SANNet is an artificial neural network framework that provides functionalities to build multi-layer neural networks. It has been born from long term interest towards artificial neural networks and curiosity to understand their inner workings.

Framework's primary components are neural network instance with variable number of input, hidden and output layers. Neural network instance and layers run in their dedicated threads to enable concurrency between multiple neural network instances. Framework allows to define multiple parallel input, hidden and output layers using flexible neural network configuration instance.

SANNet is written on Java and has been built from ground up starting from implementation of matrix library and functions.

Framework provides

• feedforward / dense / activation / add / subtract / divide / dot / multiply / positional encoding layers,
• transform layer to transform dimensions of layer input,
• connect and join layers to create connections from multiple previous layers,
• recurrent layers (simple recurrent layer, LSTM layer, Graves LSTM layer, Peephole LSTM layer, GRU layer, Minimal GRU layer) with option to reverse inputs to create bi-directional recurrent layers,
• attention layers (additive / location-based / (scaled) dot attention),
• convolutional layers (convolution / crosscorrelation / Winograd convolution / depth-wise separable convolutional / depth-wise separable crosscorrelation layer, max / average / random / cyclic pooling layer) and
• flattening layer for convolutional and attention layers.

All layers are executed as dynamically constructed procedures and expressions that have built-in automatic gradient for backpropagation.

Framework supports multiple layer activation functions for hidden layers and loss functions for output layer. Multiple layer initialization methods for weight parameters like Xavier / He / LeCun uniform and normal initializations are available.

Framework implements deep reinforcement learning agent that communicates with environment through defined interface. Deep agent learns via experience by taking actions through environment states and receiving rewards.

Framework supports value based (Deep Q Learning, Double Deep Q Learning, SARSA) with optional Dueling Layer, policy based (Actor Critic, Proximal Policy Optimization, Discrete Soft Actor Critic, REINFORCE, Deep Deterministic Policy Gradient) and Monte Carlo Tree Search (MCTS) based reinforcement learning algorithms. It supports online and replay buffering. It has support for multiple policies (greedy policy, epsilon greedy policy, noisy next best policy, entropy greedy policy, entropy noisy next best policy, sampled policy, multinomial, noisy).

Framework implements most typically used optimization methods starting from basic vanilla gradient descent up to more sofisticated parameter optimization methods such as Adam and AMSGrad.

Framework provides following regularization layers: drop-out, gradient clipping, L1 / L2 / Lp regularization and weight noising. Lp regularization is an experimental method and mathematically direct extension of L1 / L2 methods. Early stopping is implemented as function on neural network level.

Framework supports following normalization layers: batch normalization, layer normalization, instance normalization and weight normalization.

Framework provides accuracy metrics for regression. Additionally it provides basic metrics for classification such as accuracy, precision, recall, specificity and F1 score and calculates confusion matrix as needed. Framework also provides option to show trend and confusion matrix charts.

Framework provides libraries to read inputs from CSV, text and MIDI files, normalize, split, encode and decode data. It also has persistence support to serialize trained neural network instances into file and restore trained neural network instances from file for later use.

All feedback is welcome.