video_fotograms_folder2, i + 1)
# pf.create_image_Variational_weights_network(myHalfBayesianMLP, video_fotograms_folder3, i+1)
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, video_fotograms_folder4,
i + 1)
if (save_model_per_epochs):
myGeneralVBModel.save(folder_model +
"model_parameters_epoch:%i.pk" % i)
if (create_video_training): #
# 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=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
final_loss_val.append(myGeneralVBModel.get_loss(Xval, Yval).item())
##### CREATE VIDEO OF TRAINING ###############
if (create_video_training):
if ((i+1)%Step_video == 0):
myGeneralVBModel.eval()
# pf.create_image_weights_epoch(myGeneralVBModel, video_fotograms_folder_weights, i+1)
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,
video_fotograms_folder_training, i+1)
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 ############
x_grid = np.linspace(np.min([X_data_tr]) -1, np.max([X_data_val]) +1, 100)
y_grid = myBasicMLP.predict(torch.tensor(x_grid.reshape(-1,1),device=cf_a.device, dtype=cf_a.dtype)).detach().numpy()
pf.create_image_training_epoch(X_data_tr, Y_data_tr, X_data_val, Y_data_val,
tr_loss, val_loss, x_grid, y_grid, cf_a,
video_fotograms_folder, i)
pf.create_image_weights_epoch(myBasicMLP, video_fotograms_folder2, i)
myBasicMLP.train()
## Convert MSE to RMSE
tr_loss = np.sqrt(tr_loss)
val_loss = np.sqrt(val_loss)
if(create_video_training): #
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 ############
pf.create_image_training_epoch(X_data_tr, Y_data_tr,
X_data_val, Y_data_val, tr_loss,
val_loss, x_grid, y_grid, cf_a,
video_fotograms_folder, i)
pf.create_image_weights_epoch(myBasicMLP,
video_fotograms_folder2, i)
myBasicMLP.train()
## Convert MSE to RMSE
tr_loss = np.sqrt(tr_loss)
val_loss = np.sqrt(val_loss)
if (create_video_training): #
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):
legend=10,
xticks=10,
yticks=10)
image_name = "reconstrunction"
gl.savefig(folder_images + image_name, dpi=100, sizeInches=[20, 7])
"""
###################### SAVE MODEL ####################
"""
myVAE.save(folder_model + "model_parameters_epoch:%i.pk" % i)
"""
##################### STATIC PLOTS ######################
"""
if (create_video_training): #
pf.create_video_from_images(video_fotograms_folder_training,
output_file=folder_images +
"/training_weights.avi",
fps=2)
if (0):
gl.init_figure()
ax1 = gl.subplot2grid((2, 1), (0, 0), rowspan=1, colspan=1)
ax2 = gl.subplot2grid((2, 1), (1, 0), rowspan=1, colspan=1, sharex=ax1)
title = "Bar Chart. " + str(symbols[0]) + "(" + ul.period_dic[1440] + ")"
alpha = 0.9
cumulated_samples = 0
for i in range(5): # (len(days_keys)):
samples_index = day_dict[days_keys[i]]