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/neuralnet_test.c | 103 +++++++++++++++++++++++++----------------- 1 file changed, 62 insertions(+), 41 deletions(-) (limited to 'src/lib/test/neuralnet_test.c') diff --git a/src/lib/test/neuralnet_test.c b/src/lib/test/neuralnet_test.c index 14d9438..0f8d7b8 100644 --- a/src/lib/test/neuralnet_test.c +++ b/src/lib/test/neuralnet_test.c @@ -1,8 +1,8 @@ #include -#include #include "activation.h" #include "neuralnet_impl.h" +#include #include "test.h" #include "test_util.h" @@ -10,23 +10,31 @@ #include TEST_CASE(neuralnet_perceptron_test) { - const int num_layers = 1; - const int layer_sizes[] = { 1, 1 }; - const nnActivation layer_activations[] = { nnSigmoid }; - const R weights[] = { 0.3 }; + const int num_layers = 2; + const int input_size = 1; + const R weights[] = {0.3}; + const R biases[] = {0.0}; + const nnLayer layers[] = { + {.type = nnLinear, + .linear = + {.weights = nnMatrixFromArray(1, 1, weights), + .biases = nnMatrixFromArray(1, 1, biases)}}, + {.type = nnSigmoid}, + }; - nnNeuralNetwork* net = nnMakeNet(num_layers, layer_sizes, layer_activations); + nnNeuralNetwork* net = nnMakeNet(layers, num_layers, input_size); assert(net); - nnSetWeights(net, weights); - nnQueryObject* query = nnMakeQueryObject(net, /*num_inputs=*/1); + nnQueryObject* query = nnMakeQueryObject(net, 1); - const R input[] = { 0.9 }; - R output[1]; + const R input[] = {0.9}; + R output[1]; nnQueryArray(net, query, input, output); const R expected_output = sigmoid(input[0] * weights[0]); - printf("\nOutput: %f, Expected: %f\n", output[0], expected_output); + printf( + "\n[neuralnet_perceptron_test] Output: %f, Expected: %f\n", output[0], + expected_output); TEST_TRUE(double_eq(output[0], expected_output, EPS)); nnDeleteQueryObject(&query); @@ -34,53 +42,66 @@ TEST_CASE(neuralnet_perceptron_test) { } TEST_CASE(neuralnet_xor_test) { - const int num_layers = 2; - const int layer_sizes[] = { 2, 2, 1 }; - const nnActivation layer_activations[] = { nnRelu, nnIdentity }; - const R weights[] = { - 1, 1, 1, 1, // First (hidden) layer. - 1, -2 // Second (output) layer. - }; - const R biases[] = { - 0, -1, // First (hidden) layer. - 0 // Second (output) layer. + // First (hidden) layer. + const R weights0[] = {1, 1, 1, 1}; + const R biases0[] = {0, -1}; + // Second (output) layer. + const R weights1[] = {1, -2}; + const R biases1[] = {0}; + // Network. + const int num_layers = 3; + const int input_size = 2; + const nnLayer layers[] = { + {.type = nnLinear, + .linear = + {.weights = nnMatrixFromArray(2, 2, weights0), + .biases = nnMatrixFromArray(1, 2, biases0)}}, + {.type = nnRelu}, + {.type = nnLinear, + .linear = + {.weights = nnMatrixFromArray(2, 1, weights1), + .biases = nnMatrixFromArray(1, 1, biases1)}}, }; - nnNeuralNetwork* net = nnMakeNet(num_layers, layer_sizes, layer_activations); + nnNeuralNetwork* net = nnMakeNet(layers, num_layers, input_size); assert(net); - nnSetWeights(net, weights); - nnSetBiases(net, biases); // First layer weights. - TEST_EQUAL(nnMatrixAt(&net->weights[0], 0, 0), 1); - TEST_EQUAL(nnMatrixAt(&net->weights[0], 0, 1), 1); - TEST_EQUAL(nnMatrixAt(&net->weights[0], 0, 2), 1); - TEST_EQUAL(nnMatrixAt(&net->weights[0], 0, 3), 1); - // Second layer weights. - TEST_EQUAL(nnMatrixAt(&net->weights[1], 0, 0), 1); - TEST_EQUAL(nnMatrixAt(&net->weights[1], 0, 1), -2); + TEST_EQUAL(nnMatrixAt(&net->layers[0].linear.weights, 0, 0), 1); + TEST_EQUAL(nnMatrixAt(&net->layers[0].linear.weights, 0, 1), 1); + TEST_EQUAL(nnMatrixAt(&net->layers[0].linear.weights, 0, 2), 1); + TEST_EQUAL(nnMatrixAt(&net->layers[0].linear.weights, 0, 3), 1); + // Second linear layer (third layer) weights. + TEST_EQUAL(nnMatrixAt(&net->layers[2].linear.weights, 0, 0), 1); + TEST_EQUAL(nnMatrixAt(&net->layers[2].linear.weights, 0, 1), -2); // First layer biases. - TEST_EQUAL(nnMatrixAt(&net->biases[0], 0, 0), 0); - TEST_EQUAL(nnMatrixAt(&net->biases[0], 0, 1), -1); - // Second layer biases. - TEST_EQUAL(nnMatrixAt(&net->biases[1], 0, 0), 0); + TEST_EQUAL(nnMatrixAt(&net->layers[0].linear.biases, 0, 0), 0); + TEST_EQUAL(nnMatrixAt(&net->layers[0].linear.biases, 0, 1), -1); + // Second linear layer (third layer) biases. + TEST_EQUAL(nnMatrixAt(&net->layers[2].linear.biases, 0, 0), 0); // 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); -- cgit v1.2.3