diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..a963e3e2644733955a13fbc6e5c91495707db898
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+*.png
+*.dat
+Data_Generator/
diff --git a/LayerParallelLearning/examples/SSHE_Analytical/6/SSHE.cfg b/LayerParallelLearning/examples/SSHE_Analytical/6/SSHE.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..a53fbf77d692b094484f8e9769cc8a8b2d2ca3c7
--- /dev/null
+++ b/LayerParallelLearning/examples/SSHE_Analytical/6/SSHE.cfg
@@ -0,0 +1,130 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = examples/SSHE_Analytical
+# filename of training data feature vectors
+ftrain_ex = features_training.dat
+# filename of training data labels/classes
+ftrain_labels = output_training.dat
+# filename of validation data feature
+fval_ex = features_validation.dat
+# filename of validation data labels/classes
+fval_labels = output_validation.dat
+# number of training data elements (that many lines will be read!)
+ntraining = 5000
+# number of validation data elements (that many lines will be read!)
+nvalidation = 400
+# number of features within the training and validation data set
+nfeatures = 20
+# number of labels/classes within the training and validation data set
+nclasses = 16
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = NONE
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = NONE
+
+################################
+# Neural Network
+################################
+
+# number of channels
+nchannels = 12
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 8
+# final time
+T = 1.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu" or "LRELU" or "Linear")
+activation = ReLu
+# Type of network ("dense" the default, or "convolutional")
+network_type = dense
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+# XBraid
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 2
+# coarsening factor on all other levels
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 10
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations
+braid_maxiter = 2
+# absolute tolerance
+braid_abstol = 1e-15
+# absolute adjoint tolerance
+braid_adjtol = 1e-15
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 1
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+# Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+# deterministic:
+# fixes batch size => trains on this one
+#
+# stochastic: uses some (dead) pool
+# batch elements are randomly chosen in each iteration during training
+# smaller batch size makes sense
+batch_type = deterministic
+# Batch size
+nbatch = 400
+# relaxation param for tikhonov term
+gamma_tik = 1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 1e-5
+# relaxation param for tikhonov term of classification weights
+gamma_class = 1e-7
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting at initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize
+stepsize = 1.0
+# maximum number of optimization iterations
+optim_maxiter = 1000 #20000
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 15
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS #L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 10
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 1
diff --git a/LayerParallelLearning/examples/SSHE_Analytical/8/SSHE.cfg b/LayerParallelLearning/examples/SSHE_Analytical/8/SSHE.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..1f65b62e94f03fa7d91d2e3a3c64e0266fb18353
--- /dev/null
+++ b/LayerParallelLearning/examples/SSHE_Analytical/8/SSHE.cfg
@@ -0,0 +1,130 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = examples/SSHE_Analytical
+# filename of training data feature vectors
+ftrain_ex = features_training.dat
+# filename of training data labels/classes
+ftrain_labels = output_training.dat
+# filename of validation data feature
+fval_ex = features_validation.dat
+# filename of validation data labels/classes
+fval_labels = output_validation.dat
+# number of training data elements (that many lines will be read!)
+ntraining = 5000
+# number of validation data elements (that many lines will be read!)
+nvalidation = 400
+# number of features within the training and validation data set
+nfeatures = 28
+# number of labels/classes within the training and validation data set
+nclasses = 36
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = NONE
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = NONE
+
+################################
+# Neural Network
+################################
+
+# number of channels
+nchannels = 36
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 10
+# final time
+T = 1.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu" or "LRELU" or "Linear")
+activation = Linear
+# Type of network ("dense" the default, or "convolutional")
+network_type = dense
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+# XBraid
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 2
+# coarsening factor on all other levels
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 10
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations
+braid_maxiter = 2
+# absolute tolerance
+braid_abstol = 1e-15
+# absolute adjoint tolerance
+braid_adjtol = 1e-15
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 1
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+# Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+# deterministic:
+# fixes batch size => trains on this one
+#
+# stochastic: uses some (dead) pool
+# batch elements are randomly chosen in each iteration during training
+# smaller batch size makes sense
+batch_type = deterministic
+# Batch size
+nbatch = 400
+# relaxation param for tikhonov term
+gamma_tik = 1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 1e-5
+# relaxation param for tikhonov term of classification weights
+gamma_class = 1e-7
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting at initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize
+stepsize = 1.0 #1.0
+# maximum number of optimization iterations
+optim_maxiter = 3000 #20000
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 15
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS #L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 10
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 1
diff --git a/LayerParallelLearning/examples/SSHE_Analytical/Poisson/SSHE_num.cfg b/LayerParallelLearning/examples/SSHE_Analytical/Poisson/SSHE_num.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..d70fa016fa1cf4acb9491abffcb7a7a692c62341
--- /dev/null
+++ b/LayerParallelLearning/examples/SSHE_Analytical/Poisson/SSHE_num.cfg
@@ -0,0 +1,130 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = examples/SSHE_Analytical/Poisson
+# filename of training data feature vectors
+ftrain_ex = features_training.dat
+# filename of training data labels/classes
+ftrain_labels = output_training.dat
+# filename of validation data feature
+fval_ex = features_validation.dat
+# filename of validation data labels/classes
+fval_labels = output_validation.dat
+# number of training data elements (that many lines will be read!)
+ntraining = 5000
+# number of validation data elements (that many lines will be read!)
