tom is a CPU-based neural network library written in C. It supports dense and convolutional layers, along with a multitude of different activations, optimizers, and loss functions. Internally, tom uses shared matrices in memory to process large amounts of double-precision floating-point data, by sharing data between layers in models. Models can also be serialized and loaded from disk. tom has been tested with the Iris (Fisher, 1936) and MNIST (Deng, L., 2012) datasets. See tests/iris_test.c and tests/mnist_test.c for more information.
Currently, tom supports the following layer types:
- Dense (fully-connected) layers (
layer_dense) - Convolutional 2D layers (
layer_conv2d) - Max-pooling 2D layers (
layer_maxpool2d) - Padding 2D layers (
layer_padding2d) - Dropout layers (
layer_dropout) - ReLU and Leaky ReLU activation layers (
layer_relu,layer_leaky_relu) - Softmax activation layers (
layer_softmax) - Sigmoid activation layers (
layer_sigmoid) - Hyperbolic tangent (tanh) activation layers (
layer_tanh) - Batch normalization layers (
layer_normalization)
tom also supports the following optimizers:
- Stochastic Gradient Descent (SGD) optimizer, including Nesterov momentum (
optimizer_sgd,optimizer_sgd_conv2d,optimizer_sgd_bn) - Adam optimizer (Kingma & Ba, 2014) (
optimizer_adam,optimizer_adam_conv2d,optimizer_adam_bn) - RMSProp optimizer (Hinton, no paper) (
optimizer_rmsprop,optimizer_rmsprop_conv2d,optimizer_rmsprop_bn)
Finally, tom supports the following loss functions:
- Mean Squared Error (MSE) loss (
loss_mse) - Mean Absolute Error (MAE) loss (
loss_mae) - Cross-entropy loss (
loss_crossentropy) - Binary cross-entropy loss (
loss_binary_crossentropy)
tom supports CMake, and can be built using:
cmake --build .
You can use tom in your code as follows:
#include "tom.h"
int main(void) {
struct matrix m;
QUIT_ON_ERROR(matrix_init(&m, 5, 10));
matrix_free(&m);
return 0;
}
Tom also includes a number of other miscellaneous functions for dataset preprocessing and error handling.