wyn-keras 0.1.7

Our customized keras package for popular image classifier techniques.

wyn-keras 🎉

A Python package for building and experimenting with Vision Transformer (ViT) models using TensorFlow and Keras.

Directory Structure 📁

wyn-keras/
├── pyproject.toml
├── README.md
├── wyn_keras
│   ├── __init__.py
│   └── vit.py
├── tests
│   └── __init__.py
└── .gitignore

Installation Instructions (From PIP) 📦

To install the package from PyPI, use the following command:

pip install wyn-keras

For more information, visit the PyPI page.

Installation Instructions (Local) 📦

To install the package and its dependencies, use Poetry:

# Install Poetry if you haven't already
curl -sSL https://install.python-poetry.org | python3 -

# Install the package
poetry install

Usage 🚀

Vision Transformer

The ViT class allows you to create and train Vision Transformer models.

Additional Functions (Coming Soon...) 🚧

Stay tuned for more functionalities to be added in the future!

Example Usage 📚

MNIST Example

import tensorflow as tf
from wyn_keras.vit import ViT

# Load and preprocess the MNIST dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train[..., tf.newaxis].astype("float32") / 255.0
x_test = x_test[..., tf.newaxis].astype("float32") / 255.0

# Number of classes in MNIST dataset
num_classes = 10

# Create an instance of the ViT class
vit_model = ViT(num_classes=num_classes, input_shape=(28, 28, 1), image_size=28, num_epochs=2)

# Create the ViT model
model = vit_model.create_vit_classifier()

# Train the model
history = vit_model.run_experiment(model, x_train, y_train, x_test, y_test)

# Plot patches
vit_model.plot_patches(x_test)

CIFAR-10 Example

import tensorflow as tf
from wyn_keras.vit import ViT

# Load and preprocess the CIFAR-10 dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0

# Number of classes in CIFAR-10 dataset
num_classes = 10

# Create an instance of the ViT class
vit_model = ViT(num_classes=num_classes, input_shape=(32, 32, 3), image_size=32, num_epochs=2)

# Create the ViT model
model = vit_model.create_vit_classifier()

# Train the model
history = vit_model.run_experiment(model, x_train, y_train, x_test, y_test)

# Plot patches
vit_model.plot_patches(x_test)

Author ✍️

Yiqiao Yin
Email: [e-mail]
Personal Site: https://www.y-yin.io/