+nvalidation = 1000
+# number of features within the training and validation data set
+nfeatures = 64
+# number of labels/classes within the training and validation data set
+nclasses = 36
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = NONE
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = NONE
+
+################################
+# Neural Network
+################################
+
+# number of channels
+nchannels = 20
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 12
+# final time
+T = 10.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu" or "LRELU")
+activation = ReLu
+# Type of network ("dense" the default, or "convolutional")
+network_type = dense
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+# XBraid
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 2
+# coarsening factor on all other levels
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 10
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations
+braid_maxiter = 2
+# absolute tolerance
+braid_abstol = 1e-15
+# absolute adjoint tolerance
+braid_adjtol = 1e-15
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 1
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+# Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+# deterministic:
+# fixes batch size => trains on this one
+#
+# stochastic: uses some (dead) pool
+# batch elements are randomly chosen in each iteration during training
+# smaller batch size makes sense
+batch_type = stochastic
+# Batch size
+nbatch = 1000
+# relaxation param for tikhonov term
+gamma_tik = 1e-4
+# relaxation param for time-derivative term
+gamma_ddt = 1e-5
+# relaxation param for tikhonov term of classification weights
+gamma_class = 1e-5
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting at initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize
+stepsize = 1.0 #1.0
+# maximum number of optimization iterations
+optim_maxiter = 5000
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 15
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 10
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 1
diff --git a/LayerParallelLearning/examples/SSHE_Analytical/SSHE.cfg b/LayerParallelLearning/examples/SSHE_Analytical/SSHE.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..0804371402c56a0b2fdd25658ab039a0f45dec0e
--- /dev/null
+++ b/LayerParallelLearning/examples/SSHE_Analytical/SSHE.cfg
@@ -0,0 +1,130 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = examples/SSHE_Analytical
+# filename of training data feature vectors
+ftrain_ex = features_training.dat
+# filename of training data labels/classes
+ftrain_labels = output_training.dat
+# filename of validation data feature
+fval_ex = features_validation.dat
+# filename of validation data labels/classes
+fval_labels = output_validation.dat
+# number of training data elements (that many lines will be read!)
+ntraining = 5000
+# number of validation data elements (that many lines will be read!)
+nvalidation = 400
+# number of features within the training and validation data set
+nfeatures = 16
+# number of labels/classes within the training and validation data set
+nclasses = 9
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = NONE
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = NONE
+
+################################
+# Neural Network
+################################
+
+# number of channels
+nchannels = 16
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 20
+# final time
+T = 1.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu" or "LRELU" or "Linear")
+activation = tanh
+# Type of network ("dense" the default, or "convolutional")
+network_type = dense
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+# XBraid
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 2
+# coarsening factor on all other levels
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 10
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations
+braid_maxiter = 2
+# absolute tolerance
+braid_abstol = 1e-15
+# absolute adjoint tolerance
+braid_adjtol = 1e-15
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 1
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+# Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+# deterministic:
+# fixes batch size => trains on this one
+#
+# stochastic: uses some (dead) pool
+# batch elements are randomly chosen in each iteration during training
+# smaller batch size makes sense
+batch_type = deterministic
+# Batch size
+nbatch = 400
+# relaxation param for tikhonov term
+gamma_tik = 0# 1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 0# 1e-5
+# relaxation param for tikhonov term of classification weights
+gamma_class = 0 # 1e-7
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting at initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize
+stepsize = 1.0
+# maximum number of optimization iterations
+optim_maxiter = 500 #20000
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 15
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS #L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 10
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 1
diff --git a/LayerParallelLearning/examples/SSHE_numerical/15_CBC/SSHE_num.cfg b/LayerParallelLearning/examples/SSHE_numerical/15_CBC/SSHE_num.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..ed5b0a50a4a5a395d336d476a950e783fe5fd62a
--- /dev/null
+++ b/LayerParallelLearning/examples/SSHE_numerical/15_CBC/SSHE_num.cfg
@@ -0,0 +1,130 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = examples/SSHE_numerical/15_CBC
+# filename of training data feature vectors
+ftrain_ex = features_training.dat
+# filename of training data labels/classes
+ftrain_labels = output_training.dat
+# filename of validation data feature
+fval_ex = features_validation.dat
+# filename of validation data labels/classes
+fval_labels = output_validation.dat
+# number of training data elements (that many lines will be read!)
+ntraining = 5000
+# number of validation data elements (that many lines will be read!)
+nvalidation = 1000
+# number of features within the training and validation data set
+nfeatures = 56
+# number of labels/classes within the training and validation data set
+nclasses = 169
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = NONE
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = NONE
+
+################################
+# Neural Network
+################################
+
+# number of channels
+nchannels = 56
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 3
+# final time
+T = 10.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu" or "LRELU")
+activation = Linear
+# Type of network ("dense" the default, or "convolutional")
+network_type = dense
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+# XBraid
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 2
+# coarsening factor on all other levels
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 10
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations
+braid_maxiter = 2
+# absolute tolerance
+braid_abstol = 1e-15
+# absolute adjoint tolerance
+braid_adjtol = 1e-15
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 1
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+# Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+# deterministic:
+# fixes batch size => trains on this one
+#
+# stochastic: uses some (dead) pool
+# batch elements are randomly chosen in each iteration during training
+# smaller batch size makes sense
+batch_type = stochastic
+# Batch size
+nbatch = 500
+# relaxation param for tikhonov term
+gamma_tik = 0# 1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 0#1e-5
+# relaxation param for tikhonov term of classification weights
+gamma_class = 0#1e-7
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting at initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize
+stepsize = 1.0 #1.0
+# maximum number of optimization iterations
+optim_maxiter = 5000
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 15
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 10
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 1
diff --git a/LayerParallelLearning/examples/SSHE_numerical/7_CBC/SSHE_num.cfg b/LayerParallelLearning/examples/SSHE_numerical/7_CBC/SSHE_num.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..2928b744f40928c1c6a770c1563a5b1f67dcdc9e
--- /dev/null
+++ b/LayerParallelLearning/examples/SSHE_numerical/7_CBC/SSHE_num.cfg
@@ -0,0 +1,130 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = examples/SSHE_numerical/7_CBC
+# filename of training data feature vectors
+ftrain_ex = features_training.dat
+# filename of training data labels/classes
+ftrain_labels = output_training.dat
+# filename of validation data feature
+fval_ex = features_validation.dat
+# filename of validation data labels/classes
+fval_labels = output_validation.dat
+# number of training data elements (that many lines will be read!)
