add: data and network initialization done

train subcommand can read and train the network

TODO:
    - Refactor json_read() to parse multiple labels
    - Implement a function to save network weights once the network have
      trained
    - Implement a function to load trained weights to use with predict
      subcommand

2 files changed, 49 insertions, 0 deletions

diff --git a/src/main.c b/src/main.c
index 8f68d1e..30aca1e 100644
--- a/src/main.c
+++ b/src/main.c
@@ -110,6 +110,34 @@ void load_config(struct Configs *cfg, int n_args, ...)
     die("load_config() Error:");
 }
 
+Layer * load_network(struct Configs cfg)
+{
+    extern struct Activation NN_RELU;
+    extern struct Activation NN_SOFTPLUS;
+    extern struct Activation NN_SIGMOID;
+    extern struct Activation NN_LEAKY_RELU;
+
+    Layer *network = ecalloc(cfg.network_size, sizeof(Layer));
+
+    for (size_t i = 0; i < cfg.network_size; i++) {
+        if (!strcmp("relu", cfg.activations[i]))                network[i].activation = NN_RELU;
+        else if (!strcmp("sigmoid", cfg.activations[i]))        network[i].activation = NN_SIGMOID;
+        else if (!strcmp("softplus", cfg.activations[i]))       network[i].activation = NN_SOFTPLUS;
+        else if (!strcmp("leaky_relu", cfg.activations[i]))     network[i].activation = NN_LEAKY_RELU;
+        else die("load_network() Error: Unknown '%s' activation", cfg.activations[i]);
+
+        network[i].neurons = cfg.neurons[i];
+    }
+    return network;
+}
+
+struct Cost load_loss(struct Configs cfg)
+{
+    extern struct Cost NN_SQUARE;
+    if (!strcmp("square", cfg.loss)) return NN_SQUARE;
+    die("load_loss() Error: Unknown '%s' loss function", cfg.loss);
+    exit(1);
+}
 
 int main(int argc, char *argv[]) {
     struct Configs ml_configs = {
@@ -122,6 +150,26 @@ int main(int argc, char *argv[]) {
     // Try different config paths
     load_config(&ml_configs, 3, "~/.config/ml/ml.cfg", "~/.ml/ml.cfg", ml_configs.config_filepath);
     util_load_cli(&ml_configs, argc, argv);
+    argc -= optind;
+    argv += optind;
+    Layer *network = load_network(ml_configs);
+
+    Array X, y;
+    if (!strcmp("train", argv[0])) {
+        json_read(argv[1], &X, &y, ml_configs.label_keys[0], ml_configs.input_keys, ml_configs.n_input_keys);
+        nn_network_init_weights(network, ml_configs.network_size, X.shape[1]);
+        nn_network_train(
+                network, ml_configs.network_size,
+                X.data, X.shape,
+                y.data, y.shape,
+                load_loss(ml_configs),
+                ml_configs.epochs,
+                ml_configs.alpha);
+    } else if (!strcmp("predict", argv[0])) {
+    } else usage(1);
+
+    nn_network_free_weights(network, ml_configs.network_size);
+    free(network);
     util_free_config(&ml_configs);
     return 0;
 }
diff --git a/src/util.h b/src/util.h
index 00567a8..3219502 100644
--- a/src/util.h
+++ b/src/util.h
@@ -21,6 +21,7 @@ struct Configs {
     char **activations;
 };
 
+void usage(int exit_code);
 void die(const char *fmt, ...);
 void *ecalloc(size_t nmemb, size_t size);
 void *erealloc(void *ptr, size_t size);