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Minimize Neural Layer Unit Test Redundancies (shogun-toolbox#4424)
* Fixed redundancies in RectifiedLinear and Softmax Layer unit tests * Add common fixture class + formatting * Remove random seed
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| /* | ||
| * This software is distributed under BSD 3-clause license (see LICENSE file). | ||
| * | ||
| * Authors: Saatvik Shah | ||
| */ | ||
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| #ifndef NEURAL_LAYER_TEST_FIXTURE_H | ||
| #define NEURAL_LAYER_TEST_FIXTURE_H | ||
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| #include <shogun/lib/SGMatrix.h> | ||
| #include <shogun/lib/SGVector.h> | ||
| #include <shogun/mathematics/Math.h> | ||
| #include <shogun/neuralnets/NeuralInputLayer.h> | ||
| #include <shogun/neuralnets/NeuralLinearLayer.h> | ||
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| #include <gtest/gtest.h> | ||
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| #include <memory> | ||
| #include <tuple> | ||
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| using namespace shogun; | ||
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| class NeuralLayerTestFixture : public ::testing::Test | ||
| { | ||
| public: | ||
| template <typename T> | ||
| auto create_rand_matrix( | ||
| int32_t num_rows, int32_t num_cols, T lower_bound, T upper_bound) | ||
| { | ||
| auto data_batch = SGMatrix<T>(num_rows, num_cols); | ||
| for (size_t i = 0; i < num_rows * num_cols; i++) | ||
| { | ||
| data_batch[i] = CMath::random(lower_bound, upper_bound); | ||
| } | ||
| return data_batch; | ||
| } | ||
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| /** | ||
| * Generates a random dataset according to provided specifications. | ||
| * Then performs the boilerplate needed to setup the input layer | ||
| * for this dataset. | ||
| * @tparam T: Type of input data eg. float64_t | ||
| * @param num_features: For the randomized dataset | ||
| * @param num_samples: For the randomized dataset | ||
| * @param lower_bound/upper_bound: Dataset generated has values between: | ||
| * [lower_bound, upper_bound] | ||
| * @param add_to_layers: Should this layer be added to the dynamic layer | ||
| * list(`m_layers`) | ||
| * This is normally done when expecting to connect this layer ahead to some | ||
| * other layer | ||
| * @return: The randomized dataset and corresponding Neural input layer | ||
| */ | ||
| template <typename T> | ||
| auto setup_input_layer( | ||
| int32_t num_features, int32_t num_samples, T lower_bound, T upper_bound, | ||
| bool add_to_layers = true) | ||
| { | ||
| auto data_batch = create_rand_matrix( | ||
| num_features, num_samples, lower_bound, upper_bound); | ||
| auto input_layer = new CNeuralInputLayer(data_batch.num_rows); | ||
| input_layer->set_batch_size(data_batch.num_cols); | ||
| input_layer->compute_activations(data_batch); | ||
| if (add_to_layers) | ||
| { | ||
| m_layers->append_element(input_layer); | ||
| } | ||
| return std::make_tuple(data_batch, input_layer); | ||
| } | ||
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| /** | ||
| * Initializes Linear layer metadata according to specifications provided | ||
| * @param layer: The layer to initialize | ||
| * @param input_indices: the indices of layers from `m_layers` that are | ||
| * inputs to this layer | ||
| * @param batch_size: Batch size for this layer | ||
| * @param sigma: The parameters are initialized as Normal(0 mean, sigma | ||
| * stdev) | ||
| * @param add_to_layers: Whether this layer should be added to the dynamic | ||
| * layer list(`m_layers`) | ||
| * This is normally done when expecting to connect this layer ahead to some | ||
| * other layer. | ||
| * @return: The initialized parameters | ||
| */ | ||
| auto init_linear_layer( | ||
| CNeuralLinearLayer* layer, const SGVector<int32_t>& input_indices, | ||
| int32_t batch_size, double sigma, bool add_to_layers) const | ||
| { | ||
| if (add_to_layers) | ||
| { | ||
| m_layers->append_element(layer); | ||
| } | ||
| layer->initialize_neural_layer(m_layers.get(), input_indices); | ||
| SGVector<float64_t> params(layer->get_num_parameters()); | ||
| SGVector<bool> param_regularizable(layer->get_num_parameters()); | ||
| layer->initialize_parameters(params, param_regularizable, sigma); | ||
| layer->set_batch_size(batch_size); | ||
| layer->compute_activations(params, m_layers.get()); | ||
| return params; | ||
| } | ||
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| // dynamic list of layers | ||
| std::unique_ptr<CDynamicObjectArray> m_layers; | ||
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| protected: | ||
| void SetUp() final | ||
| { | ||
| m_layers = std::make_unique<CDynamicObjectArray>(); | ||
| } | ||
| }; | ||
| #endif // NEURAL_LAYER_TEST_FIXTURE_H |
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