def main():
# --- CHANGE THESE
NAME = None
C_VECTOR = None
DIMENSION = None
RADIUS = None
RANDOM_SEED = None
NUM_EPOCHS = None
FREQUENCY = None
LAYER_SIZES = None
LOCAL_METHODS = [FastLip, LipLP, LipMIP]
GLOBAL_METHODS = OTHER_METHODS + [LipMIP]
NUM_RANDOM = None # Number of points in exp.do_random_evals
NUM_DATA = None # Number of points in exp.do_data_evals
assert all(_ is not None for _ in [NAME, C_VECTOR, DIMENSION, RADIUS,
RANDOM_SEED, NUM_EPOCHS, FREQUENCY,
LAYER_SIZES])
# --- THESE CAN REMAIN AS IS
exp_kwargs = {'c_vector': C_VECTOR, 'primal_norm': 'linf'}
GLOBAL_LO = np.zeros(DIMENSION)
GLOBAL_HI = np.ones(DIMENSION)
DOMAIN = Hyperbox.build_unit_hypercube(DIMENSION)
BALL_FACTORY = Factory(Hyperbox.build_linf_ball, radius=RADIUS)
# ================================================================
# = Data Parameters Setup =
# ================================================================
# Make both the training/validation sets
data_params = dl.RandomKParameters(num_points=300, k=10, radius=0.01,
dimension=DIMENSION)
dataset = dl.RandomDataset(data_params, batch_size=128,
random_seed=RANDOM_SEED)
train_set, _ = dataset.split_train_val(1.0)
# Make the data arg_bundle object
loader_kwargs = {'batch_size': NUM_DATA, 'random_seed': RANDOM_SEED}
data_arg_bundle = {'data_type': 'synthetic',
'params': data_params,
'loader_kwargs': loader_kwargs,
'ball_factory': BALL_FACTORY}
# ================================================================
# = Training Parameter Setup =
# ================================================================
# Build the loss functional and set the optimizer
xentropy = train.XEntropyReg()
l2_penalty = train.LpWeightReg(scalar=1e-2, lp='l2')
loss_functional = train.LossFunctional(regularizers=[xentropy, l2_penalty])
train_params = train.TrainParameters(train_set, train_set, NUM_EPOCHS,
loss_functional=loss_functional,
test_after_epoch=20)
# Build the base network architecture
network = ReLUNet(layer_sizes=LAYER_SIZES)
# ================================================================
# = Build the Experiment objects =
# ================================================================
local_exp = Experiment(LOCAL_METHODS, network=network, **exp_kwargs)
global_exp = Experiment(GLOBAL_METHODS, network=network, **exp_kwargs)
# ================================================================
# = Build the methodNests =
# ================================================================
# --- randomly evaluated method nest
random_nest = MethodNest(Experiment.do_random_evals,
{'sample_domain': DOMAIN,
'ball_factory': BALL_FACTORY,
'num_random_points': NUM_RANDOM})
# --- data-based method nest
data_nest = MethodNest(Experiment.do_data_evals, data_arg_bundle)
# --- hypercube stuff
cube_nest = MethodNest(Experiment.do_unit_hypercube_eval)
local_nests = [random_nest, data_nest]
global_nests = [cube_nest]
def build_jobs(epoch_no, network=None):
local_job_name = '%s_EPOCH%04d_LOCAL' % (NAME, epoch_no)
local_job = Job(local_job_name, local_exp, local_nests,
save_loc=SCHEDULE_DIR)
local_job.write()
global_job_name = '%s_EPOCH%04d_GLOBAL' % (NAME, epoch_no)
global_job = Job(global_job_name, global_exp, global_nests,
save_loc=SCHEDULE_DIR)
global_job.write()
job_do_every = DoEvery(build_jobs, FREQUENCY)
# ==============================================================
# = Train the network =
# ==============================================================
train.training_loop(network, train_params, epoch_callback=job_do_every)
def main():
# ==========================================================
# = SETUP -- CHANGE THESE VARIABLES!!! =
# ==========================================================
# --- COMMON BLOCK (holds names, objectives, etc)
NAME = 'REGBUNDLE' # Name for this experiment
C_VECTOR = np.array([1.0, -1.0])
PRIMAL_NORM = 'linf'
