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#pragma once
#include <neuralnet/matrix.h>
/// Neural network object.
///
/// We store the transposes of the weight matrices so that we can do forward
/// passes with a minimal amount of work. That is, if in paper we write:
///
/// [w11 w21]
/// [w12 w22]
///
/// then the weight matrix in memory is stored as the following array:
///
/// w11 w12 w21 w22
typedef struct nnNeuralNetwork {
int num_layers; // Number of non-input layers (hidden + output).
nnMatrix* weights; // One matrix per non-input layer.
nnMatrix* biases; // One vector per non-input layer.
nnActivation* activations; // One per non-input layer.
} nnNeuralNetwork;
/// A query object that holds all the memory necessary to query a network.
///
/// |layer_outputs| is an array of matrices of intermediate layer outputs. There
/// is one matrix per intermediate layer. Each matrix holds the layer's output,
/// with one row per input, and as many columns as the layer's output size (the
/// output vector is transposed.)
///
/// |network_outputs| points to the last output matrix in |layer_outputs| for
/// convenience.
typedef struct nnQueryObject {
int num_layers;
nnMatrix* layer_outputs; // Output matrices, one output per layer.
nnMatrix* network_outputs; // Points to the last output matrix.
} nnTrainingQueryObject;
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