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full impl done - backprop finished

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vik 2024-11-29 11:43:24 -06:00
parent edf2373378
commit 17cdabf289
1 changed files with 55 additions and 9 deletions
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56
snn.c
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@ -18,6 +18,7 @@ typedef struct Layer {
// next layer while n is the number of neurons in the current layer
// -> exploit BLAS to matmul and get the results of the next layer
gsl_matrix* values; // the layer's values
gsl_matrix* biases; // weights for the bias. bias will always be 1
} Layer;
double uniformrandom(double low, double high) {
@ -25,10 +26,16 @@ double uniformrandom(double low, double high) {
return low + ((double)rand() / (RAND_MAX / (high - low)));
}
// activation function and it's derivative
double relu(double input, double alpha) {
return (input >= 0) ? input : (alpha * input);
}
double drelu(double input, double alpha) {
return (input >= 0) ? 1 : alpha;
}
Layer* createlayer(Layer* lprev, Layer* lnext, int neurons, gsl_matrix* nvalues) {
Layer* self = (Layer*) calloc(1, sizeof(Layer));
if (self == NULL) return NULL;
@ -38,11 +45,12 @@ Layer* createlayer(Layer* lprev, Layer* lnext, int neurons, gsl_matrix* nvalues)
self->neurons = neurons;
assert(neurons == nvalues->size1);
self->values = nvaules;
self->values = nvalues;
// setup the weights matrix
assert(lnext != NULL);
self->weights = gsl_matrix_calloc(lnext->neurons, neurons);
self->biases = gsl_matrix_calloc(lnext->neurons, 1);
// make the matrix have uniform random values from -0.5 to 0.5
gsl_matrix_set_all(self->weights, uniformrandom(-0.5, 0,5));
return self;
@ -52,13 +60,15 @@ void freelayer(Layer* layer) {
assert(layer != NULL);
if (layer->weights != NULL) gsl_matrix_free(layer->weights);
if (layer->values != NULL) gsl_matrix_free(layer->values);
if (layer->biases != NULL) gsl_matrix_free(layer->biases);
free(layer);
}
void forwardprop(Layer* layer) {
assert(layer->next != NULL);
gsl_blas_dgemm(CblasNoTrans, CblasNoTrans, 1.0, layer->weights, layer->values, 0, layer->next->values);
// layer->next->values will only ever have a single row
gsl_matrix_add(layer->next->values, layer->biases);
// layer->next->values will only ever have a single column
for(unsigned int i = 0; i < layer->next->values->size1; i++) {
double davalue = gsl_matrix_get(layer->next->values, i, 0);
gsl_matrix_set(layer->next->values, i, 0, relu(davalue, ALPHA));
@ -92,16 +102,52 @@ double msecost(Layer* layer, gsl_matrix* expected) {
void backprop(Layer* layer, gsl_matrix* expected) {
// b/c you use mse, you can just do like ouput layer - expected output (matrix subtraction)
assert(layer->previous != NULL);
// signifies this is the output layer - previous layer would be hidden layer (ideally)
gsl_matrix* deltao = gsl_matrix_alloc(layer->neurons, 1);
gsl_matrix_memcpy(deltao, layer->values);
gsl_matrix_sub(deltao, expected);
gsl_matrix* prevlayertranposed = gsl_matrix_alloc(1, layer->previous->values->size2);
if (layer->next == NULL) {
// signified this is the output layer
gsl_matrix* prevlayertranposed = gsl_matrix_alloc(layer->previous->values->size2, layer->previous->values->size1);
gsl_matrix_transpose_memcpy(prevlayertranposed, layer->previous->values);
gsl_matrix* updatedweights = gsl_matrix_alloc(layer->neurons, layer->previous->neurons);
gsl_blas_dgemm(CblasNoTrans, CblasNoTrans, 1.0, deltao, prevlayertranposed, 0.0, updatedweights);
gsl_matrix_scale(updatedweights, (double)(LEARNING_RATE * -1.00));
gsl_matrix_memcpy(layer->previous->weights, updatedweights);
// finish impl
gsl_matrix* updatedbiases = gsl_matrix_alloc(layer->neurons, 1);
gsl_matrix_memcpy(updatedbiases, deltao);
gsl_matrix_scale(updatedbiases, (double)(LEARNING_RATE * -1.00));
gsl_matrix_memcpy(layer->previous->biases, updatedbiases);
gsl_matrix_free(prevlayertranposed);
gsl_matrix_free(updatedweights);
gsl_matrix_free(updatedbiases);
} else {
// weights connecting the input layer to the hidden layer - cant do MSE trick
gsl_matrix* deltah = gsl_matrix_alloc(layer->neurons, 1);
for (unsigned int i = 0; i < layer->neurons; i++) {
gsl_matrix_set(deltah, i, 0, drelu(gsl_matrix_get(layer->values, i, 0), ALPHA));
// derivative values are set in deltah
}
gsl_matrix* weightstransposed = gsl_matrix_alloc(layer->weights->size2, layer->weights->size1);
gsl_matrix_transpose_memcpy(weightstransposed, layer->weights);
gsl_matrix* transposedweightsmultipliedbydelta = gsl_matrix_alloc(weightstransposed->size1, 1);
gsl_blas_dgemm(CblasNoTrans, CblasNoTrans, 1.0, weightstransposed, deltao, 0.0, transposedweightsmultipliedbydelta);
gsl_matrix_mul_elements(deltah, transposedweightsmultipliedbydelta);
gsl_matrix* previnputtransposed = gsl_matrix_alloc(layer->previous->values->size2, layer->previous->values->size1);
gsl_matrix_transpose_memcpy(previnputtransposed, layer->previous->values);
gsl_matrix* updatedweights = gsl_matrix_alloc(layer->neurons, layer->previous->neurons);
gsl_blas_dgemm(CblasNoTrans, CblasNoTrans, 1.0, deltah, previnputtransposed, 0.0, updatedweights);
gsl_matrix_scale(updatedweights, (double)(LEARNING_RATE * -1.00));
gsl_matrix_memcpy(layer->previous->weights, updatedweights);
gsl_matrix* updatedbiases = gsl_matrix_alloc(layer->neurons, 1);
gsl_matrix_memcpy(updatedbiases, deltah);
gsl_matrix_scale(updatedbiases, (double)(LEARNING_RATE * -1.00));
gsl_matrix_memcpy(layer->previous->biases, updatedbiases);
gsl_matrix_free(deltah);
gsl_matrix_free(weightstransposed);
gsl_matrix_free(transposedweightsmultipliedbydelta);
gsl_matrix_free(previnputtransposed);
gsl_matrix_free(updatedweights);
gsl_matrix_free(updatedbiases);
}
gsl_matrix_free(deltao);
}