+ntraining = 8000
+# number of validation data elements (that many lines will be read!)
+nvalidation = 1600
+# number of features within the training and validation data set
+nfeatures = 24
+# number of labels/classes within the training and validation data set
+nclasses = 25
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = NONE
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = NONE
+
+################################
+# Neural Network
+################################
+
+# number of channels
+nchannels = 20
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 4
+# final time
+T = 1.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu" or "LRELU")
+activation = Linear
+# Type of network ("dense" the default, or "convolutional")
+network_type = dense
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+# XBraid
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 2
+# coarsening factor on all other levels
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 10
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations
+braid_maxiter = 2
+# absolute tolerance
+braid_abstol = 1e-15
+# absolute adjoint tolerance
+braid_adjtol = 1e-15
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 1
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+# Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+# deterministic:
+# fixes batch size => trains on this one
+#
+# stochastic: uses some (dead) pool
+# batch elements are randomly chosen in each iteration during training
+# smaller batch size makes sense
+batch_type = stochastic
+# Batch size
+nbatch = 800
+# relaxation param for tikhonov term
+gamma_tik = 0.0#1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 0.0#1e-5
+# relaxation param for tikhonov term of classification weights
+gamma_class = 0.0#1e-7
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting at initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize
+stepsize = 1.0
+# maximum number of optimization iterations
+optim_maxiter = 5000
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 15
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 10
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 1
diff --git a/LayerParallelLearning/examples/SSHE_numerical/8.1/SSHE_num.cfg b/LayerParallelLearning/examples/SSHE_numerical/8.1/SSHE_num.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..d347bd2efef9798d418e2e1093a5a923ddec74a2
--- /dev/null
+++ b/LayerParallelLearning/examples/SSHE_numerical/8.1/SSHE_num.cfg
@@ -0,0 +1,130 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = examples/SSHE_numerical/8.1
+# filename of training data feature vectors
+ftrain_ex = features_training.dat
+# filename of training data labels/classes
+ftrain_labels = output_training.dat
+# filename of validation data feature
+fval_ex = features_validation.dat
+# filename of validation data labels/classes
+fval_labels = output_validation.dat
+# number of training data elements (that many lines will be read!)
+ntraining = 5000
+# number of validation data elements (that many lines will be read!)
+nvalidation = 500
+# number of features within the training and validation data set
+nfeatures = 28
+# number of labels/classes within the training and validation data set
+nclasses = 36
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = NONE
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = NONE
+
+################################
+# Neural Network
+################################
+
+# number of channels
+nchannels = 28
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 4
+# final time
+T = 1.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu" or "LRELU")
+activation = Linear
+# Type of network ("dense" the default, or "convolutional")
+network_type = dense
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+# XBraid
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 2
+# coarsening factor on all other levels
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 10
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations
+braid_maxiter = 2
+# absolute tolerance
+braid_abstol = 1e-15
+# absolute adjoint tolerance
+braid_adjtol = 1e-15
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 1
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+# Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+# deterministic:
+# fixes batch size => trains on this one
+#
+# stochastic: uses some (dead) pool
+# batch elements are randomly chosen in each iteration during training
+# smaller batch size makes sense
+batch_type = stochastic
+# Batch size
+nbatch = 500
+# relaxation param for tikhonov term
+gamma_tik = 0.0 #1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 0.0 #1e-5
+# relaxation param for tikhonov term of classification weights
+gamma_class = 0.0 #1e-7
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting at initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize
+stepsize = 1.0
+# maximum number of optimization iterations
+optim_maxiter = 7500
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 15
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 10
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 1
diff --git a/LayerParallelLearning/examples/SSHE_numerical/8/SSHE_num.cfg b/LayerParallelLearning/examples/SSHE_numerical/8/SSHE_num.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..be3ec4cb399d928c12748fdb8ca3729965e4cf9b
--- /dev/null
+++ b/LayerParallelLearning/examples/SSHE_numerical/8/SSHE_num.cfg
@@ -0,0 +1,130 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = examples/SSHE_numerical/8
+# filename of training data feature vectors
+ftrain_ex = features_training.dat
+# filename of training data labels/classes
+ftrain_labels = output_training.dat
+# filename of validation data feature
+fval_ex = features_validation.dat
+# filename of validation data labels/classes
+fval_labels = output_validation.dat
+# number of training data elements (that many lines will be read!)
+ntraining = 2000
+# number of validation data elements (that many lines will be read!)
+nvalidation = 400
+# number of features within the training and validation data set
+nfeatures = 28
+# number of labels/classes within the training and validation data set
+nclasses = 36
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = NONE
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = NONE
+
+################################
+# Neural Network
+################################
+
+# number of channels
+nchannels = 36
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 8
+# final time
+T = 1.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu" or "LRELU")
+activation = Linear
+# Type of network ("dense" the default, or "convolutional")
+network_type = dense
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+# XBraid
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 2
+# coarsening factor on all other levels
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 10
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations
+braid_maxiter = 2
+# absolute tolerance
+braid_abstol = 1e-15
+# absolute adjoint tolerance
+braid_adjtol = 1e-15
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 1
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+# Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+# deterministic:
+# fixes batch size => trains on this one
+#
+# stochastic: uses some (dead) pool
+# batch elements are randomly chosen in each iteration during training
+# smaller batch size makes sense
+batch_type = deterministic
+# Batch size
+nbatch = 800
+# relaxation param for tikhonov term
+gamma_tik = 0.0#1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 0.0#1e-5
+# relaxation param for tikhonov term of classification weights
+gamma_class = 0.0#1e-7
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting at initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize
+stepsize = 1.0
+# maximum number of optimization iterations
+optim_maxiter = 500
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 15
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 10
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 1
diff --git a/LayerParallelLearning/examples/SSHE_numerical/8_CBC/SSHE_num.cfg b/LayerParallelLearning/examples/SSHE_numerical/8_CBC/SSHE_num.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..6f29bf664aef0abdeb60f6712daeefebb46de10b
--- /dev/null
+++ b/LayerParallelLearning/examples/SSHE_numerical/8_CBC/SSHE_num.cfg
@@ -0,0 +1,130 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = examples/SSHE_numerical/8_CBC
+# filename of training data feature vectors
+ftrain_ex = features_training.dat
+# filename of training data labels/classes
+ftrain_labels = output_training.dat
+# filename of validation data feature
+fval_ex = features_validation.dat
+# filename of validation data labels/classes
+fval_labels = output_validation.dat
+# number of training data elements (that many lines will be read!)
