Add nn_neuron_set_weights and nn_neuron_set_bias

devfacet · Apr 7, 2024 · 6c7dd92 · 6c7dd92
1 parent b06d8c2
commit 6c7dd92
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Showing 9 changed files with 75 additions and 54 deletions.
diff --git a/examples/arch/arm/cmsis-dsp/neuron/main.c b/examples/arch/arm/cmsis-dsp/neuron/main.c
@@ -15,13 +15,13 @@ int main(int argc, char *argv[]) {
 
  // Init vars
  NNNeuron neuron;
- int n_inputs = 3;
  float inputs[NEURON_MAX_WEIGHTS] = {1, 2, 3};
  float weights[NEURON_MAX_WEIGHTS] = {0.2f, 0.8f, -0.5f};
+ int n_weights = 3;
  float bias = 2.0f;
 
  // Compute the output
- nn_neuron_init(&neuron, weights, n_inputs, bias, nn_activation_func_identity, nn_dot_product_cmsis);
+ nn_neuron_init(&neuron, weights, n_weights, bias, nn_activation_func_identity, nn_dot_product_cmsis);
  printf("output (CMSIS): %f\n", nn_neuron_compute(&neuron, inputs));
 
  return 0;

diff --git a/examples/arch/arm/neon/neuron/main.c b/examples/arch/arm/neon/neuron/main.c
@@ -15,13 +15,13 @@ int main(int argc, char *argv[]) {
 
  // Init vars
  NNNeuron neuron;
- int n_inputs = 3;
  float inputs[NEURON_MAX_WEIGHTS] = {1, 2, 3};
  float weights[NEURON_MAX_WEIGHTS] = {0.2f, 0.8f, -0.5f};
+ int n_weights = 3;
  float bias = 2.0f;
 
  // Compute the output
- nn_neuron_init(&neuron, weights, n_inputs, bias, nn_activation_func_identity, nn_dot_product_neon);
+ nn_neuron_init(&neuron, weights, n_weights, bias, nn_activation_func_identity, nn_dot_product_neon);
  printf("output (NEON): %f\n", nn_neuron_compute(&neuron, inputs));
 
  return 0;

diff --git a/examples/arch/generic/neuron/main.c b/examples/arch/generic/neuron/main.c
@@ -10,13 +10,13 @@ int main(int argc, char *argv[]) {
 
  // Init vars
  NNNeuron neuron;
- int n_inputs = 3;
  float inputs[NEURON_MAX_WEIGHTS] = {1, 2, 3};
  float weights[NEURON_MAX_WEIGHTS] = {0.2f, 0.8f, -0.5f};
+ int n_weights = 3;
  float bias = 2.0f;
 
  // Compute the output
- nn_neuron_init(&neuron, weights, n_inputs, bias, nn_activation_func_identity, nn_dot_product);
+ nn_neuron_init(&neuron, weights, n_weights, bias, nn_activation_func_identity, nn_dot_product);
  printf("output (generic): %f\n", nn_neuron_compute(&neuron, inputs));
 
  return 0;

diff --git a/include/nn_neuron.h b/include/nn_neuron.h
@@ -17,14 +17,20 @@
 // real-world applications since it's not optimized for performance.
 typedef struct {
  float weights[NEURON_MAX_WEIGHTS];
+ size_t n_weights;
  float bias;
- size_t n_inputs;
  NNActivationFunction act_func;
  NNDotProductFunction dot_product_func;
 } NNNeuron;
 
 // nn_neuron_init initializes a neuron with the given arguments.
-void nn_neuron_init(NNNeuron *neuron, const float *weights, size_t n_inputs, float bias, NNActivationFunction act_func, NNDotProductFunction dot_product_func);
+void nn_neuron_init(NNNeuron *neuron, const float *weights, size_t n_weights, float bias, NNActivationFunction act_func, NNDotProductFunction dot_product_func);
+
+// nn_neuron_set_weights sets the weights of the given neuron.
+void nn_neuron_set_weights(NNNeuron *neuron, const float *weights);
+
+// nn_neuron_set_bias sets the bias of the given neuron.
+void nn_neuron_set_bias(NNNeuron *neuron, float bias);
 
 // nn_neuron_compute computes the given neuron and returns the output.
 float nn_neuron_compute(const NNNeuron *neuron, const float *inputs);

diff --git a/src/nn_neuron.c b/src/nn_neuron.c
@@ -6,12 +6,12 @@
 #include <stdio.h>
 
 // nn_neuron_init initializes a neuron with the given arguments.
-void nn_neuron_init(NNNeuron *neuron, const float *weights, size_t n_inputs, float bias, NNActivationFunction act_func, NNDotProductFunction dot_product_func) {
+void nn_neuron_init(NNNeuron *neuron, const float *weights, size_t n_weights, float bias, NNActivationFunction act_func, NNDotProductFunction dot_product_func) {
  if (weights == NULL) {
  return;
  }
- neuron->n_inputs = n_inputs;
- for (size_t i = 0; i < neuron->n_inputs; ++i) {
+ neuron->n_weights = n_weights;
+ for (size_t i = 0; i < neuron->n_weights; ++i) {
  neuron->weights[i] = weights[i];
  }
  neuron->bias = bias;
@@ -23,6 +23,21 @@ void nn_neuron_init(NNNeuron *neuron, const float *weights, size_t n_inputs, flo
  }
 }
 
