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Changed name case, code style.

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This commit is contained in:
Lewis Van Winkle
2016-02-11 14:38:42 -06:00
parent 2bbc1b146c
commit 99e4d6a0e1
8 changed files with 61 additions and 61 deletions
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32
README.md
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@@ -1,6 +1,6 @@
#GENANN
#Genann
GENANN is a very minimal library for training and using feedforward artificial neural
Genann is a very minimal library for training and using feedforward artificial neural
networks (ANN) in C. Its primary focus is on being simple, fast, and hackable. It achieves
this by providing only the necessary functions and little extra.
@@ -39,7 +39,7 @@ double **training_data_input, **training_data_output, **test_data_input;
/* New network with 5 inputs,
* 2 hidden layer of 10 neurons each,
* and 1 output. */
GENANN *ann = genann_init(5, 2, 10, 1);
genann *ann = genann_init(5, 2, 10, 1);
/* Learn on the training set. */
for (i = 0; i < 300; ++i) {
@@ -63,23 +63,23 @@ prevent over-fitting.
###Creating and Freeing ANNs
```C
GENANN *genann_init(int inputs, int hidden_layers, int hidden, int outputs);
GENANN *genann_copy(GENANN const *ann);
void genann_free(GENANN *ann);
genann *genann_init(int inputs, int hidden_layers, int hidden, int outputs);
genann *genann_copy(genann const *ann);
void genann_free(genann *ann);
```
Creating a new ANN is done with the `genann_init()` function. It's arguments
are the number of inputs, the number of hidden layers, the number of neurons in
each hidden layer, and the number of outputs. It returns a `GENANN` struct pointer.
each hidden layer, and the number of outputs. It returns a `genann` struct pointer.
Calling `genann_copy()` will create a deep-copy of an existing GENANN struct.
Calling `genann_copy()` will create a deep-copy of an existing `genann` struct.
Call `genann_free()` when you're finished with an ANN returned by `genann_init()`.
###Training ANNs
```C
void genann_train(GENANN const *ann, double const *inputs,
void genann_train(genann const *ann, double const *inputs,
double const *desired_outputs, double learning_rate);
```
@@ -88,14 +88,14 @@ should be called by passing in an array of inputs, an array of expected output,
and a learning rate. See *example1.c* for an example of learning with
backpropogation.
A primary design goal of GENANN was to store all the network weights in one
A primary design goal of Genann was to store all the network weights in one
contigious block of memory. This makes it easy and efficient to train the
network weights using direct-search numeric optimizion algorthims,
such as [Hill Climbing](https://en.wikipedia.org/wiki/Hill_climbing),
[the Genetic Algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm), [Simulated
Annealing](https://en.wikipedia.org/wiki/Simulated_annealing), etc.
These methods can be used by searching on the ANN's weights directly.
Every `GENANN` struct contains the members `int total_weights;` and
Every `genann` struct contains the members `int total_weights;` and
`double *weight;`. `*weight` points to an array of `total_weights`
size which contains all weights used by the ANN. See *example2.c* for
an example of training using random hill climbing search.
@@ -103,16 +103,16 @@ an example of training using random hill climbing search.
###Saving and Loading ANNs
```C
GENANN *genann_read(FILE *in);
void genann_write(GENANN const *ann, FILE *out);
genann *genann_read(FILE *in);
void genann_write(genann const *ann, FILE *out);
```
GENANN provides the `genann_read()` and `genann_write()` functions for loading or saving an ANN in a text-based format.
Genann provides the `genann_read()` and `genann_write()` functions for loading or saving an ANN in a text-based format.
###Evaluating
```C
double const *genann_run(GENANN const *ann, double const *inputs);
double const *genann_run(genann const *ann, double const *inputs);
```
Call `genann_run()` on a trained ANN to run a feed-forward pass on a given set of inputs. `genann_run()`
@@ -134,4 +134,4 @@ If you're looking for a heavier, more opinionated neural network library in C,
I highly recommend the [FANN library](http://leenissen.dk/fann/wp/). Another
good library is Peter van Rossum's [Lightweight Neural
Network](http://lwneuralnet.sourceforge.net/), which despite its name, is
heavier and has more features than GENANN.
heavier and has more features than Genann.

2
example1.c
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@@ -14,7 +14,7 @@ int main(int argc, char *argv[])
/* New network with 2 inputs,
* 1 hidden layer of 2 neurons,
* and 1 output. */
GENANN *ann = genann_init(2, 1, 2, 1);
genann *ann = genann_init(2, 1, 2, 1);
/* Train on the four labeled data points many times. */
for (i = 0; i < 300; ++i) {

4
example2.c
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@@ -16,7 +16,7 @@ int main(int argc, char *argv[])
/* New network with 2 inputs,
* 1 hidden layer of 2 neurons,
* and 1 output. */
GENANN *ann = genann_init(2, 1, 2, 1);
genann *ann = genann_init(2, 1, 2, 1);
double err;
double last_err = 1000;
@@ -29,7 +29,7 @@ int main(int argc, char *argv[])
genann_randomize(ann);
}
GENANN *save = genann_copy(ann);
genann *save = genann_copy(ann);
/* Take a random guess at the ANN weights. */
for (i = 0; i < ann->total_weights; ++i) {

