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(
muscat.TF_obj - myparams.nEmbb -
myparams.dn) #-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.constant(0., name='tf_glob_real')
tf_glob_imag = tf.constant(0., name='tf_glob_imag')
tf_norm = tf.complex(tf_glob_real, tf_glob_imag)
'''Define Loss-function'''
if (0):
print('-------> Losstype is L1')
tf_fidelity = tf.reduce_mean(
(tf.abs((muscat.tf_meas) - tf_fwd))
) # allow a global phase parameter to avoid unwrapping effects
elif (0):
# Total Variation
print('We are using TV - Regularization')
tf_tvloss = muscat.tf_lambda_tv * reg.Reg_TV(
muscat.TF_obj,
BetaVals=[muscat.dx, muscat.dy, muscat.dz],
epsR=muscat.tf_eps,
is_circ=True
) #Alernatively tf_total_variation_regularization # total_variation
tf_tvloss += muscat.tf_lambda_tv * reg.Reg_TV(
muscat.TF_obj_absorption,
BetaVals=[muscat.dx, muscat.dy, muscat.dz],
epsR=muscat.tf_eps,
is_circ=True
) #Alernatively tf_total_variation_regularization # total_variation
'''Negativity Constraint'''
tf_negsqrloss = lambda_neg * reg.Reg_NegSqr(muscat.TF_obj)
tf_negsqrloss += lambda_neg * reg.Reg_NegSqr(muscat.TF_obj_absorption)
# Correc the fwd model - not good here!
tf_norm = tf.complex(tf_global_phase, tf_global_abs)
tf_fwd_corrected = tf_fwd + tf_norm
#tf_fwd_corrected = (tf_fwd+1j*tf.cast(tf_global_phase, tf.complex64))/tf.cast(tf_global_abs, tf.complex64)
'''Define Loss-function'''
if (0):
print('-------> ATTENTION Losstype is L1')
tf_fidelity = tf.reduce_mean(
(tf.abs(muscat.tf_meas - tf_fwd_corrected)
)) # allow a global phase parameter to avoid unwrapping effects
else:
print('-------> ATTENTION: Losstype is L2')
tf_fidelity = tf.reduce_mean(
