From 64af36ca895736fd285b3ada53030f64817eb752 Mon Sep 17 00:00:00 2001 From: Gloria Zhu Date: Fri, 19 Aug 2022 00:05:05 -0700 Subject: [PATCH] Add project files --- .gitignore | 8 + Makefile | 8 + README.MD | 56 + activationFunctions.c | 22 + bitmaps/originalfullsize.bmp | Bin 0 -> 40858 bytes bitmaps/originalreducedsize.bmp | Bin 0 -> 22256 bytes bitmaps/outputreducedsize - 200I 2000A.bmp | Bin 0 -> 22254 bytes bitmaps/outputreducedsize - 250I 250A.bmp | Bin 0 -> 22254 bytes bitmaps/outputreducedsize - 5197I 500A.bmp | Bin 0 -> 22254 bytes bitmaps/outputreducedsize.bmp | Bin 0 -> 22254 bytes configs/andorxorconfig.txt | 32 + configs/dibdumpconfig.txt | 33 + configs/xorconfig.txt | 32 + dibdump.c | 262 + errorFunctions.c | 39 + handprocessor.c | 32 + headerfiles/activationFunctions.h | 13 + headerfiles/dibdump.h | 18 + headerfiles/errorFunctions.h | 13 + headerfiles/outputFunctions.h | 20 + inputs/allinputs.txt | 21 + inputs/andinputs.txt | 13 + inputs/bitmapinputs.txt | 31386 +++++++++++++++++++ inputs/orinputs.txt | 13 + inputs/temp.txt | 1 + inputs/xorinputs.txt | 13 + network.c | 843 + outputFunctions.c | 99 + weights/placeholder.txt | 0 29 files changed, 32977 insertions(+) create mode 100644 .gitignore create mode 100644 Makefile create mode 100644 README.MD create mode 100644 activationFunctions.c create mode 100644 bitmaps/originalfullsize.bmp create mode 100644 bitmaps/originalreducedsize.bmp create mode 100644 bitmaps/outputreducedsize - 200I 2000A.bmp create mode 100644 bitmaps/outputreducedsize - 250I 250A.bmp create mode 100644 bitmaps/outputreducedsize - 5197I 500A.bmp create mode 100644 bitmaps/outputreducedsize.bmp create mode 100644 configs/andorxorconfig.txt create mode 100644 configs/dibdumpconfig.txt create mode 100644 configs/xorconfig.txt create mode 100644 dibdump.c create mode 100644 errorFunctions.c create mode 100644 handprocessor.c create mode 100644 headerfiles/activationFunctions.h create mode 100644 headerfiles/dibdump.h create mode 100644 headerfiles/errorFunctions.h create mode 100644 headerfiles/outputFunctions.h create mode 100644 inputs/allinputs.txt create mode 100644 inputs/andinputs.txt create mode 100644 inputs/bitmapinputs.txt create mode 100644 inputs/orinputs.txt create mode 100644 inputs/temp.txt create mode 100644 inputs/xorinputs.txt create mode 100644 network.c create mode 100644 outputFunctions.c create mode 100644 weights/placeholder.txt diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..20d1c4a --- /dev/null +++ b/.gitignore @@ -0,0 +1,8 @@ +*.exe +*.zip +*.o +.vscode +*.java +*.classgit +weightsdump.txt +hands/ \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..0675c32 --- /dev/null +++ b/Makefile @@ -0,0 +1,8 @@ +CC=gcc +CFLAGS=-I. +DEPS = network.c outputFunctions.c errorFunctions.c activationFunctions.c dibdump.c + +%.o: %.c $(DEPS) + $(CC) -o $@ $^ $(CFLAGS) + +makenet: network.o outputFunctions.o errorFunctions.o activationFunctions.o dibdump.o \ No newline at end of file diff --git a/README.MD b/README.MD new file mode 100644 index 0000000..c56681c --- /dev/null +++ b/README.MD @@ -0,0 +1,56 @@ +# Project Info +This is a set of files that runs a basic neural network. +The main file is `network.c`, and the other files +provide miscellaneous utilities: + `outputFunctions.c` - stores output functions for use in the network + `activationFunctions.c` - stores activation functions for use in the network + `errorFunctions.c` - stores error functions for use in the network + `dibdump.c` - stores utility functions for use with bitmap i/o + +Configuration values should be set in a .txt file whose path +is specified during runtime. + +# Running the network + + ``` + $ gcc -o network network.c outputFunctions.c errorFunctions.c activationFunctions.c dibdump.c + $ network.exe + ``` +to compile and run the network; enter the path to the config when prompted. + +# Config Structure + +``` +num_input_nodes 2 +num_hidden_layers 1 +num_output_nodes 3 + +hidden_layer_1_size 10 // add lines below this for more hidden layers + +trainNetwork Y // whether to train or just run +print_network_specifics Y // whether to print the specific values of the network +print_debug_messages Y // whether to print debug messages while running/training + +use_bitmap Y // whether or not to train on a bitmap +original_bitmap_file ./input.bmp // if so, input bitmap file +output_bitmap_file ./output.bmp // if so, output bitmap file + +training_sets_file ./inputs.txt // file to store input values (bitmap or not) +where_to_dump_outputs ./outputs.txt // where to dump final/periodic output values +randomize_weights Y // whether to randomize weights +random_weights_lower -0.5 // if randomize: lower bound +random_weights_upper 0.5 // if randomize: upper bound +preset_weights_file ./weights.txt // if not randomize: where to read weights from +where_to_dump_weights ./weightsdump.txt // where to dump final/periodic weights +dump_every_x_iterations 100 // dump weights/nodes every _x_ iterations + +initial_learning_factor 0.1 +learning_factor_scaler 2.0 // set this to 1.0 to disable adaptive learning +min_learning_factor 0.001 // lower and upper bounds +max_learning_factor 2.0 // for the learning factor +enable_weight_rollback Y // whether or not to enable weight rollback + +max_training_iterations 100000 // max # of iterations before stopping training +initial_error 1.0 // what value to initialize the error at +target_training_error 0.00001 // target training error (to stop at) +``` \ No newline at end of file diff --git a/activationFunctions.c b/activationFunctions.c new file mode 100644 index 0000000..729489b --- /dev/null +++ b/activationFunctions.c @@ -0,0 +1,22 @@ +/** + * Gloria Zhu + * Created 9/8/2019 + * This file holds a collection of activation functions to be used. + * + * Functions in this file: + * + * identity + */ + +#include +#include + +#include "./headerfiles/activationFunctions.h" + 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z1H1wy##7@w6Mg$VZdpyG&%%RjT0%Z`mQ=Fo&C;+x#?