2
example3.c
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@@ -16,7 +16,7 @@ int main(int argc, char *argv[])
exit(1);
}
GENANN *ann = genann_read(saved);
genann *ann = genann_read(saved);
fclose(saved);
if (!ann) {

2
example4.c
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@@ -78,7 +78,7 @@ int main(int argc, char *argv[])
* 1 hidden layer(s) of 4 neurons.
* 3 outputs (1 per class)
*/
GENANN *ann = genann_init(4, 1, 4, 3);
genann *ann = genann_init(4, 1, 4, 3);
int i, j;
int loops = 5000;

34
genann.c
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@@ -75,7 +75,7 @@ double genann_act_threshold(double a) {
}
GENANN *genann_init(int inputs, int hidden_layers, int hidden, int outputs) {
genann *genann_init(int inputs, int hidden_layers, int hidden, int outputs) {
if (hidden_layers < 0) return 0;
if (inputs < 1) return 0;
if (outputs < 1) return 0;
@@ -89,8 +89,8 @@ GENANN *genann_init(int inputs, int hidden_layers, int hidden, int outputs) {
const int total_neurons = (inputs + hidden * hidden_layers + outputs);
/* Allocate extra size for weights, outputs, and deltas. */
const int size = sizeof(GENANN) + sizeof(double) * (total_weights + total_neurons + (total_neurons - inputs));
GENANN *ret = malloc(size);
const int size = sizeof(genann) + sizeof(double) * (total_weights + total_neurons + (total_neurons - inputs));
genann *ret = malloc(size);
if (!ret) return 0;
ret->inputs = inputs;
@@ -102,7 +102,7 @@ GENANN *genann_init(int inputs, int hidden_layers, int hidden, int outputs) {
ret->total_neurons = total_neurons;
/* Set pointers. */
ret->weight = (double*)((char*)ret + sizeof(GENANN));
ret->weight = (double*)((char*)ret + sizeof(genann));
ret->output = ret->weight + ret->total_weights;
ret->delta = ret->output + ret->total_neurons;
@@ -115,11 +115,11 @@ GENANN *genann_init(int inputs, int hidden_layers, int hidden, int outputs) {
}
GENANN *genann_read(FILE *in) {
genann *genann_read(FILE *in) {
int inputs, hidden_layers, hidden, outputs;
fscanf(in, "%d %d %d %d", &inputs, &hidden_layers, &hidden, &outputs);
GENANN *ann = genann_init(inputs, hidden_layers, hidden, outputs);
genann *ann = genann_init(inputs, hidden_layers, hidden, outputs);
int i;
for (i = 0; i < ann->total_weights; ++i) {
@@ -130,15 +130,15 @@ GENANN *genann_read(FILE *in) {
}
GENANN *genann_copy(GENANN const *ann) {
const int size = sizeof(GENANN) + sizeof(double) * (ann->total_weights + ann->total_neurons + (ann->total_neurons - ann->inputs));
GENANN *ret = malloc(size);
genann *genann_copy(genann const *ann) {
const int size = sizeof(genann) + sizeof(double) * (ann->total_weights + ann->total_neurons + (ann->total_neurons - ann->inputs));
genann *ret = malloc(size);
if (!ret) return 0;
memcpy(ret, ann, size);
/* Set pointers. */
ret->weight = (double*)((char*)ret + sizeof(GENANN));
ret->weight = (double*)((char*)ret + sizeof(genann));
ret->output = ret->weight + ret->total_weights;
ret->delta = ret->output + ret->total_neurons;
@@ -146,7 +146,7 @@ GENANN *genann_copy(GENANN const *ann) {
}
void genann_randomize(GENANN *ann) {
void genann_randomize(genann *ann) {
int i;
for (i = 0; i < ann->total_weights; ++i) {
double r = GENANN_RANDOM();
@@ -156,13 +156,13 @@ void genann_randomize(GENANN *ann) {
}
void genann_free(GENANN *ann) {
void genann_free(genann *ann) {
/* The weight, output, and delta pointers go to the same buffer. */
free(ann);
}
double const *genann_run(GENANN const *ann, double const *inputs) {
double const *genann_run(genann const *ann, double const *inputs) {
double const *w = ann->weight;
double *o = ann->output + ann->inputs;
double const *i = ann->output;
@@ -173,8 +173,8 @@ double const *genann_run(GENANN const *ann, double const *inputs) {
int h, j, k;
const GENANN_ACTFUN act = ann->activation_hidden;
const GENANN_ACTFUN acto = ann->activation_output;
const genann_actfun act = ann->activation_hidden;
const genann_actfun acto = ann->activation_output;
/* Figure hidden layers, if any. */
for (h = 0; h < ann->hidden_layers; ++h) {
@@ -217,7 +217,7 @@ double const *genann_run(GENANN const *ann, double const *inputs) {
}
void genann_train(GENANN const *ann, double const *inputs, double const *desired_outputs, double learning_rate) {
void genann_train(genann const *ann, double const *inputs, double const *desired_outputs, double learning_rate) {
/* To begin with, we must run the network forward. */
genann_run(ann, inputs);
@@ -334,7 +334,7 @@ void genann_train(GENANN const *ann, double const *inputs, double const *desired
}
void genann_write(GENANN const *ann, FILE *out) {
void genann_write(genann const *ann, FILE *out) {
fprintf(out, "%d %d %d %d", ann->inputs, ann->hidden_layers, ann->hidden, ann->outputs);
int i;