DATA_PARAMS = {'num_points': 256,
'k': 10,
'radius': 0.02,
'num_classes': 2,
'dimension': 2}
LAYER_SIZES = [2, 20, 40, 20, 2] # defines architecture of net to test
# --- CONTROL BLOCK (holds training params, num_restarts, jobs at end?)
REGULARIZER_SERIES = {'vanilla': [train.XEntropyReg()],
'l2Pen': [train.XEntropyReg(),
train.LpWeightReg(scalar=1e-2, lp='l2')],
'l1Pen': [train.XEntropyReg(),
train.LpWeightReg(scalar=1e-3, lp='l1')],
'FGSM': [train.FGSM(0.1)]}
NUM_RESTARTS = 5 # how many times to restart training
JOB_FREQUENCY = 100
# --- EXPERIMENTAL PARAM BLOCK (holds ball_factory, which methods to eval)
RADIUS = 0.1 # Ball factory radius for random/data evals
RANDOM_SEED = 420 # Dataset random seed
NUM_EXP_RANDOM = 20 # num of random points to test in experiments
NUM_EXP_DATA = 20 # num of data points to test in experiments
LOCAL_METHODS = [FastLip, LipLP, LipMIP] #Methods to do random/data
GLOBAL_METHODS = [LipMIP, FastLip, LipLP, SeqLip, LipSDP, NaiveUB, RandomLB] # Methods to do unit hcube
# -- COMPUTED HELPER BLOCK
exp_kwargs = {'c_vector': C_VECTOR,
'primal_norm': PRIMAL_NORM}
DOMAIN = Hyperbox.build_unit_hypercube(DATA_PARAMS['dimension'])
BALL_FACTORY = Factory(Hyperbox.build_linf_ball, radius=RADIUS)
# ================================================================
# = Data Parameters Setup =
# ================================================================
# Make both the training/validation sets
data_params = dl.RandomKParameters(**DATA_PARAMS)
dataset = dl.RandomDataset(data_params, batch_size=128,
random_seed=RANDOM_SEED)
train_set, _ = dataset.split_train_val(1.0)
# Make the data arg_bundle object
loader_kwargs = {'batch_size': NUM_EXP_DATA,
'random_seed': RANDOM_SEED}
data_arg_bundle = {'data_type': 'synthetic',
'params': data_params,
'loader_kwargs': loader_kwargs,
'ball_factory': BALL_FACTORY}
# ================================================================
# = Build the methodNests =
# ================================================================
# --- randomly evaluated method nest
random_nest = MethodNest(Experiment.do_random_evals,
{'sample_domain': DOMAIN,
'ball_factory': BALL_FACTORY,
'num_random_points': NUM_EXP_DATA})
# --- data-based method nest
data_nest = MethodNest(Experiment.do_data_evals, data_arg_bundle)
# --- hypercube stuff
cube_nest = MethodNest(Experiment.do_unit_hypercube_eval)
local_nests = [random_nest, data_nest]
global_nests = [cube_nest]
def build_callback_and_final(reg_name):
# Builds the epoch_callback, final call
def build_jobs(epoch_no, network=None, NAME=NAME, reg_name=reg_name):
prefix = '%s_REG%s_EPOCH%04d' % (NAME, reg_name, epoch_no)
local_exp = Experiment(LOCAL_METHODS, network=network, **exp_kwargs)
global_exp = Experiment(GLOBAL_METHODS, network=network, **exp_kwargs)
local_job = Job('%s_LOCAL' % prefix, local_exp, local_nests,
save_loc=SCHEDULE_DIR)
global_job = Job('%s_GLOBAL' % prefix, global_exp, global_nests,
save_loc=SCHEDULE_DIR)
local_job.write()
global_job.write()
if JOB_FREQUENCY is None:
return None, build_jobs
else:
return DoEvery(build_jobs, JOB_FREQUENCY), build_jobs
# ==============================================================
# = Train the networks =
# ==============================================================
for reg_name, regularizers in REGULARIZER_SERIES.items():
print('-' * 30, "TRAINING --", reg_name)
# First build job builder:
callback, final = build_callback_and_final(reg_name)