+ntraining = 8000
+# number of validation data elements (that many lines will be read!)
+nvalidation = 1600
+# number of features within the training and validation data set
+nfeatures = 28
+# number of labels/classes within the training and validation data set
+nclasses = 36
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = NONE
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = NONE
+
+################################
+# Neural Network
+################################
+
+# number of channels
+nchannels = 36
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 3
+# final time
+T = 10.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu" or "LRELU")
+activation = Linear
+# Type of network ("dense" the default, or "convolutional")
+network_type = dense
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+# XBraid
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 2
+# coarsening factor on all other levels
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 10
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations
+braid_maxiter = 2
+# absolute tolerance
+braid_abstol = 1e-15
+# absolute adjoint tolerance
+braid_adjtol = 1e-15
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 1
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+# Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+# deterministic:
+# fixes batch size => trains on this one
+#
+# stochastic: uses some (dead) pool
+# batch elements are randomly chosen in each iteration during training
+# smaller batch size makes sense
+batch_type = stochastic
+# Batch size
+nbatch = 400
+# relaxation param for tikhonov term
+gamma_tik = 0# 1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 0#1e-5
+# relaxation param for tikhonov term of classification weights
+gamma_class = 0#1e-7
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting at initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize
+stepsize = 1.0 #1.0
+# maximum number of optimization iterations
+optim_maxiter = 5000
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 15
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 10
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 1
diff --git a/LayerParallelLearning/examples/SSHE_numerical/SSHE_num.cfg b/LayerParallelLearning/examples/SSHE_numerical/SSHE_num.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..112c262b4c7951a78027446cea912d1cf1ccb1db
--- /dev/null
+++ b/LayerParallelLearning/examples/SSHE_numerical/SSHE_num.cfg
@@ -0,0 +1,130 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = examples/SSHE_numerical
+# filename of training data feature vectors
+ftrain_ex = features_training.dat
+# filename of training data labels/classes
+ftrain_labels = output_training.dat
+# filename of validation data feature
+fval_ex = features_validation.dat
+# filename of validation data labels/classes
+fval_labels = output_validation.dat
+# number of training data elements (that many lines will be read!)
+ntraining = 2000
+# number of validation data elements (that many lines will be read!)
+nvalidation = 400
+# number of features within the training and validation data set
+nfeatures = 24
+# number of labels/classes within the training and validation data set
+nclasses = 25
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = NONE
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = NONE
+
+################################
+# Neural Network
+################################
+
+# number of channels
+nchannels = 24
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 3
+# final time
+T = 1.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu" or "LRELU")
+activation = Linear
+# Type of network ("dense" the default, or "convolutional")
+network_type = dense
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+# XBraid
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 2
+# coarsening factor on all other levels
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 10
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations
+braid_maxiter = 2
+# absolute tolerance
+braid_abstol = 1e-15
+# absolute adjoint tolerance
+braid_adjtol = 1e-15
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 1
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+# Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+# deterministic:
+# fixes batch size => trains on this one
+#
+# stochastic: uses some (dead) pool
+# batch elements are randomly chosen in each iteration during training
+# smaller batch size makes sense
+batch_type = deterministic
+# Batch size
+nbatch = 400
+# relaxation param for tikhonov term
+gamma_tik = 0.0#1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 0.0#1e-5
+# relaxation param for tikhonov term of classification weights
+gamma_class = 0.0#1e-7
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting at initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize
+stepsize = 1.0
+# maximum number of optimization iterations
+optim_maxiter = 7500
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 15
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 10
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 1
diff --git a/LayerParallelLearning/examples/cfd1/cfd1.cfg b/LayerParallelLearning/examples/cfd1/cfd1.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..86162addd731507b7cf0ca15c6daabf40fac4544
--- /dev/null
+++ b/LayerParallelLearning/examples/cfd1/cfd1.cfg
@@ -0,0 +1,130 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = examples/cfd1
+# filename of training data feature vectors
+ftrain_ex = features_training.dat
+# filename of training data labels/classes
+ftrain_labels = output_training.dat
+# filename of validation data feature
+fval_ex = features_validation.dat
+# filename of validation data labels/classes
+fval_labels = output_validation.dat
+# number of training data elements (that many lines will be read!)
+ntraining = 1000
+# number of validation data elements (that many lines will be read!)
