tiny-dnn/tiny-dnn

Features

• Deep Learning Frameworks - Provides a header-only C++14 deep learning framework with no external dependencies.
• Backpropagation Training Loops - Adjusts network weights using backpropagation and gradient descent on labeled training data.
• Static Graph Builders - Builds the entire network topology at construction time as a fixed directed acyclic graph with no runtime graph mutation.
• Binary Parsers - Provides direct binary parsing of Caffe model files without external protocol buffer dependencies.
• CPU-Optimized Training - Trains feedforward and convolutional networks on CPU using minibatch SGD with configurable optimizers.
• Header-Only Implementations - Provides a header-only C++14 deep learning framework with no external dependencies.
• Automatic Differentiation Engines - Implements automatic differentiation through a computational graph for gradient-based backpropagation.
• Inference Execution - Feeds input data through a trained network to produce predictions or classifications.
• Training Workflows - Provides built-in support for training networks on the MNIST dataset with configurable epochs and minibatch size.
• Model Inference - Feeds input data through a completed network to produce predictions or classifications.
• Template-Based Compositions - Composes network layers as C++ template classes at compile time for zero-cost abstraction.
• Minibatch Training Utilities - Trains networks by processing small random subsets of training data per iteration with configurable learning rate and momentum.
• Neural Network Composition - Constructs a deep learning model by composing layers in C++ without external dependencies or separate compilation steps.
• Sequential Model Builders - Assembles a network by stacking layers such as convolution, pooling, and fully-connected in a sequential pipeline.
• Configurable Training Loops - Runs a training loop with a chosen optimizer and loss function over labeled data for a specified number of epochs and minibatch size.
• Stochastic Gradient Descent - Trains networks using minibatch stochastic gradient descent with configurable learning rate and momentum.
• Weight Serialization - Saves and loads model parameters as flat binary files for fast storage and retrieval.
• Header-Only Implementations - Provides a dependency-free, header-only machine learning toolkit for constructing and training neural networks.
• C++ Implementations - Provides a C++14 header-only library for building, training, and running inference on neural networks.
• CPU-Optimized Implementations - Provides a CPU-optimized C++14 library for building, training, and running inference on neural networks.
• Cache-Friendly Tensor Operations - Implements CPU-optimized tensor operations with cache-friendly memory layouts and loop unrolling.
• Activation Functions - Applies a non-linear transformation like ReLU, sigmoid, or tanh to a layer's output to introduce non-linearity.
• Model Importers - Loads a pre-trained network definition and weights from the Caffe framework for inference or fine-tuning.
• Custom Neural Network Layers - Implements forward and backward propagation functions to introduce a new layer type into a neural network.
• Image Classification - Applies a trained convolutional neural network to recognize objects in image datasets such as MNIST or Cifar-10.
• MNIST Dataset Studies - Trains and evaluates convolutional networks on the MNIST dataset for handwritten digit classification.
• Custom Layer Implementations - Extends the set of available neural network operations by implementing a new layer type through a defined interface.
• Weight Persistence - Persists trained network parameters to a file and restores them later for inference or continued training.
• Loss Function Selections - Measures the difference between predicted and target values using cross-entropy, mean squared error, or mean absolute error.
• Optimizer Selections - Updates network weights during training using SGD, Adagrad, RMSprop, or Adam to minimize the loss.
• Layer Construction - Constructs a single neural network layer, such as a fully-connected or convolutional layer, to process data.
• AI & Machine Learning - Header-only deep learning framework
• Artificial Intelligence - Header-only, dependency-free deep learning framework.
• Machine Learning - Dependency-free deep learning framework for C++14.
• Machine Learning and AI - Header-only deep learning framework with no dependencies.

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