# Then train function
loss_functional = train.LossFunctional(regularizers=regularizers)
train_params = train.TrainParameters(train_set, train_set, 500,
loss_functional=loss_functional,
test_after_epoch=100)
network = ReLUNet(layer_sizes=LAYER_SIZES)
train.best_of_k(network, train_params, NUM_RESTARTS,
epoch_callback=callback)
# Finally call the final fxn
final(epoch_no=train_params.num_epochs, network=network)
def main():
NAME = 'WideSeq'
exp_kwargs = {'c_vector': np.array([1.0, -1.0]), 'primal_norm': 'linf'}
DIMENSION = 2
RADIUS = 0.1
GLOBAL_LO = np.zeros(DIMENSION)
GLOBAL_HI = np.ones(DIMENSION)
RANDOM_SEED = 420
DOMAIN = Hyperbox.build_unit_hypercube(DIMENSION)
BALL_FACTORY = Factory(Hyperbox.build_linf_ball, radius=RADIUS)
LAYER_SEQ = [([2, 20] + [20 * i] + [20, 2]) for i in range(1, 6)]
def NAME_FXN(network):
""" Returns a string based on the network """
width = network.layer_sizes[2]
return '%s_WIDTH%04d' % (NAME, width)
# ================================================================
# = Data Parameters Setup =
# ================================================================
# Make both the training/validation sets
data_params = dl.RandomKParameters(num_points=300,
k=10,
radius=0.01,
dimension=DIMENSION)
dataset = dl.RandomDataset(data_params,
batch_size=128,
random_seed=RANDOM_SEED)
train_set, _ = dataset.split_train_val(1.0)
# Make the data arg_bundle object
loader_kwargs = {'batch_size': 100, 'random_seed': RANDOM_SEED}
data_arg_bundle = {
'data_type': 'synthetic',
'params': data_params,
'loader_kwargs': loader_kwargs,
'ball_factory': BALL_FACTORY
}
# ================================================================
# = Training Parameter Setup =
# ================================================================
# Build the loss functional and set the optimizer
xentropy = train.XEntropyReg()
l2_penalty = train.LpWeightReg(scalar=1e-4, lp='l2')
loss_functional = train.LossFunctional(regularizers=[xentropy])
train_params = train.TrainParameters(train_set,
train_set,
500,
loss_functional=loss_functional,
test_after_epoch=50)
# ================================================================
# = Build the methodNests =
# ================================================================
# --- randomly evaluated method nest
random_nest = MethodNest(
Experiment.do_random_evals, {
'sample_domain': DOMAIN,
'ball_factory': BALL_FACTORY,
'num_random_points': 20
})
# --- data-based method nest
data_nest = MethodNest(Experiment.do_data_evals, data_arg_bundle)
# --- hypercube stuff
cube_nest = MethodNest(Experiment.do_unit_hypercube_eval)
local_nests = [random_nest, data_nest, cube_nest]
global_nests = [cube_nest]
def build_jobs(network, **exp_kwargs):
local_exp = Experiment([FastLip, LipLP, LipMIP],
network=network,
**exp_kwargs)
global_exp = Experiment(
[LipMIP, FastLip, LipLP, SeqLip, LipSDP, NaiveUB],
network=network,
**exp_kwargs)
prefix = NAME_FXN(network)
#prefix = '%s_RELUS%02d' % (NAME, network.num_relus)
local_job_name = prefix + "_LOCAL"
local_job = Job(local_job_name,
local_exp,
local_nests,
save_loc=SCHEDULE_DIR)
global_job_name = prefix + "_GLOBAL"
global_job = Job(global_job_name,
global_exp,
global_nests,
save_loc=SCHEDULE_DIR)
local_job.write()
global_job.write()
# ==============================================================
# = Train the networks =
# ==============================================================
for layer_size in LAYER_SEQ:
print("Starting training:", layer_size)