+nvalidation = 20
+# number of features within the training and validation data set
+nfeatures = 28
+# number of labels/classes within the training and validation data set
+nclasses = 36
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = NONE
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = NONE
+
+################################
+# Neural Network
+################################
+
+# number of channels
+nchannels = 36
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 32
+# final time
+T = 1.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu" or "LRELU")
+activation = ReLu
+# Type of network ("dense" the default, or "convolutional")
+network_type = dense
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+# XBraid
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 2
+# coarsening factor on all other levels
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 10
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations
+braid_maxiter = 2
+# absolute tolerance
+braid_abstol = 1e-15
+# absolute adjoint tolerance
+braid_adjtol = 1e-15
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 1
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+# Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+# deterministic:
+# fixes batch size => trains on this one
+#
+# stochastic: uses some (dead) pool
+# batch elements are randomly chosen in each iteration during training
+# smaller batch size makes sense
+batch_type = deterministic
+# Batch size
+nbatch = 1000
+# relaxation param for tikhonov term
+gamma_tik = 1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 1e-5
+# relaxation param for tikhonov term of classification weights
+gamma_class = 1e-7
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting at initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize
+stepsize = 1.0
+# maximum number of optimization iterations
+optim_maxiter = 1000
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 15
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 10
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 0
diff --git a/LayerParallelLearning/examples/function/function.cfg b/LayerParallelLearning/examples/function/function.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..9efeb75d9462ea4e6c74a62fd637846da9394fab
--- /dev/null
+++ b/LayerParallelLearning/examples/function/function.cfg
@@ -0,0 +1,130 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = examples/function
+# filename of training data feature vectors
+ftrain_ex = features_training.dat
+# filename of training data labels/classes
+ftrain_labels = output_training.dat
+# filename of validation data feature
+fval_ex = features_validation.dat
+# filename of validation data labels/classes
+fval_labels = output_validation.dat
+# number of training data elements (that many lines will be read!)
+ntraining = 1000
+# number of validation data elements (that many lines will be read!)
+nvalidation = 200
+# number of features within the training and validation data set
+nfeatures = 2
+# number of labels/classes within the training and validation data set
+nclasses = 1
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = NONE
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = NONE
+
+################################
+# Neural Network
+################################
+
+# number of channels
+nchannels = 8
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 32
+# final time
+T = 1.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu")
+activation = tanh
+# Type of network ("dense" the default, or "convolutional")
+network_type = dense
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+# XBraid
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 2
+# coarsening factor on all other levels
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 10
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations
+braid_maxiter = 2
+# absolute tolerance
+braid_abstol = 1e-15
+# absolute adjoint tolerance
+braid_adjtol = 1e-15
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 1
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+# Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+# deterministic:
+# fixes batch size => trains on this one
+#
+# stochastic: uses some (dead) pool
+# batch elements are randomly chosen in each iteration during training
+# smaller batch size makes sense
+batch_type = deterministic
+# Batch size
+nbatch = 1000
+# relaxation param for tikhonov term
+gamma_tik = 1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 1e-5
+# relaxation param for tikhonov term of classification weights
+gamma_class = 1e-7
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting at initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize
+stepsize = 1.0
+# maximum number of optimization iterations
+optim_maxiter = 300
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 15
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 10
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 0
diff --git a/LayerParallelLearning/examples/indianpines/indianpines.cfg b/LayerParallelLearning/examples/indianpines/indianpines.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..81c95af730976f1c90c0d78cca985c4e4ad66c4d
--- /dev/null
+++ b/LayerParallelLearning/examples/indianpines/indianpines.cfg
@@ -0,0 +1,124 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = examples/indianpines
+# filename of training data feature vectors
+ftrain_ex = features_training_shuffle.dat
+# filename of training data labels/classes
+ftrain_labels = labels_training_shuffle.dat
+# filename of validation data feature
+fval_ex = features_validation_shuffle.dat
+# filename of validation data labels/classes
+fval_labels = labels_validation_shuffle.dat
+# number of training data elements (that many lines will be read!)
+ntraining = 4000
+# number of validation data elements (that many lines will be read!)
+nvalidation = 2000
+# number of features in training and validation examples
+nfeatures = 220
+# number of labels/classes
+nclasses = 16
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = weights_open.dat
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = weights_classification.dat
+
+################################
+# Neural Network
+################################
+
+# number of channels
+nchannels = 220
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 29
+# final time
+T = 20.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu")
+activation = tanh
+# Type of network ("dense" the default, or "convolutional")
+network_type = dense
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+#BRAID
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 2
+# coarsening factor on all other levels
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 1
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations
+braid_maxiter = 10
+# absolute tolerance
+braid_abstol = 1e-10
+# absolute adjoint tolerance
+braid_adjtol = 1e-10
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 2
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+#Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+batch_type = deterministic
+# Batch size
+nbatch = 4000
+# relaxation param for tikhonov term
+gamma_tik = 1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 1e-7
+# relaxation param for tikhonov term of classification weights
+gamma_class = 1e-7
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting at initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize for fixed alpha and backtracking linesearch selection
+stepsize = 1.0
+# maximum number of optimization iterations
+optim_maxiter = 1
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 20
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 20
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 0
diff --git a/LayerParallelLearning/examples/mnist/mnist.cfg b/LayerParallelLearning/examples/mnist/mnist.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..ea2b5da4766dcebc2bd0a4953b292aa9bfdf3e64
--- /dev/null
+++ b/LayerParallelLearning/examples/mnist/mnist.cfg
@@ -0,0 +1,125 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = /Users/eccyr/Projects/ECRP/Code/xbraid-connets/mnist_converter
+# filename of training data feature vectors
+ftrain_ex = MNIST_Ytrain.dat
+# filename of training data labels/classes
+ftrain_labels = MNIST_Ctrain.dat
+# filename of validation data feature
+fval_ex = MNIST_Yval.dat
+# filename of validation data labels/classes
+fval_labels = MNIST_Cval.dat
+# number of training data elements (those will be read in! change actual batch size below!)
+ntraining = 10
+# number of validation data elements (those will be read in!)