+// nn_neuron_set_weights sets the weights of the given neuron.
+void nn_neuron_set_weights(NNNeuron *neuron, const float *weights) {
+ if (weights == NULL) {
+ return;
+ }
+ for (size_t i = 0; i < neuron->n_weights; ++i) {
+ neuron->weights[i] = weights[i];
+ }
+}
+
+// nn_neuron_set_bias sets the bias of the given neuron.
+void nn_neuron_set_bias(NNNeuron *neuron, float bias) {
+ neuron->bias = bias;
+}
+
 // nn_neuron_compute computes the given neuron and returns the output.
 float nn_neuron_compute(const NNNeuron *neuron, const float *inputs) {
  // Compute the output of the neuron:
@@ -32,7 +47,7 @@ float nn_neuron_compute(const NNNeuron *neuron, const float *inputs) {
  float result = 0.0f;
  if (neuron->dot_product_func != NULL) {
  // Sum the weighted inputs
- result = neuron->dot_product_func(neuron->weights, inputs, neuron->n_inputs);
+ result = neuron->dot_product_func(neuron->weights, inputs, neuron->n_weights);
  }
  // Add the bias
  result += neuron->bias;

diff --git a/tests/arch/arm/cmsis-dsp/neuron/main.c b/tests/arch/arm/cmsis-dsp/neuron/main.c
@@ -15,7 +15,7 @@
 typedef struct {
  float inputs[NEURON_MAX_WEIGHTS];
  float weights[NEURON_MAX_WEIGHTS];
- int n_inputs;
+ int n_weights;
  float bias;
  NNDotProductFunction dot_product_func;
  float output_tolerance;
@@ -28,7 +28,7 @@ void run_test_cases(TestCase *test_cases, int n_cases, char *info, NNDotProductF
  TestCase tc = test_cases[i];
  NNNeuron neuron;
 
- nn_neuron_init(&neuron, tc.weights, tc.n_inputs, tc.bias, nn_activation_func_identity, dot_product_func);
+ nn_neuron_init(&neuron, tc.weights, tc.n_weights, tc.bias, nn_activation_func_identity, dot_product_func);
  const float output = nn_neuron_compute(&neuron, tc.inputs);
  assert(isnan(output) == false);
  assert(fabs(output - tc.expected_output) < tc.output_tolerance);
@@ -41,7 +41,7 @@ int main() {
  {
  .inputs = {0.5f, 1.2f, -0.8f},
  .weights = {0.2f, 0.3f, -0.1f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 0.5f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 1.04f,
@@ -50,7 +50,7 @@ int main() {
  {
  .inputs = {-0.6f, -1.1f, 0.9f},
  .weights = {-0.2f, 0.5f, 0.3f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = -0.5f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = -0.66f,
@@ -59,7 +59,7 @@ int main() {
  {
  .inputs = {1.5f, 2.0f, -1.0f},
  .weights = {0.4f, 0.4f, -0.2f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 2.0f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 3.6f,
@@ -68,7 +68,7 @@ int main() {
  {
  .inputs = {0.1f, -0.2f, 0.3f},
  .weights = {0.3f, -0.2f, 0.1f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 0.05f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 0.15f,
@@ -77,7 +77,7 @@ int main() {
  {
  .inputs = {-2.5f, 3.0f, -1.5f},
  .weights = {0.5f, -0.5f, 0.75f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 1.0f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = -2.875f,
@@ -86,7 +86,7 @@ int main() {
  {
  .inputs = {0.0f, 0.0f, 0.0f},
  .weights = {0.25f, -0.75f, 0.5f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 0.5f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 0.5f,
@@ -95,7 +95,7 @@ int main() {
  {
  .inputs = {1.2f, -1.2f, 0.8f},
  .weights = {0.0f, 0.0f, 0.0f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 0.25f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 0.25f,
@@ -104,7 +104,7 @@ int main() {
  {
  .inputs = {1.0f, -1.0f, 1.0f},
  .weights = {-1.0f, 1.0f, -1.0f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = -0.5f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = -3.5f,
@@ -113,7 +113,7 @@ int main() {
  {
  .inputs = {0.123f, 0.456f, -0.789f},
  .weights = {0.321f, -0.654f, 0.987f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 0.1f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = -0.937484,
@@ -122,7 +122,7 @@ int main() {
  {
  .inputs = {0.001f, -0.002f, 0.003f},
  .weights = {0.004f, 0.005f, -0.006f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 0.0f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 0.000012f,

diff --git a/tests/arch/arm/neon/neuron/main.c b/tests/arch/arm/neon/neuron/main.c
@@ -15,7 +15,7 @@
 typedef struct {
  float inputs[NEURON_MAX_WEIGHTS];
  float weights[NEURON_MAX_WEIGHTS];
- int n_inputs;
+ int n_weights;
  float bias;
  NNDotProductFunction dot_product_func;
  float output_tolerance;
@@ -28,7 +28,7 @@ void run_test_cases(TestCase *test_cases, int n_cases, char *info, NNDotProductF
  TestCase tc = test_cases[i];
  NNNeuron neuron;
 