{WgM!2RWi{4HiR;YRNQq$(3 zw0$f7c-zunjvF(W!Oxskj+#yFIT^^bS;6h4Z_&W^hZO!N2uqi}LUTwOD_&^@t0#}iFMBvuStN0H^=OLq z)|H!>Ia%=bWD2y7PlBL9oB6tN2P`74V{6_;Y>-)vaJ^jeH@XhFWf>IazmL4sxA9I& z-K1Ww{^&6mum(GCLu;J}Z%^khj9O<(caJMVF`4_9KM$9flo` z*2~Jc-@@^2&G@dH42|RGxGV}K3O+Cs`pi`_Z7PAx_cy$!q4Vgcn+Qe;iIQQ;@;Ep7 z1)EhD%U(5KflqpqnPx64EhywJyo`F_lDVAnsjSDdMJp#$~5sXCgTK<9M%SkLe5J8&8 zaLW@wO?JF2nTlY{PhnoxIPwKSD-^d}Y-Vr1Um#~!Bq1E5sV!)~bY1XIFq{A9*k$42 z1x*6azL|pi2Q39v{g(U=yYoWR`HjMcf7XKXGHu}*@e-lm@@oW-JG_N+w0wmVhQ12*8y*VhBpC|b##;zYZ|o2>x)t+JcFPOC*tiJ8 zT#w6t-#JmZX6#L&LWsSfuS!|?K*mbWbNxvLy@iXERp)(@f1y+?H`e1if2W?fa6S^{ zW`^Vnl^ +#include +#include +#include + +#include "./headerfiles/dibdump.h" + +/** + * This function reads in a bitmap and writes out its pels to a text file. + * Note: this will only run for 24-bit and 32-bit bitmaps (that don't have a color table) + * + * @param inFileName the name of the input bitmap file + * @param pelsOutputFile the file to output the pel values to + * @return a pointer to the array of pels (in blue|green|red|reserved byte form) + */ +int *readBitmap(char *inFileName, char *pelsOutputFile) +{ + BITMAPFILEHEADER bmpFileHeader; + BITMAPINFOHEADER bmpInfoHeader; + + char topDownDIB = 'n'; + + unsigned int *pels; // using unsigned int to prevent overflow errors + + FILE *inFile = fopen(inFileName, "rb"); + + if (inFile != NULL) + { + fread((char *)&bmpFileHeader, sizeof(BITMAPFILEHEADER), 1, inFile); + + printf("bfType=%2X\nbfSize=%lu\nbfReserved1=%u\nbfReserved2=%u\nbfOffBits=%lu\n", + bmpFileHeader.bfType, + bmpFileHeader.bfSize, + bmpFileHeader.bfReserved1, + bmpFileHeader.bfReserved2, + bmpFileHeader.bfOffBits); + + fread((char *)&bmpInfoHeader, sizeof(BITMAPINFOHEADER), 1, inFile); + + printf("biSize=%lu\nbiWidth=%ld\nbiHeight=%ld\nbiPlanes=%u\nbiBitCount=%u\nbiCompression=%lu\nbiSizeImage=%lu\nbiXPelsPerMeter=%ld\nbiYPelsPerMeter=%ld\nbiClrUsed=%lu\nbiClrImportant=%lu\n\n", + bmpInfoHeader.biSize, + bmpInfoHeader.biWidth, + bmpInfoHeader.biHeight, + bmpInfoHeader.biPlanes, + bmpInfoHeader.biBitCount, + bmpInfoHeader.biCompression, + bmpInfoHeader.biSizeImage, + bmpInfoHeader.biXPelsPerMeter, + bmpInfoHeader.biYPelsPerMeter, + bmpInfoHeader.biClrUsed, + bmpInfoHeader.biClrImportant); + + int biHeightAbs = bmpInfoHeader.biHeight; + + if (bmpInfoHeader.biHeight < 0) + { + topDownDIB = 'Y'; + biHeightAbs = -biHeightAbs; + } + + // allocating space for the pels + pels = malloc(bmpInfoHeader.biWidth * biHeightAbs * sizeof(int)); + + int bytesPerPixel = bmpInfoHeader.biBitCount / 8; + + if (bytesPerPixel == 4) // 32-bit bitmap + { + fread(pels, bytesPerPixel, bmpInfoHeader.biWidth * biHeightAbs, inFile); + + for (int i = 0; i < bmpInfoHeader.biWidth * biHeightAbs; i++) + { + unsigned int pel = pels[i]; + + // pels are read in reserved|red|green|blue order + int blue = pel & 0x00FF; + int green = (pel >> 8) & 0x00FF; + int red = (pel >> 16) & 0x00FF; + int reserved = (pel >> 24) & 0x00FF; + // rearranging pels into blue|green|red|reserved order + unsigned int newpel = blue << 24 | green << 16 | red << 8 | reserved; + + pels[i] = newpel; + } + + writePelsToTextFile(pels, bmpInfoHeader.biWidth * biHeightAbs, pelsOutputFile); + } + else if (bytesPerPixel == 3) // 24-bit bitmap + { + fread(pels, bytesPerPixel, bmpInfoHeader.biWidth * biHeightAbs, inFile); + + writePelsToTextFile(pels, bmpInfoHeader.biWidth * biHeightAbs, pelsOutputFile); + } + else // if the bitmap is not 24 or 32 bit (bytesPerPixel is not 3 or 4) + { + fprintf(stderr, "ERROR: Bitmap must be 24-bit or 32-bit."); + } + } + else // if inFile == NULL + { + fprintf(stderr, "INPUT ERROR: %s\n", strerror(errno)); + } + + return pels; +} + +/** + * This function writes an array of pels to a text file in hexadecimal form. + * It concatenates a 1 to the front and two copies of the pels to fit + * the input txt file format of the network. + * + * @param pels array of the pel values + * @param numPels the total number of pels + * @param pelsOutputFile the file to write the pels to + */ +void writePelsToTextFile(unsigned int *pels, int numPels, char *pelsOutputFile) +{ + printf("Writing pels to text file...\n"); + FILE *outFile = fopen(pelsOutputFile, "a+b"); + + // fprintf(outFile, "1\n"); uncomment me for original code submission + + for (int i = 0; i < numPels; i++) + { + fprintf(outFile, "%x\n", pels[i]); + } + + for (int i = 0; i < numPels; i++) + { + fprintf(outFile, "%x\n", pels[i]); + } + + fclose(outFile); + + printf("Finished writing %d pels to text file %s\n", numPels, pelsOutputFile); + + return; +} + +/** + * This function transfers from pel .txt file to a bitmap, using + * the original bitmap file as a reference. It is responsible for + * reading in the file/info headers of the original DIB file, and + * then passing those values + the pels to a helper function. + * + * @param pelsOutputFile the file to read pels in from + * @param originalDIBFile the original bitmap file to reference for header/info values + * @param outputDIBFile the bitmap file to output to + */ +void writeBitmap(char *pelsOutputFile, char *originalDIBFile, char *outputDIBFile) +{ + FILE *origFile = fopen(originalDIBFile, "rb"); + FILE *pelsFile = fopen(pelsOutputFile, "r"); + + unsigned int *pels; + + if (origFile != NULL) + { + BITMAPFILEHEADER bmpFileHeader; + BITMAPINFOHEADER bmpInfoHeader; + + fread((char *)&bmpFileHeader, sizeof(BITMAPFILEHEADER), 1, origFile); + fread((char *)&bmpInfoHeader, sizeof(BITMAPINFOHEADER), 1, origFile); + + int biHeightAbs = bmpInfoHeader.biHeight; + char topDownDIB = 'n'; + + if (bmpInfoHeader.biHeight < 0) + { + topDownDIB = 'Y'; + biHeightAbs = -biHeightAbs; + } + + pels = malloc(bmpInfoHeader.biWidth * biHeightAbs * sizeof(int)); + + unsigned int pel; + + for (int i = 0; i < bmpInfoHeader.biWidth * biHeightAbs; i++) + { + fscanf(pelsFile, "%x", &pel); + pels[i] = pel; + } + + writeBitmapHelper(outputDIBFile, pels, bmpInfoHeader.biWidth * biHeightAbs, bmpFileHeader, bmpInfoHeader); + + fclose(origFile); + fclose(pelsFile); + + printf("Finished writing pels from %s to bitmap file %s based on original bitmap %s\n", + pelsOutputFile, + outputDIBFile, + originalDIBFile); + + } // if (origFile == NULL) + + return; +} + +/** + * This function transfers from pel .txt file to a bitmap, having been + * given the file/info headers of the original bitmap file. + * + * @param outFileName the name of the output dibdump file + * @param pels the array of pels + * @param numPels the total number of pels + * @param bmpFileHeader the bitmap file header to use + * @param bmpInfoHeader the bitmap info header to use + */ +void writeBitmapHelper( + char *outFileName, + unsigned int *pels, + int numPels, + BITMAPFILEHEADER bmpFileHeader, + BITMAPINFOHEADER bmpInfoHeader) +{ + + FILE *outFile = fopen(outFileName, "wb"); + + unsigned int pel; + for (int i = 0; i < numPels; i++) + { + pel = pels[i]; + int reserved = pel & 0x00FF; + int red = (pel >> 8) & 0x00FF; + int green = (pel >> 16) & 0x00FF; + int blue = (pel >> 24) & 0x00FF; + // rearranging the pels back into their proper structure + unsigned int newpel = reserved << 24 | red << 16 | green << 8 | blue; + + pels[i] = newpel; + } + + if (outFile != NULL) + { + fwrite(&bmpFileHeader, sizeof(BITMAPFILEHEADER), 1, outFile); + fwrite(&bmpInfoHeader, sizeof(BITMAPINFOHEADER), 1, outFile); + fwrite(pels, 4, numPels, outFile); + fclose(outFile); + + printf("Finished writing %d pels to bitmap output %s\n", numPels, outFileName); + } + else + { + fprintf(stderr, "OUTPUT ERROR: %s\n", strerror(errno)); + } + + return; +} diff --git a/errorFunctions.c b/errorFunctions.c new file mode 100644 index 0000000..12523a4 --- /dev/null +++ b/errorFunctions.c @@ -0,0 +1,39 @@ +/** + * Gloria Zhu + * Created 9/8/2019 + * This file holds a collection of error functions to be used + * for evaluating error in a neural network. Note that every + * function here should take in as parameters two arrays of + * float values of the same length, the first being the + * expected output and the second being the actual output, + * as well as an integer that is the length of the two arrays. + * + * Functions in this file: + * + * quadraticLoss + */ + +#include +#include + +#include "./headerfiles/errorFunctions.h" + +/** + * The quadratic loss error function returns half the sum of the squares of the + * differences between expected outputs and actual outputs. + * + * @param expectedOutput array of the expected outputs + * @param actualOutput array of the actual outputs + * @param arrayLength the number of outputs to compare and use to calculate error + */ +double quadraticLoss(double expectedOutput[], double actualOutput[], int arrayLength) +{ + double error = 0.0; + for (int i = 0; i < arrayLength; i++) + { + double deviation = expectedOutput[0] - actualOutput[0]; + error += deviation * deviation; + } + return 0.5 * error; +} + diff --git a/handprocessor.c b/handprocessor.c new file mode 100644 index 0000000..8e67465 --- /dev/null +++ b/handprocessor.c @@ -0,0 +1,32 @@ +#include +#include +#include + +#include "./headerfiles/outputFunctions.h" // importing output, +#include "./headerfiles/activationFunctions.h" // activation, and +#include "./headerfiles/errorFunctions.h" // error functions + +#include "./headerfiles/dibdump.h" // importing dibdump functions + +#define MAX_FILE_NAME_LENGTH 2048 // max characters in a file name + +#define NUM_BITMAPS_TO_PROCESS 5 + +int main() +{ + char * pelsOutputFile = "./inputs/bitmapinputs.txt"; + + FILE *outFile = fopen(pelsOutputFile, "a"); + fprintf(outFile, "5\n"); + + readBitmap("./hands/Gloria1.bmp", pelsOutputFile); + fprintf(outFile, "1\n0\n0\n0\n0\n"); + readBitmap("./hands/Gloria2.bmp", pelsOutputFile); + fprintf(outFile, "0\n1\n0\n0\n0\n"); + readBitmap("./hands/Gloria3.bmp", pelsOutputFile); + fprintf(outFile, "0\n0\n1\n0\n0\n"); + readBitmap("./hands/Gloria4.bmp", pelsOutputFile); + fprintf(outFile, "0\n0\n0\n1\n0\n"); + readBitmap("./hands/Gloria5.bmp", pelsOutputFile); + fprintf(outFile, "0\n0\n0\n0\n1\n"); +} \ No newline at end of file diff --git a/headerfiles/activationFunctions.h b/headerfiles/activationFunctions.h new file mode 100644 index 0000000..1bf5632 --- /dev/null +++ b/headerfiles/activationFunctions.h @@ -0,0 +1,13 @@ +/** + * Gloria Zhu + * Created 9/15/2019 + * This file contains the header files for activation functions. + * More specific documentation can be found in the source file. + */ + +#ifndef activationFunctions_h +#define activationFunctions_h + +double identity(double); + +#endif \ No newline at end of file diff --git a/headerfiles/dibdump.h b/headerfiles/dibdump.h new file mode 100644 index 0000000..2c0ff58 --- /dev/null +++ b/headerfiles/dibdump.h @@ -0,0 +1,18 @@ +/** + * Gloria Zhu + * Created 11/15/2019 + * This file contains the header files for dibdump-related functions. + * More specific documentation can be found in the source file. + */ + +#ifndef dibdump_h +#define dibdump_h + +#include + +int *readBitmap(char *, char *); +void writePelsToTextFile(unsigned int *, int, char *); +void writeBitmap(char *, char *, char *); +void writeBitmapHelper(char *, unsigned int *, int, BITMAPFILEHEADER, BITMAPINFOHEADER); + +#endif \ No newline at end of file diff --git a/headerfiles/errorFunctions.h b/headerfiles/errorFunctions.h new file mode 100644 index 0000000..2cdee1f --- /dev/null +++ b/headerfiles/errorFunctions.h @@ -0,0 +1,13 @@ +/** + * Gloria Zhu + * Created 9/15/2019 + * This file contains the header files