phitodeep

Deep learning framework built from scratch with numpy!

Installation

$ pip install phitodeep

Usage

MNIST quickstart:

import numpy as np
from datasets import load_dataset

import phitodeep.loss as loss
import phitodeep.model as m

train_dataset = load_dataset("ylecun/mnist", split="train")
test_dataset = load_dataset("ylecun/mnist", split="test")

X_train = train_dataset["image"]
y_train = train_dataset["label"]
X_test = test_dataset["image"]
y_test = test_dataset["label"]

X_train = np.array(X_train).astype(np.float32) / 255.0
y_train = np.array(y_train)
X_test = np.array(X_test).astype(np.float32) / 255.0
y_test = np.array(y_test)
print(X_train.shape, y_train.shape)

model = (
    m.SequentialBuilder()
    .flatten()
    .dense(784, 128)
    .relu()
    .dense(128, 10)
    .softmax()
    .optimizer("adam")
    .loss(loss.CategoricalCrossEntropy())
    .alpha(0.001)
    .epochs(300)
    .batch(32)
    .build()
)

model.summary()

model.train(X_train, y_train, X_test, y_test)

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

phitodeep was created by Ralph Dugue. It is licensed under the terms of the Apache License 2.0 license.

Credits

phitodeep was created with cookiecutter and the py-pkgs-cookiecutter template.