From 653e98e029a0d0f110b0ac599e50406060bb0f87 Mon Sep 17 00:00:00 2001
From: 3gg <3gg@shellblade.net>
Date: Sat, 16 Dec 2023 10:21:16 -0800
Subject: Decouple activations from linear layer.

---
 src/lib/test/train_sigmoid_test.c | 46 +++++++++++++++++++++------------------
 1 file changed, 25 insertions(+), 21 deletions(-)

(limited to 'src/lib/test/train_sigmoid_test.c')

diff --git a/src/lib/test/train_sigmoid_test.c b/src/lib/test/train_sigmoid_test.c
index 588e7ca..39a84b0 100644
--- a/src/lib/test/train_sigmoid_test.c
+++ b/src/lib/test/train_sigmoid_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"
@@ -11,21 +11,24 @@
 #include <assert.h>
 
 TEST_CASE(neuralnet_train_sigmoid_test) {
-  const int num_layers = 1;
-  const int layer_sizes[] = { 1, 1 };
-  const nnActivation layer_activations[] = { nnSigmoid };
+  const int     num_layers = 2;
+  const int     input_size = 1;
+  const nnLayer layers[]   = {
+      {.type = nnLinear, .linear = {.input_size = 1, .output_size = 1}},
+      {.type = nnSigmoid},
+  };
 
-  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 sigmoid function.
-  #define N 3
+// Try to learn the sigmoid function.
+#define N 3
   R inputs[N];
   R targets[N];
   for (int i = 0; i < N; ++i) {
-    inputs[i] = lerp(-1, +1, (R)i / (R)(N-1));
+    inputs[i]  = lerp(-1, +1, (R)i / (R)(N - 1));
     targets[i] = sigmoid(inputs[i]);
   }
 
@@ -35,29 +38,30 @@ TEST_CASE(neuralnet_train_sigmoid_test) {
   nnMatrixInit(&targets_matrix, targets);
 
   nnTrainingParams params = {
-    .learning_rate = 0.9,
-    .max_iterations = 100,
-    .seed = 0,
-    .weight_init = nnWeightInit01,
-    .debug = false,
+      .learning_rate  = 0.9,
+      .max_iterations = 100,
+      .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[] = { 0.3 };
-  R test_output[1];
+  const R test_input[] = {0.3};
+  R       test_output[1];
   nnQueryArray(net, query, test_input, test_output);
 
-  const R expected_output = 0.574442516811659;  // sigmoid(0.3)
+  const R expected_output = 0.574442516811659; // sigmoid(0.3)
   printf("Output: %f, Expected: %f\n", test_output[0], expected_output);
   TEST_TRUE(double_eq(test_output[0], expected_output, OUTPUT_EPS));
 
-- 
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