for error functions. + * More specific documentation can be found in the source file. + */ + +#ifndef errorFunctions_h +#define errorFunctions_h + +double quadraticLoss(double[], double[], int); + +#endif \ No newline at end of file diff --git a/headerfiles/outputFunctions.h b/headerfiles/outputFunctions.h new file mode 100644 index 0000000..87eac52 --- /dev/null +++ b/headerfiles/outputFunctions.h @@ -0,0 +1,20 @@ +/** + * Gloria Zhu + * Created 9/15/2019 + * This file contains the header files for output functions. + * More specific documentation can be found in the source file. + */ + +#ifndef outputFunctions_h +#define outputFunctions_h + +double sigmoid(double); +double sigmoidDeriv(double); + +double tanh(double); +double tanhDeriv(double); + +double relu(double); +double reluDeriv(double); + +#endif \ No newline at end of file diff --git a/inputs/allinputs.txt b/inputs/allinputs.txt new file mode 100644 index 0000000..97f266a --- /dev/null +++ b/inputs/allinputs.txt @@ -0,0 +1,21 @@ +4 +0 +0 +0 +0 +0 +0 +1 +0 +1 +1 +1 +0 +0 +1 +1 +1 +1 +1 +1 +0 \ No newline at end of file diff --git a/inputs/andinputs.txt b/inputs/andinputs.txt new file mode 100644 index 0000000..5606db2 --- /dev/null +++ b/inputs/andinputs.txt @@ -0,0 +1,13 @@ +4 +0 +0 +0 +0 +1 +0 +1 +0 +0 +1 +1 +1 \ No newline at end of file diff --git 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+0 +1 +0 +0 +0 +1 +0 +0 +0 +0 +5 diff --git a/inputs/orinputs.txt b/inputs/orinputs.txt new file mode 100644 index 0000000..2f58fcd --- /dev/null +++ b/inputs/orinputs.txt @@ -0,0 +1,13 @@ +4 +0 +0 +0 +1 +0 +1 +0 +1 +1 +1 +1 +1 \ No newline at end of file diff --git a/inputs/temp.txt b/inputs/temp.txt new file mode 100644 index 0000000..5bc24a9 --- /dev/null +++ b/inputs/temp.txt @@ -0,0 +1 @@ +7fffffff diff --git a/inputs/xorinputs.txt b/inputs/xorinputs.txt new file mode 100644 index 0000000..1a2e301 --- /dev/null +++ b/inputs/xorinputs.txt @@ -0,0 +1,13 @@ +4 +0 +0 +0 +0 +1 +1 +1 +0 +1 +1 +1 +0 \ No newline at end of file diff --git a/network.c b/network.c new file mode 100644 index 0000000..278aaad --- /dev/null +++ b/network.c @@ -0,0 +1,843 @@ +/** + * Gloria Zhu + * Created 9/7/2019 + * This file defines and runs a network. + * + * Functions in this file: + * + * void parseConfig(void) + * void takeDimensionInputs(void) + * void takeTrainingSetsInputs(void) + * void initializeWeightsFromFile(void) + * void initializeWeightsRandomly(double, double) + * double randomNumber(double, double) + * void writeWeightsToFile(void) + * void writeOutputsToFile(void) + * void calculateNumNodesAndWeights(void) + * void freeMemory(void) + * + * void printWeights(void) + * void printNetworkConfiguration(void) + * void runNetwork(void) + * + * double calculateError(void) + * void runForAllTrainingSets(void); + * void trainForAllTrainingSets(void); + * void train(int, double); + */ + +#include +#include +#include +#include // need this library to get unique seed (current unix time) for rng + +#include "./headerfiles/outputFunctions.h" // importing output, +#include "./headerfiles/activationFunctions.h" // activation, and +#include "./headerfiles/errorFunctions.h" // error functions + +#include "./headerfiles/dibdump.h" // importing dibdump functions + +#define MAX_FILE_NAME_LENGTH 2048 // max characters in a file name +#define UNSIGNED_INT_SCALER 4294967295.0 // used for scaling the pels to [0,1] + +/** + * This function pointer refers to the output function + * to be used in determining a node's output according + * to its activation level. Output functions are defined + * in ./outputFunctions.c and included in this file. + */ +double (*outputFunction)(double value) = &sigmoid; // set the output function here + +/** + * This function pointer refers to the output derivative function + * to be used in determining partial derivatives. They are defined + * in ./outputFunctions.c and included in this file. + */ +double (*outputDerivFunction)(double value) = &sigmoidDeriv; // set the output derivative function here + +/** + * This function pointer refers to the activation function + * to be used in calculating the activation of a unit according + * to input. Activation functions are + * defined in ./activationFunctions.c and included in this file. + */ +double (*activationFunction)(double input) = &identity; // set the activation function here + +/** + * This function pointer refers to the error function + * to be used in calculating error. Error functions are + * defined in ./errorFunctions.c and included in this file. + */ +double (*errorFunction)( + double expectedOutput[], + double actualOutput[], + int numNodes) = &quadraticLoss; // set the error function here + +// function headers ---------------------- + +// functions that handle utility tasks like i/o and mem allocation +void parseConfig(void); +void takeDimensionInputs(void); +void takeTrainingSetsInputs(void); +void initializeWeightsFromFile(void); +void initializeWeightsRandomly(double, double); +double randomNumber(double, double); +void writeWeightsToFile(void); +void writeOutputsToFile(void); +void calculateNumNodesAndWeights(void); +void freeMemory(void); + +// functions for printing and debugging +void printWeights(void); +void printNetworkConfiguration(void); + +// functions that run/train the network +void runNetwork(void); +double calculateError(void); +void runForAllTrainingSets(void); // does not train +void trainForAllTrainingSets(void); // helper function +void train(int, double); + +// variable declarations ---------------------- + +// variables that describe the structure of the network +char configFilename[MAX_FILE_NAME_LENGTH]; + +int numLayers; +int numHiddenLayers; +int numInputNodes; +int *layerDimensions; +int numOutputNodes; + +char useRandomWeights; + +// arrays that hold the actual values of the