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#include <neuralnet/neuralnet.h>
#include <neuralnet/matrix.h>
#include "activation.h"
#include "neuralnet_impl.h"
#include <assert.h>
#include <stdlib.h>
nnNeuralNetwork* nnMakeNet(int num_layers, const int* layer_sizes, const nnActivation* activations) {
assert(num_layers > 0);
assert(layer_sizes);
assert(activations);
nnNeuralNetwork* net = calloc(1, sizeof(nnNeuralNetwork));
if (net == 0) {
return 0;
}
net->num_layers = num_layers;
net->weights = calloc(num_layers, sizeof(nnMatrix));
net->biases = calloc(num_layers, sizeof(nnMatrix));
net->activations = calloc(num_layers, sizeof(nnActivation));
if ( (net->weights == 0) || (net->biases == 0) || (net->activations == 0) ) {
nnDeleteNet(&net);
return 0;
}
for (int l = 0; l < num_layers; ++l) {
// layer_sizes = { input layer size, first hidden layer size, ...}
const int layer_input_size = layer_sizes[l];
const int layer_output_size = layer_sizes[l+1];
// We store the transpose of the weight matrix as written in textbooks.
// Our vectors are row vectors and the matrices row-major.
const int rows = layer_input_size;
const int cols = layer_output_size;
net->weights[l] = nnMatrixMake(rows, cols);
net->biases[l] = nnMatrixMake(1, cols);
net->activations[l] = activations[l];
}
return net;
}
void nnDeleteNet(nnNeuralNetwork** net) {
if ( (!net) || (!(*net)) ) {
return;
}
if ((*net)->weights != 0) {
for (int l = 0; l < (*net)->num_layers; ++l) {
nnMatrixDel(&(*net)->weights[l]);
}
free((*net)->weights);
(*net)->weights = 0;
}
if ((*net)->biases != 0) {
for (int l = 0; l < (*net)->num_layers; ++l) {
nnMatrixDel(&(*net)->biases[l]);
}
free((*net)->biases);
(*net)->biases = 0;
}
if ((*net)->activations) {
free((*net)->activations);
(*net)->activations = 0;
}
free(*net);
*net = 0;
}
void nnSetWeights(nnNeuralNetwork* net, const R* weights) {
assert(net);
assert(weights);
for (int l = 0; l < net->num_layers; ++l) {
nnMatrix* layer_weights = &net->weights[l];
R* layer_values = layer_weights->values;
for (int j = 0; j < layer_weights->rows * layer_weights->cols; ++j) {
*layer_values++ = *weights++;
}
}
}
void nnSetBiases(nnNeuralNetwork* net, const R* biases) {
assert(net);
assert(biases);
for (int l = 0; l < net->num_layers; ++l) {
nnMatrix* layer_biases = &net->biases[l];
R* layer_values = layer_biases->values;
for (int j = 0; j < layer_biases->rows * layer_biases->cols; ++j) {
*layer_values++ = *biases++;
}
}
}
void nnQuery(const nnNeuralNetwork* net, nnQueryObject* query, const nnMatrix* input) {
assert(net);
assert(query);
assert(input);
assert(net->num_layers == query->num_layers);
assert(input->rows <= query->network_outputs->rows);
assert(input->cols == nnNetInputSize(net));
for (int i = 0; i < input->rows; ++i) {
// Not mutating the input, but we need the cast to borrow.
nnMatrix input_vector = nnMatrixBorrowRows((nnMatrix*)input, i, 1);
for (int l = 0; l < net->num_layers; ++l) {
const nnMatrix* layer_weights = &net->weights[l];
const nnMatrix* layer_biases = &net->biases[l];
// Y^T = (W*X)^T = X^T*W^T
//
// TODO: If we had a row-row matrix multiplication, we could compute:
// Y^T = W ** X^T
// The row-row multiplication could be more cache-friendly. We just need
// to store W as is, without transposing.
// We could also rewrite the original Mul function to go row x row,
// decomposing the multiplication. Preserving the original meaning of Mul
// makes everything clearer.
nnMatrix output_vector = nnMatrixBorrowRows(&query->layer_outputs[l], i, 1);
nnMatrixMul(&input_vector, layer_weights, &output_vector);
nnMatrixAddRow(&output_vector, layer_biases, &output_vector);
switch (net->activations[l]) {
case nnIdentity:
break; // Nothing to do for the identity function.
case nnSigmoid:
sigmoid_array(output_vector.values, output_vector.values, output_vector.cols);
break;
case nnRelu:
relu_array(output_vector.values, output_vector.values, output_vector.cols);
break;
default:
assert(0);
}
input_vector = output_vector; // Borrow.
}
}
}
void nnQueryArray(const nnNeuralNetwork* net, nnQueryObject* query, const R* input, R* output) {
assert(net);
assert(query);
assert(input);
assert(output);
assert(net->num_layers > 0);
nnMatrix input_vector = nnMatrixMake(net->weights[0].cols, 1);
nnMatrixInit(&input_vector, input);
nnQuery(net, query, &input_vector);
nnMatrixRowToArray(query->network_outputs, 0, output);
}
nnQueryObject* nnMakeQueryObject(const nnNeuralNetwork* net, int num_inputs) {
assert(net);
assert(num_inputs > 0);
assert(net->num_layers > 0);
nnQueryObject* query = calloc(1, sizeof(nnQueryObject));
if (!query) {
return 0;
}
query->num_layers = net->num_layers;
// Allocate the intermediate layer output matrices.
query->layer_outputs = calloc(net->num_layers, sizeof(nnMatrix));
if (!query->layer_outputs) {
free(query);
return 0;
}
for (int l = 0; l < net->num_layers; ++l) {
const nnMatrix* layer_weights = &net->weights[l];
const int layer_output_size = nnLayerOutputSize(layer_weights);
query->layer_outputs[l] = nnMatrixMake(num_inputs, layer_output_size);
}
query->network_outputs = &query->layer_outputs[net->num_layers - 1];
return query;
}
void nnDeleteQueryObject(nnQueryObject** query) {
if ( (!query) || (!(*query)) ) {
return;
}
if ((*query)->layer_outputs != 0) {
for (int l = 0; l < (*query)->num_layers; ++l) {
nnMatrixDel(&(*query)->layer_outputs[l]);
}
}
free((*query)->layer_outputs);
free(*query);
*query = 0;
}
const nnMatrix* nnNetOutputs(const nnQueryObject* query) {
assert(query);
return query->network_outputs;
}
int nnNetInputSize(const nnNeuralNetwork* net) {
assert(net);
assert(net->num_layers > 0);
return net->weights[0].rows;
}
int nnNetOutputSize(const nnNeuralNetwork* net) {
assert(net);
assert(net->num_layers > 0);
return net->weights[net->num_layers - 1].cols;
}
int nnLayerInputSize(const nnMatrix* weights) {
assert(weights);
return weights->rows;
}
int nnLayerOutputSize(const nnMatrix* weights) {
assert(weights);
return weights->cols;
}
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