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almost done with backprop

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vik 2024-11-28 22:05:33 -06:00
parent 8808242715
commit 2bd03dd8b8
1 changed files with 20 additions and 5 deletions
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25
snn.c
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@ -7,6 +7,8 @@
#include <gsl/gsl_cblas.h> #include <gsl/gsl_cblas.h>
#define ALPHA 0.2 #define ALPHA 0.2
#define LEARNING_RATE 0.01
typedef struct Layer { typedef struct Layer {
struct Layer* previous; struct Layer* previous;
@ -73,20 +75,33 @@ double matrixsum(gsl_matrix* matrix) {
return result; return result;
} }
double cost(Layer* layer, gsl_matrix* expected) { double msecost(Layer* layer, gsl_matrix* expected) {
// mean squared error // mean squared error cost fxn
// (for mnist at least) your expected will be a matrix of [10x1] // only on output layer
// ONLY DO THIS ON THE OUTPUT LAYER!!!!!! the layer that should be passed in is the output layer ONLY
assert(layer->values->size1 == expected->size1); assert(layer->values->size1 == expected->size1);
gsl_matrix* result = gsl_matrix_alloc(expected->size1, 1); gsl_matrix* result = gsl_matrix_alloc(expected->size1, 1);
gsl_matrix_memcpy(result, layer->values); gsl_matrix_memcpy(result, layer->values);
gsl_matrix_sub(result, expected); gsl_matrix_sub(result, expected);
gsl_matrix_mul_elements(result, result); // squares matrix gsl_matrix_mul_elements(result, result); // squares matrix
double matsum = matrixsum(result); double matsum = matrixsum(result);
gsl_matrix_free(result);
return (((double)1 / layer->neurons) * matsum); return (((double)1 / layer->neurons) * matsum);
// you dont need this for mean squared error. need this if you implement a diff cost function
} }
void backprop(Layer* layer) { 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); 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);
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);
}
} }