network +double *nodes; +double *weights; +double *expectedOutputs; + +// backprop arrays +double *thetas; +double *psis; + +// calculated values related to the structure of the network +int totalWeights; +int maxNodesInALayer; +int maxWeightsInALayer; + +// file paths for i/o files +char weightsFileInput[MAX_FILE_NAME_LENGTH]; +char weightsFileOutput[MAX_FILE_NAME_LENGTH]; + +char nodesFileInput[MAX_FILE_NAME_LENGTH]; +char nodesFileOutput[MAX_FILE_NAME_LENGTH]; +int numTrainingSets; + +char useBitmap; +char bitmapFileInput[MAX_FILE_NAME_LENGTH]; +char bitmapFileOutput[MAX_FILE_NAME_LENGTH]; + +// values related to training +char trainNetwork; // whether or not to train (Y for yes, anything else for no) +char printNetworkSpecifics; // whether or not to print the specific values of the network +char printDebugMessages; // whether or not to print debug messages +char enableWeightRollback; // whether or not to enable weight rollback + +double *trainingSets; // stores training set values + +double error; // current error of network (set to some initial config value) +double learningFactor; // current lambda value +double learningFactorScaler; // lambda scaler + +double minLearningFactor; // lower bound for lambda value +double maxLearningFactor; // upper bound for lambda value + +int dumpEveryIterations; // dump weights/outputs every _x_ iterations + +int maxIterations; // max number of iterations before stopping +double targetError; // training stops when error reaches this value + +/** + * The main function makes the actual calls that complete parts + * of the process of running a neural network. + */ +int main() +{ + printf("What config file should I use? "); + scanf("%s", &configFilename); + parseConfig(); + + printf("\nINITIAL NETWORK:\n"); + runForAllTrainingSets(); + + clock_t CPU_time_1 = clock(); + + if (trainNetwork == 'Y') + { + printf("AFTER TRAINING:\n"); + train(maxIterations, targetError); + } + + clock_t CPU_time_2 = clock(); + + writeWeightsToFile(); + writeOutputsToFile(); + + if (useBitmap == 'Y') + { + writeBitmap(nodesFileOutput, bitmapFileInput, bitmapFileOutput); + } + + freeMemory(); + + printf("Time taken: %fms", ((double)(CPU_time_2 - CPU_time_1)) / CLOCKS_PER_SEC * 1000); + + return 0; +} + +/** + * This function parses in all of the network's options through the + * config file (see the README for details on the options). + */ +void parseConfig() +{ + FILE *config = fopen(configFilename, "r"); + + char dummy[MAX_FILE_NAME_LENGTH]; // dummy value for handling the descriptor strings in the config file + + fscanf(config, "%s", &dummy); + fscanf(config, "%d", &numInputNodes); // reading in number of input nodes + + fscanf(config, "%s", &dummy); + fscanf(config, "%d", &numHiddenLayers); // reading in number of hidden layers + + fscanf(config, "%s", &dummy); + fscanf(config, "%d", &numOutputNodes); // reading in number of output nodes + + numLayers = numHiddenLayers + 2; // setting some network structure values + layerDimensions = calloc(numLayers, sizeof(int)); + layerDimensions[0] = numInputNodes; + layerDimensions[numLayers - 1] = numOutputNodes; + + for (int i = 0; i < numHiddenLayers; i++) // taking in hidden layer dimensions + { + fscanf(config, "%s", &dummy); + fscanf(config, "%d", layerDimensions + i + 1); + } + + fscanf(config, "%s", &dummy); + fscanf(config, "%s", &trainNetwork); // whether to train or just run instead + + fscanf(config, "%s", &dummy); + fscanf(config, "%s", &printNetworkSpecifics); // whether or not to print network specifics + + fscanf(config, "%s", &dummy); + fscanf(config, "%s", &printDebugMessages); // whether or not to print debug messages + + calculateNumNodesAndWeights(); // calculating some useful values + + nodes = malloc(maxNodesInALayer * numLayers * sizeof(double)); // allocating memory + if (nodes == NULL) + { + printf("There was an error allocating memory for nodes.\n"); + } + weights = malloc(maxWeightsInALayer * numLayers * sizeof(double)); + if (weights == NULL) + { + printf("There was an error allocating memory for weights.\n"); + } + expectedOutputs = malloc(numOutputNodes * sizeof(double)); + if (expectedOutputs == NULL) + { + printf("There was an error allocating memory for expected outputs.\n"); + } + + thetas = malloc(maxNodesInALayer * numLayers * sizeof(double)); + if (thetas == NULL) + { + printf("There was an error allocating memory for thetas.\n"); + } + psis = malloc(maxNodesInALayer * numLayers * sizeof(double)); + if (psis == NULL) + { + printf("There was an error allocating memory for psis.\n"); + } + + fscanf(config, "%s", &dummy); + fscanf(config, "%s", &useBitmap); // whether or not to use bitmaps + printf("use bitmap? %c\n", useBitmap); + + fscanf(config, "%s", &dummy); + fscanf(config, "%s", &bitmapFileInput); // input bitmap file + printf("bitmap input: %s\n", bitmapFileInput); + + fscanf(config, "%s", &dummy); + fscanf(config, "%s", &bitmapFileOutput); // outputted bitmap file + printf("bitmap output: %s\n", bitmapFileOutput); + + fscanf(config, "%s", &dummy); + fscanf(config, "%s", nodesFileInput); // reading in training sets + printf("nodes input: %s\n", nodesFileInput); + + if (useBitmap == 'Y') + { + readBitmap(bitmapFileInput, nodesFileInput); + } + + takeTrainingSetsInputs(); + + fscanf(config, "%s", &dummy); + fscanf(config, "%s", nodesFileOutput); // where it would dump output values + printf("nodes output: %s\n", nodesFileOutput); + + fscanf(config, "%s", &dummy); + fscanf(config, "%s", &useRandomWeights); // whether or not to randomize weights + printf("use random weights? %c\n", useRandomWeights); + + double randomWeightsLowerBound; + double randomWeightsUpperBound; + + fscanf(config, "%s", &dummy); + fscanf(config, "%lf", &randomWeightsLowerBound); // reading in randomized weights' lower bound + + fscanf(config, "%s", &dummy); + fscanf(config, "%lf", &randomWeightsUpperBound); // reading in randomized weights' lower bound + + fscanf(config, "%s", &dummy); + fscanf(config, "%s", &weightsFileInput); // where it would read preset weights from + printf("weights input: %s\n", weightsFileInput); + + fscanf(config, "%s", &dummy); + fscanf(config, "%s", &weightsFileOutput); // where it would dump weights to + printf("weights output: %s\n", weightsFileOutput); + + fscanf(config, "%s", &dummy); + fscanf(config, "%d", &dumpEveryIterations); // where it would dump weights to + + if (useRandomWeights == 'Y') + { + initializeWeightsRandomly(randomWeightsLowerBound, randomWeightsUpperBound); + } + else + { + initializeWeightsFromFile(); + } + + fscanf(config, "%s", &dummy); + fscanf(config, "%lf", &learningFactor); // reading in initial learning factor + printf("learning factor: %lf\n", learningFactor); + + fscanf(config, "%s", &dummy); + fscanf(config, "%lf", &learningFactorScaler); // reading in learning factor scaler + printf("learning factor scaler: %lf\n", learningFactorScaler); + + fscanf(config, "%s", &dummy); + fscanf(config, "%lf", &minLearningFactor); // reading in minimum allowed learning factor + + fscanf(config, "%s", &dummy); + fscanf(config, "%lf", &maxLearningFactor); // reading in maximum allowed learning factor + + fscanf(config, "%s", &dummy); + fscanf(config, "%s", &enableWeightRollback); // whether or not to enable weight rollback + + fscanf(config, "%s", &dummy); + fscanf(config, "%d", &maxIterations); // reading in max iterations for training + + fscanf(config, "%s", &dummy); + fscanf(config, "%lf", &error); // reading in initial error + + fscanf(config, "%s", &dummy); + fscanf(config, "%lf", &targetError); // reading in target error + + fclose(config); +} + +/** + * This function allocates space for all the training sets + * according to the number of training sets (first line of + * the input file) and the number of input and output nodes + * (set in the config file). + * It then reads in the values and stores them. + */ +void takeTrainingSetsInputs() +{ + if (useBitmap == 'Y') // take input from a bitmap + { + unsigned int node = 0.0; + FILE *nodesFile = fopen(nodesFileInput, "r"); + + fscanf(nodesFile, "%x", &numTrainingSets); + + printf("num training sets: %d\n", numTrainingSets); + + trainingSets = calloc(numTrainingSets * (numInputNodes + numOutputNodes), sizeof(double)); + + for (int i = 0; i < numTrainingSets * (numInputNodes + numOutputNodes); i++) + { + fscanf(nodesFile, "%x", &node); + // printf("%u divided by %lf is %lf\n", (unsigned int) node, UNSIGNED_INT_SCALER, ((double)node) / UNSIGNED_INT_SCALER); + *(trainingSets + i) = ((double)node) / UNSIGNED_INT_SCALER; + } + + fclose(nodesFile); + } + else // take input from a pre-setup file + { + double node = 0.0; + FILE *nodesFile = fopen(nodesFileInput, "r"); + + fscanf(nodesFile, "%x", &numTrainingSets); + + trainingSets = calloc(numTrainingSets * (numInputNodes + numOutputNodes), sizeof(double)); + + for (int i = 0; i < numTrainingSets * (numInputNodes + numOutputNodes); i++) + { + fscanf(nodesFile, "%lf", &node); + *(trainingSets + i) = node; + } + + fclose(nodesFile); + } + + return; +} + +/** + * This function initializes the weights to known values from + * a file. Weights are stored in mkj order. + */ +void initializeWeightsFromFile() +{ + double weight = 0.0; + FILE *weightsFile = fopen(weightsFileInput, "r"); + + for (int i = 0; i < totalWeights; i++) + { + fscanf(weightsFile, "%lf", &weight); + *(weights + i) = weight; + } + + fclose(weightsFile); + + return; +} + +/** + * Initializes all weights randomly to values between given bounds. + * Randomization uses the current time as its seed. + * + * @param lowerBound the lower bound of the randomized weights + * @param upperBound the upper bound of the randomized weights + */ +void initializeWeightsRandomly(double lowerBound, double upperBound) +{ + srand(time(0)); + for (int m = 0; m < numLayers - 1; m++) + { + for (int j = 0; j < layerDimensions[m]; j++) + { + for (int k = 0; k < layerDimensions[m + 1]; k++) + { + unsigned int index = m * maxWeightsInALayer + j * maxNodesInALayer + k; + double randWeight = randomNumber(lowerBound, upperBound); + + weights[index] = randWeight; + } + } + } + printf("Finished initializing weights\n"); + return; +} + +/** + * @return a random number between a given lower and upper bound + * + * @param lowerBound the lower bound of the randomized weights + * @param upperBound the upper bound of the randomized weights + */ +double randomNumber(double lowerBound, double upperBound) { + return (double)rand() / (double)RAND_MAX * (upperBound - lowerBound) + lowerBound; +} + +/** + * This function write the current weights to a file. + * Weights are stored in mkj order. + */ +void writeWeightsToFile() +{ + FILE *weightsFile = fopen(weightsFileOutput, "w+"); + + for (int i = 0; i < totalWeights; i++) + { + fprintf(weightsFile, "%lf\n", weights[i]); + } + + fclose(weightsFile); + + return; +} + +/** + * This function writes all the current outputs to a specified file. + */ +void writeOutputsToFile() +{ + FILE *outFile = fopen(nodesFileOutput, "w"); + + for (int i = 0; i < numOutputNodes; i++) + { + fprintf(outFile, "%x\n", (unsigned int)(nodes[maxNodesInALayer * (numLayers - 1) + i] * UNSIGNED_INT_SCALER)); + } + + fclose(outFile); + + return; +} + +/** + * This function is responsible for calculating the maximum nodes + * and maximum weights in a layer. These values are used for + * allocating space. + */ +void calculateNumNodesAndWeights() +{ + for (int layer = 0; layer < numLayers; layer++) + { + // maintaining maxNodesInALayer + if (layerDimensions[layer] > maxNodesInALayer) + { + maxNodesInALayer = layerDimensions[layer]; + } + } + + maxWeightsInALayer = maxNodesInALayer * maxNodesInALayer; + totalWeights = maxWeightsInALayer * (numLayers - 1); + + return; +} + +/** + * This function actually runs the network (which is assumed + * to have already been initialized with inputs and weights). + * It does not do any error calculation or training and merely + * propagates values throughout the nodes, while collecting + * theta values to be used in backprop. + */ +void runNetwork() +{ + for (int m = 0; m < numLayers - 1; m++) // looping through connectivity layers + { + int numSourceNodes = layerDimensions[m]; + int numDestNodes = layerDimensions[m + 1]; + + for (int j = 0; j < numDestNodes; j++) // looping through right layer + { + int destNodeIndex = (m + 1) * maxNodesInALayer + j; + thetas[destNodeIndex] = 0.0; + + for (int k = 0; k < numSourceNodes; k++) // looping through left layer + { + int sourceNodeIndex = m * maxNodesInALayer + k; + int currentWeightIndex = m * maxNodesInALayer * maxNodesInALayer + k * maxNodesInALayer + j; + + thetas[destNodeIndex] += activationFunction(weights[currentWeightIndex] * nodes[sourceNodeIndex]); + } // for (int k = 0; k < numSourceNodes; k++) + + nodes[destNodeIndex] = outputFunction(thetas[destNodeIndex]); + } // for (int j = 0; j < numDestNodes; j++) + } // for (int m = 0; m < numLayers - 1; m++) + + return; +} + +/** + * This function calculates the error of a network (that + * should already have been run) according to the + * error function defined above. + */ +double calculateError() +{ + double *actualOutputs = calloc(numOutputNodes, sizeof(double)); + + // storing actual outputs into an array to pass into the error function + int nodeIndex = (numLayers - 1) * maxNodesInALayer; + for (int i = 0; i < numOutputNodes; i++) + { + actualOutputs[i] = nodes[nodeIndex + i]; + } + + double error = errorFunction(expectedOutputs, actualOutputs, numOutputNodes); + + free(actualOutputs); + + return error; +} + +/** + * This function is responsible for freeing memory after running/training the network. + */ +void freeMemory() +{ + free(layerDimensions); + free(nodes); + free(weights); + free(expectedOutputs); + free(thetas); + free(psis); + + return; +} + +/** + * This function prints the current + * weights of the neural network. + */ +void printWeights() +{ + printf("\nNOW PRINTING WEIGHTS\n"); + for (int i = 0; i < totalWeights; i++) + { + printf("%lf\n", weights[i]); + } + + return; +} + +/** + * Trains the network once for all training sets, using backprop, + * then calculates the new error. + * + * Adaptive learning can be disabled by setting the learning + * factor scaler to 1.0 in the config. Weight rollback can + * also be enabled/disabled. + */ +void trainForAllTrainingSets() +{ + double *oldWeights; + // only enable weight rollback if adaptive learning is enabled as well + if (enableWeightRollback == 'Y' && learningFactorScaler != 1.0) + { + oldWeights = calloc(totalWeights, sizeof(double)); + for (int i = 0; i < totalWeights; i++) + { + oldWeights[i] = weights[i]; // storing old weights + } + } + + double errorSum = 0.0; + int index = 0; + for (int t = 0; t < numTrainingSets; t++) // train on every training set + { + for (int k = 0; k < numInputNodes; k++) // setting correct input values + { + nodes[k] = trainingSets[index]; + index++; + } + for (int k = 0; k < numOutputNodes; k++) // setting correct expected output values + { + expectedOutputs[k] = trainingSets[index]; + index++; + } + + runNetwork(); + + // collecting/applying psi values in the rightmost layer + for (int j = layerDimensions[numLayers - 2] - 1; j >= 0; j--) // last hidden layer + { + int sourceNodeIndex = maxNodesInALayer * (numLayers - 2) + j; + + for (int i = layerDimensions[numLayers - 1] - 1; i >= 0; i--) // output layer + { + int destNodeIndex = maxNodesInALayer * (numLayers - 1) + i; + + int weightJIIndex = maxWeightsInALayer * (numLayers - 2) + maxNodesInALayer * j + i; + + double w = nodes[destNodeIndex] - expectedOutputs[i]; + double theta = thetas[maxNodesInALayer * (numLayers - 1) + i]; + double psiI = w * outputDerivFunction(theta); + + psis[destNodeIndex] = psiI; + psis[maxNodesInALayer * (numLayers - 2) + j] += psiI * weights[weightJIIndex]; + + /** + * A -= is used here instead of a += like the documentation states + * because when the weights are calculated, they are not multiplied + * by the the extra -1 in the calculation formula. This avoids + * unnecessarily flipping signs two times, saving time. + */ + weights[weightJIIndex] -= learningFactor * nodes[sourceNodeIndex] * psiI; + + } // for (int i = layerDimensions[numLayers - 1] - 1; i >= 0; i--) + + double thetaJ = thetas[maxNodesInALayer * (numLayers - 2) + j]; + psis[maxNodesInALayer * (numLayers - 2) + j] *= outputDerivFunction(thetaJ); + } // for (int j = layerDimensions[numLayers - 2] - 1; j >= 0; j--) + + // collecting/applying values in the non-rightmost layers + for (int m = numLayers - 3; m >= 0; m--) // looping backwards through connectivity layers + { + int numSourceNodes = layerDimensions[m]; + int numDestNodes = layerDimensions[m + 1]; + + for (int j = numDestNodes - 1; j >= 0; j--) // looping through right layer + { + int destNodeIndex = maxNodesInALayer * (m + 1) + j; + + for (int k = numSourceNodes - 1; k >= 0; k--) // looping through left layer + { + int sourceNodeIndex = maxNodesInALayer * m + k; + + double psiJ = psis[destNodeIndex]; + int weightKJIndex = maxWeightsInALayer * m + maxNodesInALayer * k + j; + + weights[weightKJIndex] -= learningFactor * nodes[sourceNodeIndex] * psiJ; + + } // for (int k = numSourceNodes - 1; k >= 0; k--) + } // for (int j = numDestNodes - 1; j >= 0; j--) + } // for (int m = numLayers - 3; m >= 0; m--) + + double err = calculateError(); + + errorSum += err * err; + } // for (int t = 0; t < numTrainingSets; t++) + + double newError = 0.5 * errorSum; // multiply by 0.5 according to the error