network = ReLUNet(layer_sizes=layer_size)
train.training_loop(network, train_params)
build_jobs(network, **exp_kwargs)
def main():
# ==========================================================
# = SETUP -- CHANGE THESE VARIABLES!!! =
# ==========================================================
NAME = 'kdData'
C_VECTOR = np.array([1.0, -1.0])
PRIMAL_NORM = 'linf'
RADIUS = 0.2
RANDOM_SEED = 420
LAYER_SIZES = [20, 40, 80, 2]
DIMENSION_SERIES = [2, 4, 8, 16]
NUM_POINT_SERIES = [250, 1000, 2000, 4000]
LOCAL_METHODS = [FastLip, LipLP, LipMIP]
GLOBAL_METHODS = [LipMIP, FastLip, LipLP, SeqLip, LipSDP,
NaiveUB, RandomLB]
exp_kwargs = {'c_vector': C_VECTOR,
'primal_norm': PRIMAL_NORM}
# ==========================================================
# = HELPER SETUP -- THESE CAN BE LEFT ALONE =
# ==========================================================
BALL_FACTORY = Factory(Hyperbox.build_linf_ball, radius=RADIUS)
def NAME_FXN(network):
""" Returns a string based on the network """
return '%s_DIM%04d' % (NAME, network.layer_sizes[0])
def build_jobs(network, **exp_kwargs):
local_exp = Experiment([FastLip, LipLP, LipMIP],
network=network, **exp_kwargs)
global_exp = Experiment([LipMIP, FastLip, LipLP, SeqLip, LipSDP, NaiveUB],
network=network, **exp_kwargs)
prefix = NAME_FXN(network)
#prefix = '%s_RELUS%02d' % (NAME, network.num_relus)
local_job_name = prefix + "_LOCAL"
local_job = Job(local_job_name, local_exp, local_nests,
save_loc=SCHEDULE_DIR)
global_job_name = prefix + "_GLOBAL"
global_job = Job(global_job_name, global_exp, global_nests,
save_loc=SCHEDULE_DIR)
local_job.write()
global_job.write()
# =======================================================
# = LOOP OVER DIMENSIONS WE ALLOW =
# =======================================================
for DIMENSION, NUM_POINTS in zip(DIMENSION_SERIES, NUM_POINT_SERIES):
# ----------- Build dataset -----------
DOMAIN = Hyperbox.build_unit_hypercube(DIMENSION)
data_params = dl.RandomKParameters(num_points=NUM_POINTS, k=5 * DIMENSION,
radius=0.01 * DIMENSION, dimension=DIMENSION)
dataset = dl.RandomDataset(data_params, batch_size=128,
random_seed=RANDOM_SEED)
train_set, _ = dataset.split_train_val(1.0)
# Make the data arg_bundle object
loader_kwargs = {'batch_size': 100, 'random_seed': RANDOM_SEED}
data_arg_bundle = {'data_type': 'synthetic',
'params': data_params,
'loader_kwargs': loader_kwargs,
'ball_factory': BALL_FACTORY}
# ----------- Build training parameters -----------
# Build the loss functional and set the optimizer
xentropy = train.XEntropyReg()
l2_penalty = train.LpWeightReg(scalar=1e-4, lp='l2')
loss_functional = train.LossFunctional(regularizers=[xentropy])
train_params = train.TrainParameters(train_set, train_set, 1000,
loss_functional=loss_functional,
test_after_epoch=50)
# ----------- Build MethodNests -----------
# --- randomly evaluated method nest
random_nest = MethodNest(Experiment.do_random_evals,
{'sample_domain': DOMAIN,
'ball_factory': BALL_FACTORY,
'num_random_points': 20})
# --- data-based method nest
data_nest = MethodNest(Experiment.do_data_evals, data_arg_bundle)
# --- hypercube stuff
cube_nest = MethodNest(Experiment.do_unit_hypercube_eval)
local_nests = [random_nest, data_nest, cube_nest]
global_nests = [cube_nest]
# ----------- Train the networks -----------
print("Starting training: DIMENSION ", DIMENSION)
network = ReLUNet(layer_sizes=[DIMENSION] + LAYER_SIZES)
train.training_loop(network, train_params)
build_jobs(network, **exp_kwargs)