Exemple #1
0
int main(int argc, char *argv[])
{
    QApplication a(argc, argv);

    std::string logfile = createLogDir("./logs");
    GLogger glog( argv[0], logfile.c_str() );
    std::cout << "Log to path: " << logfile << std::endl;


    MainWindow w;
    w.show();

    return a.exec();
}
Exemple #2
0
int main(int argc, char *argv[]){
  
	Params params;
  
	std::map<std::string, std::string> args;
	readArgs(argc, argv, args);
	if(args.find("algo")!=args.end()){
		params.algo = args["algo"];
	}else{
		params.algo = "qdMCNat";
	}

	if(args.find("inst_file")!=args.end())
		setParamsFromFile(args["inst_file"], args, params);
	else   
		setParams(params.algo, args, params);
  
	createLogDir(params.dir_path);
  
	gen.seed(params.seed);

	// Load the dataset
	MyMatrix X_train, X_valid;
	VectorXd Y_train, Y_valid;
	loadMnist(params.ratio_train, X_train, X_valid, Y_train, Y_valid);
	//loadCIFAR10(params.ratio_train, X_train, X_valid, Y_train, Y_valid);
	//loadLightCIFAR10(params.ratio_train, X_train, X_valid, Y_train, Y_valid);
  
	// ConvNet parameters
	std::vector<ConvLayerParams> conv_params;
	ConvLayerParams conv_params1;
	conv_params1.Hf = 5;
	conv_params1.stride = 1;
	conv_params1.n_filter = 20;
	conv_params1.padding = 0;
	conv_params.push_back(conv_params1);
  
	ConvLayerParams conv_params2;
	conv_params2.Hf = 5;
	conv_params2.stride = 1;
	conv_params2.n_filter = 50;
	conv_params2.padding = 0;
	conv_params.push_back(conv_params2);

	std::vector<PoolLayerParams> pool_params;
	PoolLayerParams pool_params1;
	pool_params1.Hf = 2;
	pool_params1.stride = 2;
	pool_params.push_back(pool_params1);

	PoolLayerParams pool_params2;
	pool_params2.Hf = 2;
	pool_params2.stride = 2;
	pool_params.push_back(pool_params2);
  
	const unsigned n_conv_layer = conv_params.size();
  
	for(unsigned l = 0; l < conv_params.size(); l++){

		if(l==0){
			conv_params[l].filter_size = conv_params[l].Hf * conv_params[l].Hf * params.img_depth;
			conv_params[l].N = (params.img_width - conv_params[l].Hf + 2*conv_params[l].padding)/conv_params[l].stride + 1;
		}
		else{
			conv_params[l].filter_size = conv_params[l].Hf * conv_params[l].Hf * conv_params[l-1].n_filter;
			conv_params[l].N = (pool_params[l-1].N - conv_params[l].Hf + 2*conv_params[l].padding)/conv_params[l].stride + 1;
		}
		pool_params[l].N = (conv_params[l].N - pool_params[l].Hf)/pool_params[l].stride + 1;
	}
  
	// Neural Network parameters
	const unsigned n_training = X_train.rows();
	const unsigned n_valid = X_valid.rows();
	const unsigned n_feature = X_train.cols();
	const unsigned n_label = Y_train.maxCoeff() + 1;
  
	params.nn_arch.insert(params.nn_arch.begin(),conv_params[n_conv_layer-1].n_filter * pool_params[n_conv_layer-1].N * pool_params[n_conv_layer-1].N);
	params.nn_arch.push_back(n_label);
	const unsigned n_layers = params.nn_arch.size();
  
	// Optimization parameter
	const int n_train_batch = ceil(n_training/(float)params.train_minibatch_size);
	const int n_valid_batch = ceil(n_valid/(float)params.valid_minibatch_size);
	double prev_loss = std::numeric_limits<double>::max();
	double eta = params.eta;

	// Create the convolutional layer
	std::vector<MyMatrix> conv_W(n_conv_layer);
	std::vector<MyMatrix> conv_W_T(n_conv_layer);
	std::vector<MyVector> conv_B(n_conv_layer);
  
