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compare: muellerla101249:05fbd80b8ccad330a3754968c804c95e9841add3
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2 changed files with 0 additions and 7 deletions
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info2praktikum-neuronalesnetz/neuralNetwork.c
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@ -118,10 +118,6 @@ static void assignActivations(NeuralNetwork model)
if(model.numberOfLayers > 0) if(model.numberOfLayers > 0)
model.layers[model.numberOfLayers-1].activation = softmax; model.layers[model.numberOfLayers-1].activation = softmax;
} }
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "neuralNetwork.h"
NeuralNetwork loadModel(const char *path) NeuralNetwork loadModel(const char *path)
{ {
@ -172,7 +168,6 @@ NeuralNetwork loadModel(const char *path)
return model; return model;
} }
static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count) static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
{ {
Matrix matrix = {NULL, 0, 0}; Matrix matrix = {NULL, 0, 0};
@ -259,7 +254,6 @@ unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[],
return result; return result;
} }
void clearModel(NeuralNetwork *model) void clearModel(NeuralNetwork *model)
{ {
if(model != NULL) if(model != NULL)

1
info2praktikum-neuronalesnetz/neuralNetworkTests.c
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@ -38,7 +38,6 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
fwrite(layer.biases.buffer, sizeof(MatrixType), biasCount, file); fwrite(layer.biases.buffer, sizeof(MatrixType), biasCount, file);
} }
// End-Marker (inputDim = 0) // End-Marker (inputDim = 0)
unsigned int zero = 0; unsigned int zero = 0;
fwrite(&zero, sizeof(unsigned int), 1, file); fwrite(&zero, sizeof(unsigned int), 1, file);