diff options
| -rw-r--r-- | src/nn.c | 44 | ||||
| -rw-r--r-- | src/nn.h | 13 |
2 files changed, 39 insertions, 18 deletions
@@ -3,7 +3,7 @@ static void fill_random_weights(double *weights, double *bias, size_t rows, size_t cols); void nn_forward( - double **out, + double **out, double **zout, double *X, size_t X_shape[2], Layer network[], size_t network_size) { @@ -14,25 +14,46 @@ void nn_forward( for (size_t l = 0; l < network_size; l++) { out_shape[1] = network[l].neurons; - nn_layer_forward(network[l], out[l], out_shape, input, in_shape); + nn_layer_forward(network[l], zout[l], out_shape, input, in_shape); + nn_layer_map_activation(network[l].activation, out[l], out_shape, zout[l], out_shape); in_shape[1] = out_shape[1]; input = out[l]; } } -void nn_layer_forward(Layer layer, double *out, size_t out_shape[2], double *input, size_t input_shape[2]) +void nn_layer_map_activation( + double (*activation)(double), + double *aout, size_t aout_shape[2], + double *zout, size_t zout_shape[2]) { - if (out_shape[0] != input_shape[0] || out_shape[1] != layer.neurons) { + if (zout_shape[0] != aout_shape[0] || zout_shape[1] != aout_shape[1]) { fprintf(stderr, - "nn_layer_forward() Error: out must have (%zu x %zu) dimensions not (%zu x %zu)\n", - input_shape[0], layer.neurons, out_shape[0], out_shape[1]); + "nn_layer_map_activation() Error: zout must have (%zu x %zu) dimensions not (%zu x %zu)\n", + aout_shape[0], aout_shape[1], zout_shape[0], zout_shape[1]); + exit(1); + } + + for (size_t i = 0; i < aout_shape[0]; i++) { + for (size_t j = 0; j < aout_shape[1]; j ++) { + size_t index = aout_shape[1] * i + j; + aout[index] = activation(zout[index]); + } + } +} + +void nn_layer_forward(Layer layer, double *zout, size_t zout_shape[2], double *input, size_t input_shape[2]) +{ + if (zout_shape[0] != input_shape[0] || zout_shape[1] != layer.neurons) { + fprintf(stderr, + "nn_layer_forward() Error: zout must have (%zu x %zu) dimensions not (%zu x %zu)\n", + input_shape[0], layer.neurons, zout_shape[0], zout_shape[1]); exit(1); } for (size_t i = 0; i < input_shape[0]; i++) { for (size_t j = 0; j < layer.neurons; j++) { size_t index = layer.neurons * i + j; - out[index] = layer.bias[j]; + zout[index] = layer.bias[j]; } } @@ -40,14 +61,7 @@ void nn_layer_forward(Layer layer, double *out, size_t out_shape[2], double *inp input_shape[0], layer.neurons, layer.input_nodes, // m, n, k 1.0, input, input_shape[1], //alpha X layer.weights, layer.neurons, // W - 1.0, out, layer.neurons); // beta B - - for (size_t i = 0; i < input_shape[0]; i++) { - for (size_t j = 0; j < layer.neurons; j ++) { - size_t index = layer.neurons * i + j; - out[index] = layer.activation(out[index]); - } - } + 1.0, zout, layer.neurons); // beta B } void nn_network_init_weights(Layer layers[], size_t nmemb, size_t n_inputs) @@ -18,13 +18,20 @@ typedef struct Layer { void nn_network_init_weights(Layer *network, size_t nmemb, size_t input_cols); void nn_network_free_weights(Layer *network, size_t nmemb); -void nn_layer_forward(Layer layer, double *out, size_t out_shape[2], double *input, size_t input_shape[2]); //TODO -void nn_layer_backward(Layer *layer, double *out, size_t out_shape[2]); //TODO +void nn_layer_map_activation(double (*activation)(double), double *aout, size_t aout_shape[2], double *zout, size_t zout_shape[2]); +void nn_layer_forward(Layer layer, double *out, size_t out_shape[2], double *input, size_t input_shape[2]); +void nn_layer_backward( + Layer *layer, + double *weights, + double *out, size_t out_shape[2], + double *labels, size_t labels_shape[2], + double *local_gradient); //TODO double sigmoid(double x); double relu(double x); double identity(double x); -void nn_forward(double **out, double *input, size_t input_shape[2], Layer network[], size_t network_size); +void nn_forward(double **aout, double **zout, double *input, size_t input_shape[2], Layer network[], size_t network_size); +double nn_layer_out_delta(double error, double (*activation_derivative)(double)); #endif |