Metadata-Version: 2.2
Name: autoneuronet
Version: 0.1.7
Summary: AutoNeuroNet is a fully implemented automatic differentiation engine with custom matrices and a full neural network architecture and training pipeline. It comes with Python bindings through PyBind11, allowing for quick and easy development of networks through Python, backed with C++ for enhanced speed and performance.
Keywords: derivative,gradient,matrix,machine learning,deep learning,neural network
Author-Email: Rishab Alagharu <rishabsaia@gmail.com>
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Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: C++
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Project-URL: Homepage, https://github.com/RishabSA/AutoNeuroNet
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Project-URL: Documentation, https://rishabsa.github.io/AutoNeuroNet/
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Description-Content-Type: text/markdown

<!-- ![AutoNeuroNet logo](assets/autoneuronet_name.svg) -->

![AutoNeuroNet logo](https://raw.githubusercontent.com/RishabSA/AutoNeuroNet/refs/heads/main/assets/autoneuronet_name.svg)

**AutoNeuroNet** is a fully implemented automatic differentiation engine with custom matrices and a full neural network architecture and training pipeline. It comes with Python bindings through PyBind11, allowing for quick and easy development of networks through Python, backed with C++ for enhanced speed and performance.

Install **AutoNeuroNet** with PIP:

```bash
pip install autoneuronet
```

See the full documentation at [https://rishabsa.github.io/AutoNeuroNet/](https://rishabsa.github.io/AutoNeuroNet/)

## Quickstart

To get started with AutoNeuroNet, import the package. AutoNeuroNet allows you to make automatically differentiable variables and matrices easily through the `Var` and `Matrix` classes, which store values and gradients as doubles.

**Scalar Automatic Differentiation**

```python
import autoneuronet as ann

x = ann.Var(2.0)
y = x**2 + x * 3.0 + 1.0

# Set the final gradient to 1.0 and perform Backpropagation
y.setGrad(1.0)
y.backward()

print(f"y: {y.val}") # 11.0 = (2)^2 + 3x + 1
print(f"dy/dx: {x.grad}") # 7.0 = 2x + 3
```

**Matrix Initialization**

```python
import autoneuronet as ann

X = ann.Matrix(10, 1)  # shape: (10, 1)
y = ann.Matrix(10, 1)  # shape: (10, 1)

for i in range(n_samples):
    X[i, 0] = ann.Var(i)
    y[i, 0] = 5.0 * i + 3.0 # y = 5x + 3
```

**Matrix Math**

```python
import autoneuronet as ann

X = ann.Matrix(2, 2)
X[0] = [1.0, 2.0]
X[1] = [3.0, 4.0]

Y = ann.Matrix(2, 2)
Y[0] = [5.0, 6.0]
Y[1] = [7.0, 8.0]

# Z = ann.matmul(X, Y)
Z = X @ Y
print(Z)

# Output:
# Matrix(2 x 2) =
# 19.000000 22.000000
# 43.000000 50.000000
```

**NumPy to Matrix**

```python
import autoneuronet as ann
import numpy as np

x = np.array([[1.0, 2.0], [3.0, 4.0]])
X = ann.numpy_to_matrix(x)
print(X)

# Output:
# Matrix(2 x 2) =
# 1.000000 2.000000
# 3.000000 4.000000
```

**Neural Networks, Loss Functions, and Optimizers**

AutoNeuroNet supports several types of layers, including `Linear` fully-connected layers and activations functions such as `ReLU`, `Sigmoid`, or `Softmax`.

```python
import autoneuronet as ann
import numpy as np

model = ann.NeuralNetwork(
    [
        ann.Linear(784, 256, init="he"),
        ann.ReLU(),
        ann.Linear(256, 128, init="he"),
        ann.ReLU(),
        ann.Linear(128, 10, init="he"),
        ann.Softmax(),
    ]
)
optimizer = ann.SGDOptimizer(
    learning_rate=1e-2, model=model, momentum=0.9, weight_decay=1e-4
)

print(model)
```

AutoNeuroNet also supports several loss functions, such as the `MSELoss`, `MAELoss`, `BCELoss`, `CrossEntropyLoss`, and `CrossEntropyLossWithLogits`, and optimzers, such as `GradientDescentOptimizer` and `SGDOptimizer`.

```python
loss = ann.MSELoss(labels, logits)
loss.setGrad(1.0)
loss.backward()

optimizer.optimize()
optimizer.resetGrad()

print(f"Loss: {loss.getVal()}")
```

Reference Resources used in the development of AutoNeuroNet:

- [What's Automatic Differentiation? - HuggingFace](https://huggingface.co/blog/andmholm/what-is-automatic-differentiation)
- [Differentiate Automatically](https://comp6248.ecs.soton.ac.uk/handouts/autograd-handouts.pdf)
- [Andrej Karpathy's MicroGrad](https://github.com/karpathy/micrograd)
