Decouple activations from linear layer.

1 files changed, 35 insertions, 20 deletions

diff --git a/src/lib/test/train_xor_test.c b/src/lib/test/train_xor_test.c
index 6ddc6e0..78695a3 100644
--- a/src/lib/test/train_xor_test.c
+++ b/src/lib/test/train_xor_test.c
@@ -1,9 +1,9 @@
 #include <neuralnet/train.h>
 
-#include <neuralnet/matrix.h>
-#include <neuralnet/neuralnet.h>
 #include "activation.h"
 #include "neuralnet_impl.h"
+#include <neuralnet/matrix.h>
+#include <neuralnet/neuralnet.h>
 
 #include "test.h"
 #include "test_util.h"
@@ -11,18 +11,27 @@
 #include <assert.h>
 
 TEST_CASE(neuralnet_train_xor_test) {
-  const int num_layers = 2;
-  const int layer_sizes[] = { 2, 2, 1 };
-  const nnActivation layer_activations[] = { nnRelu, nnIdentity };
+  const int     num_layers = 3;
+  const int     input_size = 2;
+  const nnLayer layers[]   = {
+      {.type = nnLinear, .linear = {.input_size = 2, .output_size = 2}},
+      {.type = nnRelu},
+      {.type = nnLinear, .linear = {.input_size = 2, .output_size = 1}}
+  };
 
-  nnNeuralNetwork* net = nnMakeNet(num_layers, layer_sizes, layer_activations);
+  nnNeuralNetwork* net = nnMakeNet(layers, num_layers, input_size);
   assert(net);
 
   // Train.
 
-  #define N 4
-  const R inputs[N][2]  = { { 0., 0. }, { 0., 1. }, { 1., 0. }, { 1., 1. } };
-  const R targets[N] = { 0., 1., 1., 0. };
+#define N 4
+  const R inputs[N][2] = {
+      {0., 0.},
+      {0., 1.},
+      {1., 0.},
+      {1., 1.}
+  };
+  const R targets[N] = {0., 1., 1., 0.};
 
   nnMatrix inputs_matrix  = nnMatrixMake(N, 2);
   nnMatrix targets_matrix = nnMatrixMake(N, 1);
@@ -30,31 +39,37 @@ TEST_CASE(neuralnet_train_xor_test) {
   nnMatrixInit(&targets_matrix, targets);
 
   nnTrainingParams params = {
-    .learning_rate = 0.1,
-    .max_iterations = 500,
-    .seed = 0,
-    .weight_init = nnWeightInit01,
-    .debug = false,
+      .learning_rate  = 0.1,
+      .max_iterations = 500,
+      .seed           = 0,
+      .weight_init    = nnWeightInit01,
+      .debug          = false,
   };
 
   nnTrain(net, &inputs_matrix, &targets_matrix, &params);
 
   // Test.
 
-  #define M 4
+#define M 4
 
-  nnQueryObject* query = nnMakeQueryObject(net, /*num_inputs=*/M);
+  nnQueryObject* query = nnMakeQueryObject(net, M);
 
-  const R test_inputs[M][2] = { { 0., 0. }, { 1., 0. }, { 0., 1. }, { 1., 1. } };
+  const R test_inputs[M][2] = {
+      {0., 0.},
+      {1., 0.},
+      {0., 1.},
+      {1., 1.}
+  };
   nnMatrix test_inputs_matrix = nnMatrixMake(M, 2);
   nnMatrixInit(&test_inputs_matrix, (const R*)test_inputs);
   nnQuery(net, query, &test_inputs_matrix);
 
-  const R expected_outputs[M] = { 0., 1., 1., 0. };
+  const R expected_outputs[M] = {0., 1., 1., 0.};
   for (int i = 0; i < M; ++i) {
     const R test_output = nnMatrixAt(nnNetOutputs(query), i, 0);
-    printf("\nInput: (%f, %f), Output: %f, Expected: %f\n",
-      test_inputs[i][0], test_inputs[i][1], test_output, expected_outputs[i]);
+    printf(
+        "\nInput: (%f, %f), Output: %f, Expected: %f\n", test_inputs[i][0],
+        test_inputs[i][1], test_output, expected_outputs[i]);
   }
   for (int i = 0; i < M; ++i) {
     const R test_output = nnMatrixAt(nnNetOutputs(query), i, 0);