DeepTensor 🔥

• DeepTensor: A minimal PyTorch-like deep learning library focused on custom autograd and efficient tensor operations.

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Features at a Glance 🚀

• Automatic gradient computation with a custom autograd engine.
• Weight initialization schemes:
  • Xavier/Glorot and He initialization in both uniform and normal variants.
• Activation functions:
  • ReLU, GeLU, Sigmoid, Tanh, SoftMax, LeakyReLU, and more.
• Built-in loss functions:
  • Mean Squared Error (MSE), Cross Entropy, and Binary Cross Entropy.
• Optimizers:
  • SGD, Momentum, AdaGrad, RMSprop, and Adam.

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Why DeepTensor?

DeepTensor offers a hands-on implementation of deep learning fundamentals with a focus on customizability and learning the internals of deep learning frameworks like PyTorch.

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Installation

pip install deeptensor

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Setup the project for development

git clone --recurse-submodules -j8 git@github.com:deependujha/DeepTensor.git
cd DeepTensor
# run ctests
make ctest
# install python package in editable mode
pip install -e .# run pytest
make test

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Checkout Demo

• play with latest demo

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Check Docs

• visit docs

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Basic Usage

fromdeeptensorimport (
# modelModel,
# LayersConv2D,
MaxPooling2D,
Flatten,
LinearLayer,
# activation layersGeLu,
LeakyReLu,
ReLu,
Sigmoid,
SoftMax,
Tanh,
# core objectsTensor,
Value,
# optimizersSGD,
Momentum,
AdaGrad,
RMSprop,
Adam,
# lossesmean_squared_error,
cross_entropy,
binary_cross_entropy,
)
model=Model(
[
LinearLayer(2, 16),
ReLu(),
LinearLayer(16, 16),
LeakyReLu(0.1),
LinearLayer(16, 1),
Sigmoid(),
],
False, # using_cuda
)
opt=Adam(model, 0.01) # learning rateprint(model)
tensor_input=Tensor([2])
tensor_input.set(0, Value(2.4))
tensor_input.set(1, Value(5.2))
out=model(tensor_input)
loss=mean_squared_error(out, YOUR_EXPECTED_OUTPUT)
# backproploss.backward()
opt.step()
opt.zero_grad()

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Features expected to be added

• Save & Load model
• Train a character-level transformer model
• Add support for DDP
• Add support for CUDA execution ⭐️

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Open to Opportunities 🎅🏻🎁

I am actively seeking new opportunities to contribute to impactful projects in the deep learning and AI space.

If you are interested in collaborating or have a position that aligns with my expertise, feel free to reach out!

You can connect with me on GitHub, X (formerly twitter), or email me: deependujha21@gmail.com.