	// Create the neural network
	MyMatrix W_out(params.nn_arch[n_layers-2],n_label);
	std::vector<MySpMatrix> W(n_layers-2);
	std::vector<MySpMatrix> Wt(n_layers-2);
	std::vector<MyVector> B(n_layers-1);

	double init_sigma = 0.;
	ActivationFunction act_func;
	ActivationFunction eval_act_func;
	if(params.act_func_name=="sigmoid"){
		init_sigma = 4.0;
		act_func = std::bind(logistic,true,_1,_2,_3);
		eval_act_func = std::bind(logistic,false,_1,_2,_3);
	}else if(params.act_func_name=="tanh"){
		init_sigma = 1.0;
		act_func = std::bind(my_tanh,true,_1,_2,_3);
		eval_act_func = std::bind(my_tanh,false,_1,_2,_3);
	}else if(params.act_func_name=="relu"){
		init_sigma = 1.0; // TODO: Find the good value
		act_func = std::bind(relu,true,_1,_2,_3);
		eval_act_func = std::bind(relu,false,_1,_2,_3);
	}else{
		std::cout << "Not implemented yet!" << std::endl;
		assert(false);
	}

	std::cout << "Initializing the network... ";
	params.n_params = initNetwork(params.nn_arch, params.act_func_name, params.sparsity, conv_params, pool_params, W_out, W, Wt, B, conv_W, conv_W_T, conv_B); // TODO: Init the conv bias

	// Deep copy of parameters for the adaptive rule
	std::vector<MyMatrix> mu_dW(n_layers-1);
	std::vector<MyVector> mu_dB(n_layers-1);

	MyMatrix pW_out = W_out;
	std::vector<MySpMatrix> pW = W;
	std::vector<MySpMatrix> pWt = Wt;
	std::vector<MyVector> pB = B;

	MyMatrix ppMii_out, ppM0i_out;
	MyVector ppM00_out;
  
	std::vector<MySpMatrix> ppMii,ppM0i;
	std::vector<MyVector> ppM00;

	MyMatrix pMii_out,pM0i_out;
	MyVector pM00_out;
  
	std::vector<MySpMatrix> pMii,pM0i;
	std::vector<MyVector> pM00;

	std::vector<MyMatrix> conv_ppMii, conv_ppM0i;
	std::vector<MyVector> conv_ppM00;

	std::vector<MyMatrix> conv_pMii, conv_pM0i;
	std::vector<MyVector> conv_pM00;
  
	// Convert the labels to one-hot vector
	MyMatrix one_hot = MyMatrix::Zero(n_training, n_label);
	labels2oneHot(Y_train,one_hot);
  
	// Configure the logger 
	std::ostream* logger;
	if(args.find("verbose")!=args.end()){
		getOutput("",logger);
	}else{
		getOutput(params.file_path,logger);
	}

	double cumul_time = 0.;
  
	printDesc(params, logger);
	printConvDesc(params, conv_params, pool_params, logger);
	std::cout << "Starting the learning phase... " << std::endl;
	*logger << "Epoch Time(s) train_loss train_accuracy valid_loss valid_accuracy eta" << std::endl;
  
	for(unsigned i = 0; i < params.n_epoch; i++){
		for(unsigned j = 0; j < n_train_batch; j++){
      
			// Mini-batch creation
			unsigned curr_batch_size = 0;
			MyMatrix X_batch, one_hot_batch;
			getMiniBatch(j, params.train_minibatch_size, X_train, one_hot, params, conv_params[0], curr_batch_size, X_batch, one_hot_batch);
      
			double prev_time = gettime();

			// Forward propagation for conv layer
			std::vector<std::vector<unsigned>> poolIdxX1(n_conv_layer);
			std::vector<std::vector<unsigned>> poolIdxY1(n_conv_layer);
      
			MyMatrix z0;
			std::vector<MyMatrix> conv_A(conv_W.size());
			std::vector<MyMatrix> conv_Ap(conv_W.size());
			convFprop(curr_batch_size, conv_params, pool_params, act_func, conv_W, conv_B, X_batch, conv_A, conv_Ap, z0, poolIdxX1, poolIdxY1);
            
			// Forward propagation
			std::vector<MyMatrix> Z(n_layers-1);
			std::vector<MyMatrix> A(n_layers-2);
			std::vector<MyMatrix> Ap(n_layers-2);
			fprop(params.dropout_flag, act_func, W, W_out, B, z0, Z, A, Ap);
      