+nvalidation = 10
+# number of features in training and validation examples
+nfeatures = 784
+# number of labels/classes
+nclasses = 10
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = NONE
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = NONE
+
+################################
+# Neural Network
+################################
+
+# number of channels for (convolutional networks this is nfeatures times the number of convolutions)
+# 6272 => 8 output channels, 4784 => 6, 3920 => 5, 3136 => 4
+nchannels = 3136
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 32
+# final time
+T = 5.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu")
+activation = tanh
+# Type of network ("dense" the default, or "convolutional")
+network_type = convolutional
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+#BRAID
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 4
+# coarsening factor (usually 4 is the best value here)
+braid_cfactor = 4
+# maximum number of levels
+braid_maxlevels = 15
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations (talk to Stefanie on good value here)
+braid_maxiter = 10
+# absolute tolerance (talk to Stefanie on good value here)
+braid_abstol = 1e-10
+# absolute adjoint tolerance (talk to Stefanie on good value here)
+braid_adjtol = 1e-10
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations (1 is a good value)
+braid_nrelax = 1
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+#Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+batch_type = deterministic
+# Batch size
+nbatch = 10
+# relaxation param for tikhonov term
+gamma_tik = 1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 1e-7
+# relaxation param for tikhonov term of classification weights
+gamma_class = 1e-7
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting with initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize for fixed alpha and backtracking linesearch selection
+stepsize = 1.0
+# maximum number of optimization iterations
+optim_maxiter = 24
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 20
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 20
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 0
diff --git a/LayerParallelLearning/examples/mnist/mnist_runs/config.cfg b/LayerParallelLearning/examples/mnist/mnist_runs/config.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..b6deaab33bc124a619ebe7bbfa5d3c3ac29afe32
--- /dev/null
+++ b/LayerParallelLearning/examples/mnist/mnist_runs/config.cfg
@@ -0,0 +1,101 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+#datafolder = /Users/jacobschroder/joint_repos/DNN_PinT/data
+#datafolder = /space/eccyr/Projects/ECRP/Code/convnet/mnist_converter
+datafolder = /Users/eccyr/Projects/ECRP/Code/xbraid-connets/mnist_converter
+# training examples filename
+ftrain_ex = MNIST_Ytrain.dat
+# training labels/classes filename
+ftrain_labels = MNIST_Ctrain.dat
+# validation examples filename
+fval_ex = MNIST_Yval.dat
+# validation labels/classes filename
+fval_labels = MNIST_Cval.dat
+# number of training data elements
+ntraining = 5000
+# number of validation data elements
+nvalidation = 200
+# number of features in training and validation examples
+nfeatures = 784
+# number of labels/classes
+nclasses = 10
+
+################################
+# Neural Network
+################################
+
+# number of channels for (convolutional networks this is nfeatures times the number of convolutions)
+# 6272 => 8 output channels, 4784 => 6, 3920 => 5, 3136 => 4
+nchannels = 3136
+# number of layers (minimum two, opening layer and classification layer)
+nlayers = 20
+# final time
+T = 5.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu")
+activation = tanh
+# Type of network ("dense" the default, or "convolutional")
+network_type = convolutional
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+#BRAID
+################################
+
+# coarsening factor
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 1
+# maximum number of iterations
+braid_maxiter = 10
+# absolute tolerance
+braid_abstol = 1e-10
+# absolute adjoint tolerance
+braid_adjtol = 1e-10
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 2
+
+####################################
+#Optimization
+####################################
+
+# relaxation param for tikhonov term
+gamma_tik = 1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 1e-7
+# relaxation param for tikhonov term of classification weights
+gamma_class = 1e-7
+# initial stepsize
+stepsize = 1.0
+# maximum number of optimization iterations
+optim_maxiter = 24
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 20
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 20
+
diff --git a/LayerParallelLearning/examples/mnist/mnist_runs/submit_paramstudy.py b/LayerParallelLearning/examples/mnist/mnist_runs/submit_paramstudy.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb4029aea0aa45337511f6a96d095c9d78ea6c39
--- /dev/null
+++ b/LayerParallelLearning/examples/mnist/mnist_runs/submit_paramstudy.py
@@ -0,0 +1,65 @@
+#!/usr/bin/env python
+
+import sys
+import os
+import copy
+
+from batch_job import submit_job
+from config import *
+from util import *
+
+# Specify runcommand for the cluster ("srun -n" on quartz or "mpirun -np" on elwe)
+#runcommand = "srun -n"
+runcommand = "mpirun -n"
+
+# Specify name of training data folder
+datafolder = "data"
+
+# Specify the global config file
+configfile = Config("config.cfg")
+
+# Specify the varying parameters
+gammatik = [1e-1,1e-3,1e-5]
+gammaddt = [1e-1,1e-3,1e-5]
+gammaclass = [1e-1,1e-3,1e-5]
+npt = 2 # number of processors
+
+# Submit a job for each parameter setup
+for itik in range(len(gammatik)):
+
+ for iddt in range(len(gammaddt)):
+
+ for iclass in range(len(gammaclass)):
+
+ # Copy the global config file
+ konfig = copy.deepcopy(configfile)
+
+ # Change the config entry
+ konfig.gamma_tik = gammatik[itik]
+ konfig.gamma_ddt = gammaddt[iddt]
+ konfig.gamma_class = gammaclass[iclass]
+
+ # Specify jobname
+ jobname = \
+ "mnist" +\
+ "-tik" + str(konfig.gamma_tik) +\
+ "-ddt" + str(konfig.gamma_ddt) +\
+ "-class" + str(konfig.gamma_class)
+
+ # create folder for the job
+ if os.path.exists(jobname):
+ pass
+ else:
+ os.mkdir(jobname)
+
+ # create a link to training and validation data
+ make_link("/Users/eccyr/Projects/ECRP/Code/xbraid-connets/mnist_converter",jobname + "/" + datafolder)
+
+ # Create a config file
+ newconfigfile = jobname + ".cfg"
+ konfig.dump(jobname + "/" + newconfigfile)
+
+ # submit the job
+ os.chdir(jobname)
+ submit_job(jobname, runcommand, npt, "10:00:00", "../../main", newconfigfile,False)
+ os.chdir("../")
diff --git a/LayerParallelLearning/examples/peaks/peaks.cfg b/LayerParallelLearning/examples/peaks/peaks.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..6ab26de1b566fe4b0b129f1890bfd5122a453c52
--- /dev/null
+++ b/LayerParallelLearning/examples/peaks/peaks.cfg
@@ -0,0 +1,130 @@
+################################
+# Data set
+################################
+
+# relative data folder location
+datafolder = examples/peaks
+# filename of training data feature vectors
+ftrain_ex = features_training.dat
+# filename of training data labels/classes
+ftrain_labels = labels_training.dat
+# filename of validation data feature
+fval_ex = features_validation.dat
+# filename of validation data labels/classes
+fval_labels = labels_validation.dat
+# number of training data elements (that many lines will be read!)