function + + if (learningFactorScaler != 1.0) // enable adaptive learning + { + if (newError > error && learningFactor > minLearningFactor) // error went up and the learning factor has room to decrease + { + learningFactor /= learningFactorScaler; + + if (enableWeightRollback == 'Y') + { + for (int i = 0; i < totalWeights; i++) + { + weights[i] = oldWeights[i]; + } + free(oldWeights); + } + } + else if (newError < error) // error went down + { + error = newError; + learningFactor *= learningFactorScaler; + } + + if (learningFactor > maxLearningFactor) + learningFactor = maxLearningFactor; // capping the learning factor + } + else // adaptive learning is disabled + { + error = newError; + } + + return; +} + +/** + * This runs the network for all the training sets and prints + * out the input nodes, output nodes, expected output nodes, and error. + * It also prints out the total error over all training sets. + * No training is done. + */ +void runForAllTrainingSets() +{ + int index = 0; + double errorSum = 0.0; + + for (int i = 0; i < numTrainingSets; i++) // looping through all training sets + { + for (int k = 0; k < numInputNodes; k++) + { + + if (printNetworkSpecifics == 'Y') // for debugging + { + printf("%lf ", trainingSets[index]); + } + + nodes[k] = trainingSets[index]; + index++; + } + if (printNetworkSpecifics == 'Y') + { + printf(" --> (expected "); + } + + for (int k = 0; k < numOutputNodes; k++) + { + + if (printNetworkSpecifics == 'Y') // for debugging + { + printf(" %lf", trainingSets[index]); + } + + expectedOutputs[k] = trainingSets[index]; + index++; + } + + runNetwork(); + double err = calculateError(); + + if (printNetworkSpecifics == 'Y') // for debugging + { + printf(") actual: "); + for (int k = 0; k < numOutputNodes; k++) + { + printf(" %lf", nodes[maxNodesInALayer * (numLayers - 1) + k]); + } + printf("\n"); + } + + errorSum += err * err; + } + + error = errorSum * 0.5; // multiply by 0.5 according to the error function + + printf("Total error: %.16lf\n\n", error); + + return; +} + +/** + * Trains the network. + * + * @param numTimes the amount of times to train the network + * @param targetError the error at which to stop training (if reached) + */ +void train(int numTimes, double targetError) +{ + int cycles = 0; + while (cycles < numTimes && error > targetError) + { + trainForAllTrainingSets(); + cycles++; + + if (printDebugMessages == 'Y') + { + printf("DEBUG: iteration %d, error: %.16lf, lambda: %lf\n", cycles, error, learningFactor); + } + + if (cycles % dumpEveryIterations == 0) // dumps values every _x_ iterations + { + writeWeightsToFile(); + writeOutputsToFile(); + } + } + + runForAllTrainingSets(); + + printf("lambda: %lf\n", learningFactor); + printf("Stopped after %d cycles (max %d cycles)\n", cycles, numTimes); + printf("Current error: %.16lf\n", error); + + // printing termination conditions that were or were not met + if (cycles == numTimes - 1) + printf("Stopped due to cycle amount\n"); + if (error <= targetError) + printf("Stopped due to sufficiently low error (%.16lf < %.16lf)\n", error, targetError); + else + printf("Did not reach specified error successfully (%.16lf > %.16lf)\n", error, targetError); + + return; +} \ No newline at end of file diff --git a/outputFunctions.c b/outputFunctions.c new file mode 100644 index 0000000..0f78ae5 --- /dev/null +++ b/outputFunctions.c @@ -0,0 +1,99 @@ +/** + * Gloria Zhu + * Created 9/8/2019 + * This file holds a collection of output functions as well as their derivatives + * to be used in a neural network. Note that every function here should + * take in as a parameter a single float value, which it will + * return the corresponding output or derivative for. + * + * Functions in this file: + * + * sigmoid & sigmoidDeriv + * tanh & tanhDeriv + * relu & reluDeriv + */ + +#include "./headerfiles/outputFunctions.h" + +#include +#include + +/** + * The sigmoid function is defined as: + * sigmoid(x) = 1/(1+e^-x) + * It returns values close to 1 for large + * values of x and values close to + * 0 for small values of x. + */ +double sigmoid(double value) +{ + return 1.0 / (1.0 + exp(-value)); +} + +/** + * This function returns the derivative of the sigmoid function, + * which is sigmoid(x) * (1-sigmoid(x)) + */ +double sigmoidDeriv(double value) +{ + double sig = sigmoid(value); + return sig * (1.0 - sig); +} + +/** + * The hyperbolic tangent function is defined as: + * tanh(x) = sinh(x)/cosh(x) + * where sinh(x) = ( e^x - e^-x )/2 + * and cosh(x) = ( e^x + e^-x )/2 + * It returns values close to 1 for large + * values of x and values close to + * -1 for small values of x. + */ +double tanh(double value) +{ + return tanh(value); +} + +/** + * This function returns the derivative of the tanh function, + * which is sech(x)^2, or 1/cosh(x)^2 + */ +double tanhDeriv(double value) +{ + return 1.0 / (cosh(value) * cosh(value)); +} + +/** + * The ReLU (rectified linear activation unit) function is defined as: + * relu(x) = x if x>0 + * 0 if x<=0 + * It returns the value itself if the value is positive + * and zero if the value is negative. + */ +double relu(double value) +{ + return fmax(0.0, value); +} + +/** + * This function returns the derivative of the relu function, + * which is 0 if the value is negative and 1 if the value is positive. + * + * Note that the derivative does not exist at x=0 since the left/right + * derivatives are different; in this case, the function merely returns 1. + * + * Please excuse the use of two return statements; this seemed like the most + * efficient way to write this function. + */ +double reluDeriv(double value) +{ + if (value>=0.0) + { + return 1.0; + } + else + { + return 0.0; + } + +} \ No newline at end of file diff --git a/weights/placeholder.txt b/weights/placeholder.txt new file mode 100644 index 0000000..e69de29