			// Compute the output and the error
			MyMatrix out;
			softmax(Z[n_layers-2], out);
      
			std::vector<MyMatrix> gradB(n_layers-1);
			gradB[n_layers-2] = out - one_hot_batch;

			// Backpropagation
			bprop(Wt, W_out, Ap, gradB);

			// Backpropagation for conv layer
			std::vector<MyMatrix> conv_gradB(conv_W.size());
			MyMatrix layer_gradB = (gradB[0] * W[0].transpose());
			MyMatrix pool_gradB;
			layer2pool(curr_batch_size, pool_params[conv_W.size()-1].N, conv_params[conv_W.size()-1].n_filter, layer_gradB, pool_gradB);
      
			convBprop(curr_batch_size, conv_params, pool_params, conv_W_T, conv_Ap, pool_gradB, conv_gradB, poolIdxX1, poolIdxY1);
      
			if(params.algo == "bprop"){
				update(eta, gradB, A, z0, params.regularizer, params.lambda, W_out, W, Wt, B);
				convUpdate(curr_batch_size, eta, conv_params, conv_gradB, conv_A, X_batch, "", 0., conv_W, conv_W_T, conv_B);
	
			}else{

				// Compute the metric
				std::vector<MyMatrix> metric_gradB(n_layers-1);
				std::vector<MyMatrix> metric_conv_gradB(conv_params.size());

				if(params.algo=="qdMCNat"){

					// Monte-Carlo Approximation of the metric
					std::vector<MyMatrix> mc_gradB(n_layers-1);
					computeMcError(out, mc_gradB[n_layers-2]);

					// Backpropagation
					bprop(Wt, W_out, Ap, mc_gradB);

					for(unsigned k = 0; k < gradB.size(); k++){
						metric_gradB[k] = mc_gradB[k].array().square();
					}

					// Backpropagation for conv layer
					std::vector<MyMatrix> mc_conv_gradB(conv_W.size());
					MyMatrix mc_layer_gradB = (mc_gradB[0] * W[0].transpose());
					MyMatrix mc_pool_gradB;
					layer2pool(curr_batch_size, pool_params[conv_W.size()-1].N, conv_params[conv_W.size()-1].n_filter, mc_layer_gradB, mc_pool_gradB);
	  
					convBprop(curr_batch_size, conv_params, pool_params, conv_W_T, conv_Ap, mc_pool_gradB, mc_conv_gradB, poolIdxX1, poolIdxY1);
	  
					for(unsigned k = 0; k < conv_params.size(); k++){
						metric_conv_gradB[k] = mc_conv_gradB[k].array().square();
					}
				}	
				else if(params.algo=="qdop"){

					for(unsigned k = 0; k < conv_params.size(); k++){
						metric_conv_gradB[k] = conv_gradB[k].array().square();
					}
					for(unsigned k = 0; k < gradB.size(); k++){
						metric_gradB[k] = gradB[k].array().square();
					}
				}
				else if(params.algo=="qdNat"){
	  
					for(unsigned k = 0; k < conv_params.size(); k++){
						metric_conv_gradB[k] = conv_gradB[k].array().square();
					}

					for(unsigned k = 0; k < metric_gradB.size(); k++){
						metric_gradB[k] = MyMatrix::Zero(gradB[k].rows(),gradB[k].cols());
					}

					for(unsigned l = 0; l < n_label; l++){
						MyMatrix fisher_ohbatch = MyMatrix::Zero(curr_batch_size, n_label);
						fisher_ohbatch.col(l).setOnes();

						std::vector<MyMatrix> fgradB(n_layers-1);
						fgradB[n_layers-2] = out - fisher_ohbatch;
						bprop(Wt, W_out, Ap, fgradB);

						// Backpropagation for conv layer
						std::vector<MyMatrix> fisher_conv_gradB(conv_W.size());
						MyMatrix fisher_layer_gradB = (fgradB[0] * W[0].transpose());
						MyMatrix fisher_pool_gradB;
						layer2pool(curr_batch_size, pool_params[conv_W.size()-1].N, conv_params[conv_W.size()-1].n_filter, fisher_layer_gradB, fisher_pool_gradB);
	    
						convBprop(curr_batch_size, conv_params, pool_params, conv_W_T, conv_Ap, fisher_pool_gradB, fisher_conv_gradB, poolIdxX1, poolIdxY1);