+ntraining = 5000
+# number of validation data elements (that many lines will be read!)
+nvalidation = 200
+# number of features within the training and validation data set
+nfeatures = 2
+# number of labels/classes within the training and validation data set
+nclasses = 5
+
+# filename for opening weights and bias (set to NONE if not given)
+weightsopenfile = NONE
+# filename for classification weights and bias (set to NONE if not given)
+weightsclassificationfile = NONE
+
+################################
+# Neural Network
+################################
+
+# number of channels
+nchannels = 8
+# number of layers (including opening layer and classification layer) (nlayer >= 3 !)
+nlayers = 32
+# final time
+T = 1.0
+# Activation function ("tanh" or "ReLu" or "SmoothReLu")
+activation = SmoothReLu
+# Type of network ("dense" the default, or "convolutional")
+network_type = dense
+# Opening layer type.
+# "replicate": replicate image for each convolution.
+# "activate": same as replicate, only apply tuned, shifted tanh activation function for MNIST.
+type_openlayer = activate
+# factor for scaling initial opening layer weights and bias
+weights_open_init = 1e-3
+# factor for scaling initial weights and bias of intermediate layers
+weights_init = 1e-3
+# factor for scaling initial classification weights and bias
+weights_class_init = 1e-3
+
+################################
+# XBraid
+################################
+
+# coarsening factor on level 0
+# generally, cfactor0 = nlayers / P_t
+# where P_t is the processors in time, and nlayers is the number of time-steps
+braid_cfactor0 = 2
+# coarsening factor on all other levels
+braid_cfactor = 2
+# maximum number of levels
+braid_maxlevels = 10
+# minimum allowed coarse time time grid size (values in 10-30 are usually best)
+braid_mincoarse = 10
+# maximum number of iterations
+braid_maxiter = 2
+# absolute tolerance
+braid_abstol = 1e-15
+# absolute adjoint tolerance
+braid_adjtol = 1e-15
+# printlevel
+braid_printlevel = 1
+# access level
+braid_accesslevel = 0
+# skip work on downcycle?
+braid_setskip = 0
+# V-cycle (0) or full multigrid (1)
+braid_fmg = 0
+# Number of CF relaxations
+braid_nrelax = 1
+# Number of CF relaxations on level 0 (1 or 0 are usually the best values)
+braid_nrelax0 = 0
+
+####################################
+# Optimization
+####################################
+# Type of batch selection ("deterministic" or "stochastic")
+# deterministic:
+# fixes batch size => trains on this one
+#
+# stochastic: uses some (dead) pool
+# batch elements are randomly chosen in each iteration during training
+# smaller batch size makes sense
+batch_type = deterministic
+# Batch size
+nbatch = 5000
+# relaxation param for tikhonov term
+gamma_tik = 1e-7
+# relaxation param for time-derivative term
+gamma_ddt = 1e-5
+# relaxation param for tikhonov term of classification weights
+gamma_class = 1e-7
+# stepsize selection type ("fixed" or "backtrackingLS" or "oneoverk")
+# determines how to choose alpha in design update x_new = x_old - alpha * direction
+# fixed : constant alpha being the initial stepsize
+# backtrackingLS : find alpha from backtracking linesearch, starting at initial stepsize
+# oneoverk : alpha = 1/k where k is the current optimization iteration index
+stepsize_type = backtrackingLS
+# initial stepsize
+stepsize = 1.0
+# maximum number of optimization iterations
+optim_maxiter = 130
+# absolute stopping criterion for the gradient norm
+gtol = 1e-4
+# maximum number of linesearch iterations
+ls_maxiter = 15
+# factor for modifying the stepsize within a linesearch iteration
+ls_factor = 0.5
+# Hessian Approximation ("BFGS", "L-BFGS" or "Identity")
+hessian_approx = L-BFGS
+# number of stages for l-bfgs method
+lbfgs_stages = 10
+# level for validation computation:
+# -1 = never validate
+# 0 = validate only after optimization finishes.
+# 1 = validate in each optimization iteration
+validationlevel = 1
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diff --git a/LayerParallelLearning/plotting_routines/features_validation.dat b/LayerParallelLearning/plotting_routines/features_validation.dat
index 62da219cf41ff9dbea38b77f36ee4405c096a49e..ab5328089156c796acfa01d52ae9a451e41e5487 100644
--- a/LayerParallelLearning/plotting_routines/features_validation.dat
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diff --git a/LayerParallelLearning/plotting_routines/main.py b/LayerParallelLearning/plotting_routines/main.py
index 04be2c0af9a5b64075573ba0bfe11234a834b414..b2390456d7765a951fe16be34984d5a3aa9b4865 100644
--- a/LayerParallelLearning/plotting_routines/main.py
+++ b/LayerParallelLearning/plotting_routines/main.py
@@ -1,9 +1,7 @@
import csv
-
import numpy as np
import matplotlib.pyplot as plt
from os import path
-outpath = "/home/multigrid/Master Thesis/LayerParallelLearning/plotting_routines/SSHE"
class Plotting:
def __init__(self, len_x, len_y, N_Val_Samples, x_s, x_e, y_s, y_e):
@@ -57,10 +55,11 @@ class Plotting:
self.ResNet_Prediction[::, 1:self.lenY - 1:1, 1:self.lenX - 1:1] = np.reshape([x.strip().split(' ') for x in f2],
(self.v_size, self.rows_in, self.cols_in))
with open('features_validation.dat', 'r') as f3:
- self.Boundary = np.reshape([x.strip().split(' ') for x in f3], (self.v_size, self.NGridPts_Bdry))
+ bdry_plus_f = np.reshape([x.strip().split(' ') for x in f3], (self.v_size, (self.NGridPts_Bdry + self.NGridPts_int)))
+ self.Boundary = bdry_plus_f[:, 0:self.NGridPts_Bdry:1]
- with open('loss_history.dat', 'r') as f5:
- self.loss = np.loadtxt('loss_history.dat')
+ with open('loss_history_pe.dat', 'r') as f5:
+ self.loss = np.loadtxt('loss_history_pe.dat')
self.iter = self.loss[:, 0]
self.training_loss = self.loss[:, 1]
self.val_loss = self.loss[:, 2]
@@ -68,15 +67,14 @@ class Plotting:
self.MAPE_val = self.loss[:, 4]
- def plot_and_save_figures(self):
- Nplots = 7
- colorinterpolation = 50
+ def plot_and_save_figures(self, Nplots):
+ # Number of figs to be generated! Entire validation set is too much!