26
genann.h
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@@ -40,18 +40,18 @@ extern "C" {
#endif
typedef double (*GENANN_ACTFUN)(double a);
typedef double (*genann_actfun)(double a);
typedef struct GENANN {
typedef struct genann {
/* How many inputs, outputs, and hidden neurons. */
int inputs, hidden_layers, hidden, outputs;
/* Which activation function to use for hidden neurons. Default: gennann_act_sigmoid_cached*/
GENANN_ACTFUN activation_hidden;
genann_actfun activation_hidden;
/* Which activation function to use for output. Default: gennann_act_sigmoid_cached*/
GENANN_ACTFUN activation_output;
genann_actfun activation_output;
/* Total number of weights, and size of weights buffer. */
int total_weights;
@@ -68,33 +68,33 @@ typedef struct GENANN {
/* Stores delta of each hidden and output neuron (total_neurons - inputs long). */
double *delta;
} GENANN;
} genann;
/* Creates and returns a new ann. */
GENANN *genann_init(int inputs, int hidden_layers, int hidden, int outputs);
genann *genann_init(int inputs, int hidden_layers, int hidden, int outputs);
/* Creates ANN from file saved with genann_write. */
GENANN *genann_read(FILE *in);
genann *genann_read(FILE *in);
/* Sets weights randomly. Called by init. */
void genann_randomize(GENANN *ann);
void genann_randomize(genann *ann);
/* Returns a new copy of ann. */
GENANN *genann_copy(GENANN const *ann);
genann *genann_copy(genann const *ann);
/* Frees the memory used by an ann. */
void genann_free(GENANN *ann);
void genann_free(genann *ann);
/* Runs the feedforward algorithm to calculate the ann's output. */
double const *genann_run(GENANN const *ann, double const *inputs);
double const *genann_run(genann const *ann, double const *inputs);
/* Does a single backprop update. */
void genann_train(GENANN const *ann, double const *inputs, double const *desired_outputs, double learning_rate);
void genann_train(genann const *ann, double const *inputs, double const *desired_outputs, double learning_rate);
/* Saves the ann. */
void genann_write(GENANN const *ann, FILE *out);
void genann_write(genann const *ann, FILE *out);
double genann_act_sigmoid(double a);

20
test.c
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@@ -32,7 +32,7 @@
void basic() {
GENANN *ann = genann_init(1, 0, 0, 1);
genann *ann = genann_init(1, 0, 0, 1);
lequal(ann->total_weights, 2);
double a;
@@ -67,7 +67,7 @@ void basic() {
void xor() {
GENANN *ann = genann_init(2, 1, 2, 1);
genann *ann = genann_init(2, 1, 2, 1);
ann->activation_hidden = genann_act_threshold;
ann->activation_output = genann_act_threshold;
@@ -102,7 +102,7 @@ void xor() {
void backprop() {
GENANN *ann = genann_init(1, 0, 0, 1);
genann *ann = genann_init(1, 0, 0, 1);
double input, output;
input = .5;
@@ -121,7 +121,7 @@ void train_and() {
double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
double output[4] = {0, 0, 0, 1};
GENANN *ann = genann_init(2, 0, 0, 1);
genann *ann = genann_init(2, 0, 0, 1);
int i, j;
@@ -145,7 +145,7 @@ void train_or() {
double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
double output[4] = {0, 1, 1, 1};
GENANN *ann = genann_init(2, 0, 0, 1);
genann *ann = genann_init(2, 0, 0, 1);
genann_randomize(ann);
int i, j;
@@ -171,7 +171,7 @@ void train_xor() {
double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
double output[4] = {0, 1, 1, 0};
GENANN *ann = genann_init(2, 1, 2, 1);
genann *ann = genann_init(2, 1, 2, 1);
int i, j;
@@ -194,7 +194,7 @@ void train_xor() {
void persist() {
GENANN *first = genann_init(1000, 5, 50, 10);
genann *first = genann_init(1000, 5, 50, 10);
FILE *out = fopen("persist.txt", "w");
genann_write(first, out);
@@ -202,7 +202,7 @@ void persist() {
FILE *in = fopen("persist.txt", "r");
GENANN *second = genann_read(in);
genann *second = genann_read(in);
fclose(out);
lequal(first->inputs, second->inputs);
@@ -222,9 +222,9 @@ void persist() {
void copy() {
GENANN *first = genann_init(1000, 5, 50, 10);
genann *first = genann_init(1000, 5, 50, 10);
GENANN *second = genann_copy(first);
genann *second = genann_copy(first);
lequal(first->inputs, second->inputs);
lequal(first->hidden_layers, second->hidden_layers);