						for(unsigned k = 0; k < conv_params.size(); k++){
							MyMatrix fisher_conv_gradB_sq = fisher_conv_gradB[k].array().square();
							for(unsigned m = 0; m < out.rows(); m++){
								for(unsigned f = 0; f < conv_params[k].n_filter; f++){
									for(unsigned n = 0; n < conv_params[k].N * conv_params[k].N; n++){
										fisher_conv_gradB_sq(f,m*conv_params[k].N*conv_params[k].N+n) *= out(m,l);
									}
								}
							}
							metric_conv_gradB[k] += fisher_conv_gradB_sq;
						}
	    
						for(unsigned k = 0; k < W.size(); k++){
							const unsigned rev_k = n_layers - k - 2;
							metric_gradB[rev_k] += (fgradB[rev_k].array().square().array().colwise() * out.array().col(l)).matrix();
						}
					}
				}
	
				bool init_flag = false;
				if(i == 0 && j == 0 && !params.init_metric_id){
					init_flag = true;
				}

				std::vector<MyMatrix> conv_Mii(conv_params.size());
				std::vector<MyMatrix> conv_M0i(conv_params.size());
				std::vector<MyVector> conv_M00(conv_params.size());
	
				buildConvQDMetric(curr_batch_size, metric_conv_gradB, conv_A, X_batch, conv_W, params.matrix_reg, conv_Mii, conv_M0i, conv_M00);

				updateConvMetric(init_flag, params.metric_gamma, conv_pMii, conv_pM0i, conv_pM00, conv_Mii, conv_M0i, conv_M00);

				MyMatrix Mii_out, M0i_out;
				MyVector M00_out;
				std::vector<MySpMatrix> Mii(W.size());
				std::vector<MySpMatrix> M0i(W.size());
				std::vector<MyVector> M00(W.size());

				buildQDMetric(metric_gradB, A, z0, W_out, W, params.matrix_reg, Mii_out, M0i_out, M00_out, Mii, M0i, M00);

				updateMetric(init_flag, params.metric_gamma, Mii_out, M0i_out, M00_out, Mii, M0i, M00, pMii_out, pM0i_out, pM00_out, pMii, pM0i, pM00);
				update(eta, gradB, A, z0, params.regularizer, params.lambda, W_out, W, Wt, B, Mii_out, M0i_out, M00_out, Mii, M0i, M00);
			}
      
			double curr_time = gettime();
			cumul_time += curr_time - prev_time;      
      
			if(params.minilog_flag){
	
				double train_loss = 0.;
				double train_accuracy = 0.;
				double valid_loss = 0.;
				double valid_accuracy = 0.;
				evalModel(eval_act_func, params, n_train_batch, n_training, X_train, Y_train, conv_params, pool_params, conv_W, conv_B, W_out, W, B, train_loss, train_accuracy);
				evalModel(eval_act_func, params, n_valid_batch, n_valid, X_valid, Y_valid, conv_params, pool_params, conv_W, conv_B, W_out, W, B, valid_loss, valid_accuracy);
	
				// Logging
				*logger << i + float(j)/n_train_batch << " " << cumul_time << " " << train_loss <<  " " << train_accuracy << " " << valid_loss <<  " " << valid_accuracy << " " << eta << std::endl;
	
			}
		}
		if(!params.minilog_flag || params.adaptive_flag){
			double train_loss = 0.;
			double train_accuracy = 0.;
			double valid_loss = 0.;
			double valid_accuracy = 0.;
			evalModel(eval_act_func, params, n_train_batch, n_training, X_train, Y_train, conv_params, pool_params, conv_W, conv_B, W_out, W, B, train_loss, train_accuracy);
			evalModel(eval_act_func, params, n_valid_batch, n_valid, X_valid, Y_valid, conv_params, pool_params, conv_W, conv_B, W_out, W, B, valid_loss, valid_accuracy);
      
			// if(params.adaptive_flag)
			// 	adaptiveRule(train_loss, prev_loss, eta, W, B, pMii, pM0i, pM00, pW, pB, ppMii, ppM0i, ppM00);
      
			// Logging
			if(!params.minilog_flag){
				*logger << i  << " " << cumul_time << " " << train_loss <<  " " << train_accuracy << " " << valid_loss <<  " " << valid_accuracy << " " << eta << std::endl;
			}
		}
	}
}