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https://github.com/augustin64/projet-tipe
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Change 'input_size' to 'size_input'
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@ -52,16 +52,16 @@ void write_image_in_network_32(int** image, int height, int width, float** input
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}
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void write_image_in_network_260(unsigned char* image, int height, int width, float*** input) {
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int input_size = 260;
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int padding = (input_size - height)/2;
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int size_input = 260;
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int padding = (size_input - height)/2;
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for (int i=0; i < padding; i++) {
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for (int j=0; j < input_size; j++) {
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for (int j=0; j < size_input; j++) {
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for (int composante=0; composante < 3; composante++) {
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input[composante][i][j] = 0.;
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input[composante][input_size-1-i][j] = 0.;
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input[composante][size_input-1-i][j] = 0.;
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input[composante][j][i] = 0.;
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input[composante][j][input_size-1-i] = 0.;
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input[composante][j][size_input-1-i] = 0.;
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}
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}
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}
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@ -48,8 +48,8 @@ void update_weights(Network* network, Network* d_network) {
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} else { // Matrice -> vecteur
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Kernel_nn* nn = k_i->nn;
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Kernel_nn* d_nn = dk_i->nn;
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int input_size = input_width*input_width*input_depth;
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for (int a=0; a<input_size; a++) {
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int size_input = input_width*input_width*input_depth;
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for (int a=0; a<size_input; a++) {
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for (int b=0; b<output_width; b++) {
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nn->weights[a][b] -= network->learning_rate * d_nn->d_weights[a][b];
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d_nn->d_weights[a][b] = 0;
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@ -148,8 +148,8 @@ void reset_d_weights(Network* network) {
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}
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} else { // Matrice -> vecteur
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Kernel_nn* nn = k_i_1->nn;
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int input_size = input_width*input_width*input_depth;
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for (int a=0; a<input_size; a++) {
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int size_input = input_width*input_width*input_depth;
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for (int a=0; a<size_input; a++) {
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for (int b=0; b<output_width; b++) {
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nn->d_weights[a][b] = 0;
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}
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