# Specification of model type and training parameters
model_type = 'mrd'
model_num_inducing = 40
model_num_iterations = 200
model_init_iterations = 1100
#fname = 'm_' + model_type + '_exp' + str(experiment_number) #+ '.pickle'
fname = modelPath + '/models/' + 'm_' + model_type + '_exp' + str(experiment_number) #+ '.pickle'
# Enable to save the model and visualise GP nearest neighbour matching
save_model=True
visualise_output=True
# Reading face data, preparation of data and training of the model
mySAMpy.readData(root_data_dir, participant_index, pose_index)
mySAMpy.prepareFaceData(model_type, Ntr, pose_selection)
mySAMpy.training(model_num_inducing, model_num_iterations, model_init_iterations, fname, save_model)
# This is for visualising the mapping of the test face back to the internal memory
if visualise_output:
ax = mySAMpy.SAMObject.visualise()
visualiseInfo=dict()
visualiseInfo['ax']=ax
ytmp = mySAMpy.SAMObject.recall(0)
ytmp = numpy.reshape(ytmp,(mySAMpy.imgHeightNew,mySAMpy.imgWidthNew))
fig_nn = pb.figure()
pb.title('Training NN')
pl_nn = fig_nn.add_subplot(111)
ax_nn = pl_nn.imshow(ytmp, cmap=plt.cm.Greys_r)
model_num_iterations = 200
model_init_iterations = 1200
#fname = 'm_' + model_type + '_exp' + str(experiment_number) #+ '.pickle'
fname = modelPath + '/models/mFaces_' + model_type + '_exp' + str(
experiment_number) #+ '.pickle'
# Enable to save the model and visualise GP nearest neighbour matching
save_model = True
economy_save = True # ATTENTION!! This is still BETA!!
visualise_output = True
##################################### SET-UP and TRAIN #############################################
#####################################################################################################
# Reading face data, preparation of data and training of the model
mySAMpy.readData(root_data_dir, participant_index, pose_index)
mySAMpy.prepareFaceData(model_type,
Ntr,
pose_selection,
randSeed=experiment_number)
mySAMpy.training(model_num_inducing, model_num_iterations,
model_init_iterations, fname, save_model, economy_save)
##################################### VISUALISATION #############################################
#####################################################################################################
# This is for visualising the mapping of the test face back to the internal memory
if visualise_output:
ax = mySAMpy.SAMObject.visualise()
visualiseInfo = dict()
visualiseInfo['ax'] = ax
