Decouple activations from linear layer.

1 files changed, 23 insertions, 21 deletions

diff --git a/src/lib/test/train_linear_perceptron_test.c b/src/lib/test/train_linear_perceptron_test.c
index 2b1336d..667643b 100644
--- a/src/lib/test/train_linear_perceptron_test.c
+++ b/src/lib/test/train_linear_perceptron_test.c
@@ -1,9 +1,8 @@
 #include <neuralnet/train.h>
 
+#include "neuralnet_impl.h"
 #include <neuralnet/matrix.h>
 #include <neuralnet/neuralnet.h>
-#include "activation.h"
-#include "neuralnet_impl.h"
 
 #include "test.h"
 #include "test_util.h"
@@ -11,19 +10,21 @@
 #include <assert.h>
 
 TEST_CASE(neuralnet_train_linear_perceptron_test) {
-  const int num_layers = 1;
-  const int layer_sizes[] = { 1, 1 };
-  const nnActivation layer_activations[] = { nnIdentity };
+  const int     num_layers = 1;
+  const int     input_size = 1;
+  const nnLayer layers[]   = {
+      {.type = nnLinear, .linear = {.input_size = 1, .output_size = 1}}
+  };
 
-  nnNeuralNetwork* net = nnMakeNet(num_layers, layer_sizes, layer_activations);
+  nnNeuralNetwork* net = nnMakeNet(layers, num_layers, input_size);
   assert(net);
 
-  // Train.
+// Train.
 
-  // Try to learn the Y=X line.
-  #define N 2
-  const R inputs[N]  = { 0., 1. };
-  const R targets[N] = { 0., 1. };
+// Try to learn the Y=X line.
+#define N 2
+  const R inputs[N]  = {0., 1.};
+  const R targets[N] = {0., 1.};
 
   nnMatrix inputs_matrix  = nnMatrixMake(N, 1);
   nnMatrix targets_matrix = nnMatrixMake(N, 1);
@@ -31,26 +32,27 @@ TEST_CASE(neuralnet_train_linear_perceptron_test) {
   nnMatrixInit(&targets_matrix, targets);
 
   nnTrainingParams params = {
-    .learning_rate = 0.7,
-    .max_iterations = 10,
-    .seed = 0,
-    .weight_init = nnWeightInit01,
-    .debug = false,
+      .learning_rate  = 0.7,
+      .max_iterations = 10,
+      .seed           = 0,
+      .weight_init    = nnWeightInit01,
+      .debug          = false,
   };
 
   nnTrain(net, &inputs_matrix, &targets_matrix, &params);
 
-  const R weight = nnMatrixAt(&net->weights[0], 0, 0);
+  const R weight          = nnMatrixAt(&net->layers[0].linear.weights, 0, 0);
   const R expected_weight = 1.0;
-  printf("\nTrained network weight: %f, Expected: %f\n", weight, expected_weight);
+  printf(
+      "\nTrained network weight: %f, Expected: %f\n", weight, expected_weight);
   TEST_TRUE(double_eq(weight, expected_weight, WEIGHT_EPS));
 
   // Test.
 
-  nnQueryObject* query = nnMakeQueryObject(net, /*num_inputs=*/1);
+  nnQueryObject* query = nnMakeQueryObject(net, 1);
 
-  const R test_input[] = { 2.3 };
-  R test_output[1];
+  const R test_input[] = {2.3};
+  R       test_output[1];
   nnQueryArray(net, query, test_input, test_output);
 
   const R expected_output = test_input[0];