1 files changed, 42 insertions, 0 deletions

diff --git a/src/lib/include/neuralnet/train.h b/src/lib/include/neuralnet/train.h
new file mode 100644
index 0000000..79f8e7b
--- /dev/null
+++ b/src/lib/include/neuralnet/train.h
@@ -0,0 +1,42 @@
+#pragma once
+
+#include <neuralnet/neuralnet.h>
+
+#include <stdbool.h>
+#include <stdint.h>
+
+typedef struct nnMatrix nnMatrix;
+
+/// Weight initialization strategy.
+///
+/// Note that regardless of strategy, a layer's weights are scaled by the
+/// layer's size. This is to avoid saturation when, e.g., using a sigmoid
+/// activation with many inputs. Thus, a (0,1) initialization is really
+/// (0,scale), for example.
+typedef enum nnWeightInitStrategy {
+  nnWeightInit01,      // (0,1) range.
+  nnWeightInit11,      // (-1,+1) range.
+  nnWeightInitNormal,  // Normal distribution.
+} nnWeightInitStrategy;
+
+/// Network training parameters.
+typedef struct nnTrainingParams {
+  R learning_rate;
+  int max_iterations;
+  uint64_t seed;
+  nnWeightInitStrategy weight_init;
+  bool debug;
+} nnTrainingParams;
+
+/// Train the network.
+///
+/// |inputs| is a matrix of inputs, one row per input and as many columns as
+/// the input's dimension.
+///
+/// |targets| is a matrix of targets, one row per target and as many columns as
+/// the target's dimension.
+void nnTrain(
+  nnNeuralNetwork*,
+  const nnMatrix* inputs,
+  const nnMatrix* targets,
+  const nnTrainingParams*);