch size you want to simulate.
• batch_size (int): The actual batch size that your hardware can handle.
• base_optimizer (tf.keras.optimizers.Optimizer): The base optimizer to wrap.
• base_optimizer_params (dict, optional): Parameters for the base optimizer. Only needed if you have any params in your base_optimizer and you're on a Mac where optimizer gets converted to legacy. Defaults to None.
• log_level (int, optional): Logging level. Defaults to optimizers.Optimizer.LOG_NONE.

Optimizer Class

This class is a wrapper for Keras optimizers to support gradient accumulation.

Logging Levels

The GA Optimizer supports different logging levels:

• LOG_NONE: No logs.
• LOG_INFO: Informational messages.
• LOG_DEBUG: Debug messages, more verbose.
• LOG_PARANOID: Paranoid debug messages, extremely verbose.
• LOG_EXTREMELY_PARANOID: Extremely paranoid debug messages, extremely, extremely verbose.

Tests

To run the tests:

python -m unittest discover -s tests

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributions

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

Acknowledgements

I would like to acknowledge and thank the developers of the RunAI GA Optimizer project, which served as the foundation for this work. Their innovative approach to gradient accumulation inspired and enabled the development of this extended version.