- nn_neuron_init(&neuron, tc.weights, tc.n_inputs, tc.bias, nn_activation_func_identity, dot_product_func);
+ nn_neuron_init(&neuron, tc.weights, tc.n_weights, tc.bias, nn_activation_func_identity, dot_product_func);
  const float output = nn_neuron_compute(&neuron, tc.inputs);
  assert(isnan(output) == false);
  assert(fabs(output - tc.expected_output) < tc.output_tolerance);
@@ -41,7 +41,7 @@ int main() {
  {
  .inputs = {0.5f, 1.2f, -0.8f},
  .weights = {0.2f, 0.3f, -0.1f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 0.5f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 1.04f,
@@ -50,7 +50,7 @@ int main() {
  {
  .inputs = {-0.6f, -1.1f, 0.9f},
  .weights = {-0.2f, 0.5f, 0.3f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = -0.5f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = -0.66f,
@@ -59,7 +59,7 @@ int main() {
  {
  .inputs = {1.5f, 2.0f, -1.0f},
  .weights = {0.4f, 0.4f, -0.2f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 2.0f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 3.6f,
@@ -68,7 +68,7 @@ int main() {
  {
  .inputs = {0.1f, -0.2f, 0.3f},
  .weights = {0.3f, -0.2f, 0.1f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 0.05f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 0.15f,
@@ -77,7 +77,7 @@ int main() {
  {
  .inputs = {-2.5f, 3.0f, -1.5f},
  .weights = {0.5f, -0.5f, 0.75f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 1.0f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = -2.875f,
@@ -86,7 +86,7 @@ int main() {
  {
  .inputs = {0.0f, 0.0f, 0.0f},
  .weights = {0.25f, -0.75f, 0.5f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 0.5f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 0.5f,
@@ -95,7 +95,7 @@ int main() {
  {
  .inputs = {1.2f, -1.2f, 0.8f},
  .weights = {0.0f, 0.0f, 0.0f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 0.25f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 0.25f,
@@ -104,7 +104,7 @@ int main() {
  {
  .inputs = {1.0f, -1.0f, 1.0f},
  .weights = {-1.0f, 1.0f, -1.0f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = -0.5f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = -3.5f,
@@ -113,7 +113,7 @@ int main() {
  {
  .inputs = {0.123f, 0.456f, -0.789f},
  .weights = {0.321f, -0.654f, 0.987f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 0.1f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = -0.937484,
@@ -122,7 +122,7 @@ int main() {
  {
  .inputs = {0.001f, -0.002f, 0.003f},
  .weights = {0.004f, 0.005f, -0.006f},
- .n_inputs = 3,
+ .n_weights = 3,
  .bias = 0.0f,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 0.000012f,

diff --git a/tests/arch/generic/dot_product/main.c b/tests/arch/generic/dot_product/main.c
@@ -14,7 +14,7 @@
 typedef struct {
  float a[4];
  float b[4];
- int n_inputs;
+ int n_weights;
  float bias;
  NNDotProductFunction dot_product_func;
  float output_tolerance;
@@ -26,7 +26,7 @@ void run_test_cases(TestCase *test_cases, int n_cases, char *info, NNDotProductF
  for (int i = 0; i < n_cases; ++i) {
  TestCase tc = test_cases[i];
 
- const float output = dot_product_func(tc.a, tc.b, tc.n_inputs);
+ const float output = dot_product_func(tc.a, tc.b, tc.n_weights);
  assert(isnan(output) == false);
  assert(fabs(output - tc.expected_output) < tc.output_tolerance);
  printf("passed: %s case=%d info=%s\n", __func__, i + 1, info);
@@ -38,31 +38,31 @@ int main() {
  {
  .a = {0.5f},
  .b = {0.2f},
- .n_inputs = 1,
+ .n_weights = 1,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 0.1f,
  },
 
  {
  .a = {-0.6f, -1.1f},
  .b = {-0.2f, 0.5f},
- .n_inputs = 2,
+ .n_weights = 2,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = -0.43f,
  },
 
  {
  .a = {1.5f, 2.0f, -1.0f},
  .b = {0.4f, 0.4f, -0.2f},
- .n_inputs = 3,
+ .n_weights = 3,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 1.6f,
  },
 
  {
  .a = {0.36f, 0.17f, 0.96f, 0.12f},
  .b = {0.77f, 0.09f, 0.12f, 0.81f},
- .n_inputs = 4,
+ .n_weights = 4,
  .output_tolerance = DEFAULT_OUTPUT_TOLERANCE,
  .expected_output = 0.5048,
  },