Added MnistForSpiNNaker

CSNN-Simulator/apps/MnistForSpiNNaker.cpp

+#include "Experiment.h"
+#include "dataset/Mnist.h"
+#include "stdp/Simplified.h"
+#include "layer/Convolution.h"
+#include "Distribution.h"
+#include "execution/OptimizedLayerByLayer.h"
+
+int main(int argc, char** argv) {
+
+	std::string name = "mnist_spinn";
+	float do_prune = atof(argv[1]);
+	float do_reinforcement = atof(argv[2]);
+
+	if(do_prune == 1){
+		name = name + "_pruned";
+	}
+	if(do_reinforcement == 1){
+		name = name + "_reinforced";
+	}
+	
+	Experiment<OptimizedLayerByLayer> experiment(argc, argv, name);
+
+	experiment.template input<LatencyCoding>();
+	
+	const char* input_path_ptr = "../Datasets/MNIST/";
+
+	if(input_path_ptr == nullptr) {
+		throw std::runtime_error("Require to define INPUT_PATH variable");
+	}
+
+	std::string input_path(input_path_ptr);
+
+	experiment.template add_train<dataset::Mnist>(input_path+"train-images.idx3-ubyte", input_path+"train-labels.idx1-ubyte");
+	experiment.template add_test<dataset::Mnist>(input_path+"t10k-images.idx3-ubyte", input_path+"t10k-labels.idx1-ubyte");
+
+	float th_lr = 1.0f;
+	float t_obj = 0.50f;
+	float alpha = 0.05f;
+	float alpha_p= 0.01f;
+	float alpha_n= 0.005f;
+	float beta_p= 1.5f;
+	float beta_n= 2.5f;
+	float prune_max_threshold = 0.7f;
+
+	auto& fc1 = experiment.template push_layer<layer::Convolution>("fc1", 28, 28, 400);
+	fc1.template parameter<float>("annealing").set(0.95f);
+	fc1.template parameter<float>("min_th").set(1.0f);
+	fc1.template parameter<float>("t_obj").set(t_obj);
+	fc1.template parameter<float>("lr_th").set(th_lr);
+	fc1.template parameter<float>("doPrune").set(do_prune);
+	fc1.template parameter<float>("doReinforcement").set(do_reinforcement);
+	fc1.template parameter<float>("prune_max_threshold").set(prune_max_threshold);
+	fc1.template parameter<Tensor<float>>("w").template distribution<distribution::Uniform>(0.0, 1.0);
+	fc1.template parameter<Tensor<float>>("th").template distribution<distribution::Gaussian>(8.0, 0.1);
+	fc1.template parameter<STDP>("stdp").template set<stdp::Simplified>(alpha_p,alpha_n,beta_p,beta_n);
+
+	alpha = alpha*2;
+
+	auto& fc2 = experiment.template push_layer<layer::Convolution>("fc2", 1, 1, 1600);
+	fc2.template parameter<float>("annealing").set(0.95f);
+	fc2.template parameter<float>("min_th").set(1.0f);
+	fc2.template parameter<float>("t_obj").set(t_obj);
+	fc2.template parameter<float>("lr_th").set(th_lr);
+	fc2.template parameter<float>("doPrune").set(do_prune);
+	fc2.template parameter<float>("doReinforcement").set(do_reinforcement);
+	fc2.template parameter<float>("prune_max_threshold").set(prune_max_threshold);
+	fc2.template parameter<Tensor<float>>("w").template distribution<distribution::Uniform>(0.0, 1.0);
+	fc2.template parameter<Tensor<float>>("th").template distribution<distribution::Gaussian>(10.0, 0.1);
+	fc2.template parameter<STDP>("stdp").template set<stdp::Simplified>(alpha_p,alpha_n,beta_p,beta_n);
+
+	experiment.add_train_step(fc1, 25);
+	experiment.add_train_step(fc2, 25);
+
+	experiment.run(10000);
+
+	return experiment.wait();
+
+}
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