SimpleGrad
SimpleGrad is a lightweight automatic differentiation library written in C++ with Python bindings.
Compatible Operating Systems:
- Linux (x86_64, aarch64)
- Windows (x86_64)
- macOS (x86_64, arm64)
Prebuilt wheels are provided for CPython 3.9–3.13. On other platforms, pip builds from source, which requires a C++17 compiler.
Installation
pip install simplegrad
Features
- Multi-layer perceptron (MLP) which can be used for regression and classification tasks
- Supports basic arithmetic operations
- Lightweight and easy to use
- Gradient computation
- Backpropagation
- Numpy compatibility
Usage
Here's a quick example of how to use MLP in SimpleGrad:
from simplegrad import MLP, Node
from sklearn import datasets
# Define the model
X, y = datasets.make_classification(
n_samples=1000,
n_features=10,
n_classes=2,
random_state=42, # for reproducibility
)
lr = 0.01
batch_size = 16
epochs = 10
# Define the model
model = MLP(
10, [16, 1]
) # 2 input nodes, 2 hidden layers with arbitrary sizes, 1 output node
# Training data
n_batches = (len(X) + batch_size - 1) // batch_size # Ceiling division
for epoch in range(epochs):
epoch_loss = 0.0
for i in range(0, len(X), batch_size):
batch_X = X[i : i + batch_size]
batch_y = y[i : i + batch_size]
current_batch_size = len(batch_X) # Handle last batch
batch_loss = 0.0
#model.zero_grad() # gradients are automatically reset after step function
# Accumulate gradients over batch
for x, y_true in zip(batch_X, batch_y):
y_hat = model(x)[0]
y_true = Node(y_true)
loss = (y_hat - y_true) ** 2
loss = loss * (1.0 / current_batch_size) # Normalize loss
batch_loss += loss.data()
loss.backward()
model.step(lr) # Update weights using accumulated gradients
del loss, y_hat, y_true # Clean up
epoch_loss += batch_loss
# Average loss over all batches
print(f"Epoch {epoch+1}, Average Loss: {epoch_loss/n_batches:.3f}")
1 maintainer
Metadata
1 maintainer
Release files for simplegrad 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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|---|---|---|---|
| simplegrad-0.1.0.tar.gz | 9.7 kB | Details |
Built distributions (wheels)
Total release size: 5.1 MB
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