Spingalett for Python

Thin ctypes bindings over the Spingalett shared library: no compiler is needed to install them, only NumPy and libspingalett (.so / .dylib / .dll). The wheels on PyPI and on the GitHub releases carry the library inside (Linux x86-64 and AArch64 with glibc 2.28 or newer, Windows x86-64, macOS 11 and newer, for any Python 3), with OpenMP:

pip install spingalett

From a source checkout, build the library and point the bindings at it:

cmake -S . -B Build -DCMAKE_BUILD_TYPE=Release -DBUILD_WITH_OPENMP=ON
cmake --build Build --parallel            # produces Bin/libspingalett.so
pip install ./Bindings/Python
export SPINGALETT_LIBRARY=$PWD/Bin/libspingalett.so   # or put Bin/ on the library path

Without SPINGALETT_LIBRARY the package looks inside itself (wheels), then in the repository's Bin/ directory (when imported from a checkout), then on the system library path. The package is typed (py.typed): editors and type checkers read its annotations.

import numpy as np
import spingalett as sg

x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
y = np.array([[0], [1], [1], [0]], dtype=np.float32)

sg.set_compute_mode(sg.ComputeMode.OPENBLAS)   # falls back to single-threaded if unavailable
sg.seed(42)

layers = [
    sg.Layer(2),
    sg.Layer(16, sg.Activation.TANH, sg.Init.XAVIER, dropout=0.1),
    sg.Layer(1, sg.Activation.SIGMOID, sg.Init.XAVIER),
]
with sg.Network(sg.Loss.MSE, layers) as net:
    net.train(x, y, epochs=3000, optimizer=sg.Optimizer.ADAMW, learning_rate=0.02,
              weight_decay=1e-4, lr_scheduler=sg.WarmupCosine(warmup_epochs=100),
              callback=lambda net, progress: progress.train_loss < 1e-4, callback_interval=100)
    print(net.forward(x))          # one row per sample
    net.save("xor", precision=sg.Precision.FP16)

# validation, early stopping and the best epoch's weights
x_train, y_train = sg.load_idx("train-images-idx3-ubyte", "train-labels-idx1-ubyte", num_classes=10)
with sg.Network(sg.Loss.CROSS_ENTROPY, [784, sg.Layer(128, sg.Activation.RELU, sg.Init.HE),
                                        sg.Layer(10, sg.Activation.SOFTMAX, sg.Init.XAVIER)]) as net:
    result = net.train(x_train[:55000], y_train[:55000], epochs=30, strategy=sg.Strategy.MINI_BATCH,
                       batch_size=128, optimizer=sg.Optimizer.ADAMW, learning_rate=1e-3,
                       validation_data=(x_train[55000:], y_train[55000:]), monitor=sg.Monitor.VAL_ACCURACY,
                       early_stopping_patience=3, restore_best_weights=True)
    print(result.status.name, result.best_epoch, net.evaluate(x_train[55000:], y_train[55000:]))

# a convolutional network: images as rows of height x width x channels floats (channels last)
cnn = sg.Network(sg.Loss.CROSS_ENTROPY, [
    sg.Input(28, 28, 1),
    sg.Conv2D(32, 3, padding=1), sg.MaxPool2D(2),             # ReLU and He initialization by default
    sg.Conv2D(64, 3, padding=1), sg.MaxPool2D(2),
    sg.Layer(128, sg.Activation.RELU, sg.Init.HE, dropout=0.3),
    sg.Layer(10, sg.Activation.SOFTMAX, sg.Init.XAVIER),
])
cnn.train(x_train[:55000], y_train[:55000], epochs=2, strategy=sg.Strategy.MINI_BATCH, batch_size=128,
          optimizer=sg.Optimizer.ADAMW, learning_rate=1e-3)
print([(l.type.name, l.shape) for l in cnn.layers], cnn.get_weights(0).shape)   # filters x 3 x 3 x 1

# batch normalization, a depthwise-separable block and image augmentation on CIFAR-10
x_cifar, y_cifar = sg.load_cifar([f"cifar-10-batches-bin/data_batch_{i}.bin" for i in range(1, 6)])
bn = sg.Network(sg.Loss.CROSS_ENTROPY, [
    sg.Input(32, 32, 3),
    sg.Conv2D(32, 3, padding=1, activation=sg.Activation.NONE), sg.BatchNorm(sg.Activation.RELU),
    sg.Conv2D(32, 3, padding=1, groups=32, activation=sg.Activation.NONE), sg.BatchNorm(sg.Activation.RELU),
    sg.Conv2D(64, 1, activation=sg.Activation.NONE), sg.BatchNorm(sg.Activation.RELU), sg.MaxPool2D(2),
    sg.Layer(10, sg.Activation.SOFTMAX, sg.Init.XAVIER),
])
bn.train(x_cifar, y_cifar, epochs=5, strategy=sg.Strategy.MINI_BATCH, batch_size=128,
         optimizer=sg.Optimizer.ADAMW, learning_rate=2e-3, augment_shift=4, augment_flip=True)

# a residual block: inputs= names earlier layers, add_add / add_concat combine them
res = sg.Network(sg.Loss.CROSS_ENTROPY)
x = res.add_input(32, 32, 3).add_conv2d(16, 3, padding=1, activation=sg.Activation.NONE) \
       .add_batch_norm(sg.Activation.RELU).last
res.add_conv2d(16, 3, padding=1, activation=sg.Activation.NONE).add_batch_norm(sg.Activation.RELU)
res.add_conv2d(16, 3, padding=1, activation=sg.Activation.NONE).add_batch_norm()
res.add_add([x, -1], activation=sg.Activation.RELU)       # -1: the layer before
res.add_global_avg_pool().add_layer(10, sg.Activation.SOFTMAX, sg.Init.XAVIER)

# PyTorch: a module through ONNX in memory, or a state dict into a network of the same layers
import torch
model = torch.nn.Sequential(torch.nn.Conv2d(3, 8, 3, padding=1), torch.nn.ReLU(), torch.nn.Flatten(),
                            torch.nn.Linear(8 * 32 * 32, 10))
net = sg.Network.from_torch(model, torch.rand(1, 3, 32, 32))     # takes (32, 32, 3) channels-last images
same = sg.Network(sg.Loss.MSE, [sg.Input(32, 32, 3), sg.Conv2D(8, 3, padding=1), sg.Layer(10, sg.Activation.NONE)])
same.load_pytorch(model.state_dict())                              # or "model.pt", "model.safetensors"
onnx = sg.Network.from_onnx("model.onnx")

with sg.Network.load("xor.slett") as net:
    print(net.topology, net.forward([1, 0]))
    # deployment: a read-only INT8 model with integer kernels, and a C header for firmware
    with net.to_model(sg.Precision.INT8) as model:
        print(model.predict([1, 0]), model.layers, model.size)
    net.export_c_header("xor_model.h", "xor_model", sg.Precision.INT8)

uint8 arrays stand for 8-bit images: value q means q / 255 in train(), forward(), predict(), evaluate() and save_dataset(). train() keeps such inputs as bytes, a quarter of the memory of float32, and converts them a batch at a time, with the same result as training on x / 255 in float32.

Library errors raise SpingalettError, whose code is an ErrorCode. The bindings check on import that the library has the same major.minor version (library_version()), because they mirror its struct layouts. A Network is not thread-safe: use one per thread. A Model is read-only and can be shared between threads.