test_nn.cpp File Reference

#include <utec/algebra/tensor.h>
#include <utec/nn/activation.h>
#include <utec/nn/dense.h>
#include <utec/nn/loss.h>
#include <utec/nn/neural_network.h>
#include <utec/nn/optimizer.h>
#include <catch_amalgamated.hpp>
#include <random>
#include <sstream>
#include <utec/nn/kan.h>

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Classes
class	Dense< T >
class	NeuralNetwork< T >
	Clase que representa una red neuronal completamente conectada. More...
class	Sigmoid< T >
	Capa de activación Sigmoid. Convierte cada valor en el rango (0, 1) abierto usando la función logística. No tiene parámetros entrenables. More...
class	Kan< T >

Functions
	TEST_CASE ("neural network predict simple", "[neural]")
	TEST_CASE ("Dense + Sigmoid with batch size 2", "[neural]")
	TEST_CASE ("Dense layer with constant input", "[neural]")
	TEST_CASE ("Empty network returns input", "[neural]")
	TEST_CASE ("Kan layer basic forward", "[kan]")
	TEST_CASE ("neural network trains and reduces loss", "[neural][train]")
	TEST_CASE ("neural network save/load consistency", "[neural][save]")
	TEST_CASE ("neural network with Kan layer", "[neural][kan]")
	TEST_CASE ("neural network backward call", "[neural][backward]")
	TEST_CASE ("neural network learns linear function f(x) = 2x", "[neural][train]")
	TEST_CASE ("neural network learns sin(x) using ReLU", "[neural][train]")

Function Documentation

TEST_CASE() [1/11]

TEST_CASE	(	"Dense + Sigmoid with batch size 2"	,
		""	[neural] )

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TEST_CASE() [2/11]

TEST_CASE	(	"Dense layer with constant input"	,
		""	[neural] )

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TEST_CASE() [3/11]

TEST_CASE	(	"Empty network returns input"	,
		""	[neural] )

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TEST_CASE() [4/11]

TEST_CASE	(	"Kan layer basic forward"	,
		""	[kan] )

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TEST_CASE() [5/11]

TEST_CASE	(	"neural network backward call"	,
		""	[neural][backward] )

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TEST_CASE() [6/11]

TEST_CASE

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)

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TEST_CASE() [7/11]

TEST_CASE	(	"neural network learns sin(x) using ReLU"	,
		""	[neural][train] )

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TEST_CASE() [8/11]

TEST_CASE	(	"neural network predict simple"	,
		""	[neural] )

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TEST_CASE() [9/11]

TEST_CASE	(	"neural network save/load consistency"	,
		""	[neural][save] )

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TEST_CASE() [10/11]

TEST_CASE	(	"neural network trains and reduces loss"	,
		""	[neural][train] )

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TEST_CASE() [11/11]

TEST_CASE	(	"neural network with Kan layer"	,
		""	[neural][kan] )

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