+ colorinterpolation = 100
colourMap = plt.cm.jet
for v in range(Nplots):
-
fig = plt.figure()
- fig.suptitle('Temperature Contour', fontsize= 14)
- plt.title("Numerical Solution Data- " + str(v))
+ fig.suptitle('Comparison of temperature contours', fontsize= 14)
+ plt.title("Ground truth solution- " + str(v))
plt.subplot(1, 2, 1)
plt.title("Numerical Solution Data- " + str(v))
plt.contourf(self.X, self.Y, self.Numerical_Sol[v], colorinterpolation, cmap=colourMap)
@@ -91,33 +89,29 @@ class Plotting:
cb2.remove()
def plot_loss(self):
- plt.plot(self.training_loss, label='train loss')
- plt.plot(self.val_loss, label='val loss')
- # plt.yscale('log')
- plt.legend()
- plt.savefig('losses.png')
+ fig, ax = plt.subplots() # Create a figure and an axes.
+ ax.plot(self.iter, self.training_loss, label='training')
+ ax.plot(self.iter, self.val_loss, label='validation')
+ plt.yscale('log')
+ ax.set_xlabel('Epochs') # Add an x-label to the axes.
+ ax.set_ylabel('Mean squared error') # Add a y-label to the axes.
+ ax.set_title("Loss history") # Add a title to the axes.
+ ax.legend() # Add a legend.
+ plt.savefig(path.join(outpath, "losses.png"))
plt.show()
- def plot_MAPE(self):
- plt.plot(self.MAPE_train, label='Mean Absolute Percentage Error: Training')
- plt.plot(self.MAPE_val, label='Mean Absolute Percentage Error: Validation')
- # plt.yscale('log')
- plt.legend()
- plt.savefig('MAPE.png')
- plt.show()
- '''
- fig = plt.figure()
- plt.title("Loss Vs number of optimization iterations")
- plt.subplot(1, 2, 1)
- plt.title("Training loss")
- plt.plot(self.loss[::][0], self.loss[::][1] , 'o-', color='g')
- plt.subplot(1, 2, 2)
- plt.title("Validation Loss")
- plt.plot(self.loss[::][0], self.loss[::][2] , 'o-', color='b')
+ def plot_MAPE(self):
+ fig, ax = plt.subplots() # Create a figure and an axes.
+ ax.plot(self.iter, self.MAPE_train, label='training')
+ ax.plot(self.iter, self.MAPE_val, label='validation')
+ plt.yscale('log')
+ ax.set_xlabel('Epochs') # Add an x-label to the axes.
+ ax.set_ylabel('Mean Absolute Percentage Error (MAPE)') # Add a y-label to the axes.
+ ax.set_title("Loss history") # Add a title to the axes.
+ ax.legend() # Add a legend.
+ plt.savefig(path.join(outpath, "MAPE.png"))
plt.show()
- fig.savefig(path.join(outpath, "loss_history.png"))
- '''
@@ -126,16 +120,35 @@ x_s = -1.
x_e = 1.
y_s = -1.
y_e = 1.
-len_x = 5
-len_y = 5
-N_Val_Samples = 400
-# N_Iterations =
+len_x = 8
+len_y = 8
+N_Val_Samples = 1000
+
+#Number of plots to be made and saved out of total validation samples
+Nplots = 10
+
+#Location where all the images will be stored
+outpath = "/home/multigrid/Master Thesis/LayerParallelLearning/plotting_routines/Poisson"
+
P = Plotting(len_x, len_y, N_Val_Samples, x_s, x_e, y_s, y_e)
P.initialize()
P.create_grid()
P.read_data()
P.set_soln_boundary()
-P.plot_and_save_figures()
+P.plot_and_save_figures(Nplots)
P.plot_loss()
P.plot_MAPE()
+
+'''
+fig = plt.figure()
+plt.title("Loss Vs number of optimization iterations")
+plt.subplot(1, 2, 1)
+plt.title("Training loss")
+plt.plot(self.loss[::][0], self.loss[::][1] , 'o-', color='g')
+plt.subplot(1, 2, 2)
+plt.title("Validation Loss")
+plt.plot(self.loss[::][0], self.loss[::][2] , 'o-', color='b')
+plt.show()
+fig.savefig(path.join(outpath, "loss_history.png"))
+'''
\ No newline at end of file
diff --git a/LayerParallelLearning/plotting_routines/output_validation.dat b/LayerParallelLearning/plotting_routines/output_validation.dat
index fb972205c43d84973b3ccac0bece17ff1682a20f..776a1e30669c8b1a9b1980573614a5e21472385a 100644
--- a/LayerParallelLearning/plotting_routines/output_validation.dat
+++ b/LayerParallelLearning/plotting_routines/output_validation.dat
@@ -1,400 +1,1000 @@
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