state.nepochs = [128]
# Different validation runs
# - 100 training examples (x20 different samples of 100 training examples)
# - 1000 training examples (x10 different samples of 1000 training examples)
# - 10000 training examples (x1 different sample of 10000 training examples)
# (because of jobman, the keys have to be strings, not ints)
# NOTE: Probably you don't want to make trainsize larger than 10K,
# because it will be too large for CPU memory.
state.validation_runs_for_each_trainingsize = {"100": 20, "1000": 10, "10000": 1}
# For each layer, a list of the epochs at which you evaluate the
# reconstruction error and linear-SVM-supervised error.
# All the different results you have from here will be stored in a
# separate file per layer.
state.epochstest = [[0,2,4,8,12,16,24,32,48,64,96,128]]
#epochstest = [[0,5,30],[0,5,30],[0,2,4,8,16,30]]
state.BATCH_TEST = 100
state.BATCH_CREATION_LIBSVM = 500
state.NB_MAX_TRAINING_EXAMPLES_SVM = 10000
#NB_MAX_TRAINING_EXAMPLES_SVM = 1000 # FIXME: Change back to 10000 <========================================================================
# 1000 is just for fast running during development
#NB_MAX_TRAINING_EXAMPLES_SVM = 100 # FIXME: Change back to 10000 <========================================================================
# # 100 is just for superfast running during development
state.SVM_INITIALC = 0.001
state.SVM_STEPFACTOR = 10.
state.SVM_MAXSTEPS = 10
#hardcoded path to your liblinear source:
# - 1000 training examples (x10 different samples of 1000 training examples)
# - 10000 training examples (x1 different sample of 10000 training examples)
# (because of jobman, the keys have to be strings, not ints)
# NOTE: Probably you don't want to make trainsize larger than 10K,
# because it will be too large for CPU memory.
state.validation_runs_for_each_trainingsize = {
"100": 20,
"1000": 10,
"10000": 1
}
# For each layer, a list of the epochs at which you evaluate the
# reconstruction error and linear-SVM-supervised error.
# All the different results you have from here will be stored in a
# separate file per layer.
state.epochstest = [[0, 1, 2, 3, 4, 6, 8, 11, 15, 30]]
#epochstest = [[0,5,30],[0,5,30],[0,2,4,8,16,30]]
state.BATCH_TEST = 100
state.BATCH_CREATION_LIBSVM = 500
state.NB_MAX_TRAINING_EXAMPLES_SVM = 10000
#NB_MAX_TRAINING_EXAMPLES_SVM = 1000 # FIXME: Change back to 10000 <========================================================================
# 1000 is just for fast running during development
#NB_MAX_TRAINING_EXAMPLES_SVM = 100 # FIXME: Change back to 10000 <========================================================================
# # 100 is just for superfast running during development
state.SVM_INITIALC = 0.001
state.SVM_STEPFACTOR = 10.
state.SVM_MAXSTEPS = 10
#hardcoded path to your liblinear source:
state.nepochs = [30]
# Different validation runs
# - 100 training examples (x20 different samples of 100 training examples)
# - 1000 training examples (x10 different samples of 1000 training examples)
# - 10000 training examples (x1 different sample of 10000 training examples)
# (because of jobman, the keys have to be strings, not ints)
# NOTE: Probably you don't want to make trainsize larger than 10K,
# because it will be too large for CPU memory.
state.validation_runs_for_each_trainingsize = {"100": 20, "1000": 10, "10000": 1}
# For each layer, a list of the epochs at which you evaluate the
# reconstruction error and linear-SVM-supervised error.
# All the different results you have from here will be stored in a
# separate file per layer.
state.epochstest = [[0,1,2,3,4,6,8,11,15,30]]
#epochstest = [[0,5,30],[0,5,30],[0,2,4,8,16,30]]
state.BATCH_TEST = 100
state.BATCH_CREATION_LIBSVM = 500
state.NB_MAX_TRAINING_EXAMPLES_SVM = 10000
#NB_MAX_TRAINING_EXAMPLES_SVM = 1000 # FIXME: Change back to 10000 <========================================================================
# 1000 is just for fast running during development
#NB_MAX_TRAINING_EXAMPLES_SVM = 100 # FIXME: Change back to 10000 <========================================================================
# # 100 is just for superfast running during development
state.SVM_INITIALC = 0.001
state.SVM_STEPFACTOR = 10.
state.SVM_MAXSTEPS = 10
#hardcoded path to your liblinear source:
