tf_regloss += muscat.tf_lambda_reg * reg.Reg_GR(
muscat.TF_obj, muscat.tf_eps, muscat.tf_eps
) #, eps1=muscat.tf_eps, eps2=muscat.tf_eps) #Alernatively tf_total_variation_regularization # total_variation
if is_absorption:
tf_regloss += muscat.tf_lambda_reg * reg.Reg_GR(
muscat.TF_obj_absorption, muscat.tf_eps, muscat.tf_eps)
elif (experiments.regularizer == 'L1'):
# L1 rgularizer
print('We are using L1 - Regularization')
tf_regloss += muscat.tf_lambda_reg * (
reg.Reg_L1(muscat.TF_obj) + reg.Reg_L1(muscat.TF_obj_absorption))
elif (experiments.regularizer == 'L2'):
# L1 rgularizer
print('We are using L2 - Regularization')
tf_regloss = muscat.tf_lambda_reg * (
reg.Reg_L2(muscat.TF_obj) + reg.Reg_L2(muscat.TF_obj_absorption))
tf_zernloss = experiments.lambda_zernike * reg.Reg_L2(
muscat.TF_zernikefactors
) # general loss on the zernike modes - don't over estimate them by value!
tf_icshiftloss = experiments.lambda_icshift * reg.Reg_L2(
tf.cast(muscat.TF_shiftIcX + muscat.TF_shiftIcY, tf.float32))
# fuse all loss functions
tf_regloss += tf_zernloss
tf_regloss += tf_icshiftloss
'''Negativity Constraint'''
if (0):
#tf_negsqrloss = reg.Reg_NegSqr(tf_helper.extract(tf.cast(muscat.TF_obj, tf.float32), muscat.mysize))#-tf.minimum(tf.reduce_min(muscat.TF_obj-1.),0)
tf_negsqrloss = reg.Reg_NegSqr(
muscat.TF_obj - myparams.nEmbb -
elif (experiments.regularizer == 'GR'):
# Goods roughness rgularizer
print('We are using GR - Regularization')
tf_regloss += muscat.tf_lambda_reg * (
reg.Reg_GR(muscat.TF_obj) +
reg.Reg_GR(muscat.TF_obj_absorption)
) #, eps1=muscat.tf_eps, eps2=muscat.tf_eps) #Alernatively tf_total_variation_regularization # total_variation
elif (experiments.regularizer == 'L1'):
# L1 rgularizer
print('We are using L1 - Regularization')
tf_regloss += muscat.tf_lambda_reg * (reg.Reg_L1(
muscat.TF_obj) + reg.Reg_L1(muscat.TF_obj_absorption))
elif (experiments.regularizer == 'L2'):
# L1 rgularizer
print('We are using L2 - Regularization')
tf_regloss = muscat.tf_lambda_reg * (reg.Reg_L2(
muscat.TF_obj) + reg.Reg_L2(muscat.TF_obj_absorption))
if (0):
tf_zernloss = experiments.lambda_zernike * reg.Reg_L2(
muscat.TF_zernikefactors
) # general loss on the zernike modes - don't over estimate them by value!
tf_icshiftloss = experiments.lambda_icshift * reg.Reg_L2(
muscat.TF_shiftIcX + muscat.TF_shiftIcY)
# fuse all loss functions
tf_regloss += tf_zernloss
tf_regloss += tf_icshiftloss
'''Define Optimizer'''
'''Negativity Constraint'''
#tf_negsqrloss = reg.Reg_NegSqr(tf_helper.extract(tf.cast(muscat.TF_obj, tf.float32), muscat.mysize))#-tf.minimum(tf.reduce_min(muscat.TF_obj-1.),0)
tf_negsqrloss = reg.Reg_NegSqr(
elif (experiments.regularizer == 'GR'):
# Goods roughness rgularizer
print('We are using GR - Regularization')
tf_regloss += muscat.tf_lambda_tv * (
reg.Reg_GR(muscat.TF_obj) + reg.Reg_GR(muscat.TF_obj_absorption)
) #, eps1=muscat.tf_eps, eps2=muscat.tf_eps) #Alernatively tf_total_variation_regularization # total_variation
elif (experiments.regularizer == 'L1'):
# L1 rgularizer
print('We are using GR - Regularization')
tf_regloss += muscat.tf_lambda_tv * (
reg.Reg_L1(muscat.TF_obj) + reg.Reg_L1(muscat.TF_obj_absorption))
elif (experiments.regularizer == 'L2'):
# L1 rgularizer
print('We are using GR - Regularization')
tf_regloss = muscat.tf_lambda_tv * (
reg.Reg_L2(muscat.TF_obj) + reg.Reg_L2(muscat.TF_obj_absorption))
tf_zernloss = experiments.lambda_zernike * reg.Reg_L2(
muscat.TF_zernikefactors + 10 *
(muscat.TF_shiftIcX + muscat.TF_shiftIcY)
) # general loss on the zernike modes - don't over estimate them by value!
tf_regloss += tf_zernloss
'''Define Optimizer'''
'''Negativity Constraint'''
#tf_negsqrloss = reg.Reg_NegSqr(tf_helper.extract(tf.cast(muscat.TF_obj, tf.float32), muscat.mysize))#-tf.minimum(tf.reduce_min(muscat.TF_obj-1.),0)
tf_negsqrloss = reg.Reg_NegSqr(
muscat.TF_obj) #-tf.minimum(tf.reduce_min(muscat.TF_obj-1.),0)
tf_negsqrloss += reg.Reg_NegSqr(muscat.TF_obj_absorption)
tf_negsqrloss *= experiments.lambda_neg
# Correc the fwd model - not good here!
tf_glob_real = tf.Variable(0., 'tf_glob_real')
tf_glob_imag = tf.Variable(0., 'tf_glob_imag')
