output_file=folder_images + "/training_weights.avi", fps=2) # pf.create_video_from_images(video_fotograms_folder3,output_file = "./training_Variational_weights.avi", fps = 2) pf.create_video_from_images(video_fotograms_folder4, output_file=folder_images + "/training_Variational_weights.avi", fps=2) """ ###################### SAVE MODEL #################### """ myGeneralVBModel.save(folder_model + "model_parameters_epoch:%i.pk" % i) """ ##################### STATIC PLOTS ###################### """ # Set it in no training myGeneralVBModel.eval() if (plot_predictions): ####### PLOT THE LEARNT FUNCTION ############ pf.create_Bayesian_analysis_charts(myGeneralVBModel, X_data_tr, Y_data_tr, X_data_val, Y_data_val, tr_data_loss, val_data_loss, KL_loss, xgrid_real_func, ygrid_real_func, folder_images) if (plot_weights): ####### PLOT ANALYSIS OF THE WEIGHTS ########### pf.plot_weights_network(myGeneralVBModel, folder_images) pf.plot_Variational_weights_network(myGeneralVBModel, folder_images)
# pf.create_video_from_images(video_fotograms_folder3,output_file = "./training_Variational_weights.avi", fps = 2) pf.create_video_from_images(video_fotograms_folder4,output_file = folder_images + "/training_Variational_weights.avi", fps = 2) """ ###################### SAVE MODEL #################### """ myHalfBayesianMLP.save(folder_model + "model_parameters_epoch:%i.pk"%i) """ ##################### STATIC PLOTS ###################### """ # Set it in no training myHalfBayesianMLP.eval() if (plot_predictions): ####### PLOT THE LEARNT FUNCTION ############ pf.create_Bayesian_analysis_charts(myHalfBayesianMLP, X_data_tr, Y_data_tr, X_data_val, Y_data_val, tr_loss, val_loss, KL_loss, xgrid_real_func, ygrid_real_func, folder_images) if (plot_weights): ####### PLOT ANALYSIS OF THE WEIGHTS ########### pf.plot_weights_network(myHalfBayesianMLP, folder_images) pf.plot_Variational_weights_network(myHalfBayesianMLP, folder_images)
fps=2) """ ###################### SAVE MODEL #################### """ myBasicMLP.save(folder_model + "model_parameters_epoch:%i.pk" % i) """ ##################### STATIC PLOTS ###################### """ # Set it in no training myBasicMLP.eval() if (plot_predictions): ####### PLOT THE LEARNT FUNCTION ############ x_grid = np.linspace( np.min([X_data_tr]) - 1, np.max([X_data_val]) + 10, 10000) y_grid = myBasicMLP.predict( torch.tensor(x_grid.reshape(-1, 1), device=device, dtype=dtype)).detach().numpy() pf.plot_learnt_function(X_data_tr, Y_data_tr, X_data_val, Y_data_val, x_grid, y_grid, cf_a, folder_images) if (plot_evolution_loss): ####### PLOT THE EVOLUTION OF RMSE ############ pf.plot_evolution_RMSE(tr_loss, val_loss, cf_a, folder_images) if (plot_weights): ####### PLOT ANALYSIS OF THE WEIGHTS ########### pf.plot_weights_network(myBasicMLP, folder_images)
if(create_video_training): # pf.create_video_from_images(video_fotograms_folder_weights,output_file =folder_images + "/training_weights.avi", fps = 2) pf.create_video_from_images(video_fotograms_folder_training,output_file = folder_images + "/training_Variational_weights.avi", fps = 2) """ ###################### SAVE MODEL #################### """ myGeneralVBModel.save(folder_model + "model_parameters_epoch:%i.pk"%i) """ ##################### STATIC PLOTS ###################### """ # Set it in no training myGeneralVBModel.eval() if (plot_predictions): ####### PLOT THE LEARNT FUNCTION ############ pf.create_Bayesian_analysis_charts(myGeneralVBModel, X_data_tr, Y_data_tr, X_data_val, Y_data_val, tr_data_loss, val_data_loss, KL_loss,final_loss_tr,final_loss_val, xgrid_real_func, ygrid_real_func, folder_images) if (plot_weights): ####### PLOT ANALYSIS OF THE WEIGHTS ########### pf.plot_weights_network(myGeneralVBModel, folder_images) pf.plot_Variational_weights_network(myGeneralVBModel, folder_images)
pf.create_video_from_images(video_fotograms_folder,output_file = "./training_loss.avi", fps = 2) pf.create_video_from_images(video_fotograms_folder2,output_file = "./training_weights.avi", fps = 2) """ ###################### SAVE MODEL #################### """ myBasicMLP.save(folder_model + "model_parameters_epoch:%i.pk"%i) """ ##################### STATIC PLOTS ###################### """ # Set it in no training myBasicMLP.eval() if (plot_predictions): ####### PLOT THE LEARNT FUNCTION ############ x_grid = np.linspace(np.min([X_data_tr]) -1, np.max([X_data_val]) +10, 10000) y_grid = myBasicMLP.predict(torch.tensor(x_grid.reshape(-1,1),device=device, dtype=dtype)).detach().numpy() pf.plot_learnt_function(X_data_tr, Y_data_tr, X_data_val, Y_data_val, x_grid, y_grid, cf_a, folder_images) if(plot_evolution_loss): ####### PLOT THE EVOLUTION OF RMSE ############ pf.plot_evolution_RMSE(tr_loss, val_loss, cf_a,folder_images) if (plot_weights): ####### PLOT ANALYSIS OF THE WEIGHTS ########### pf.plot_weights_network(myBasicMLP, folder_images)