def main():
# parse the command line arguments
parser = NeonArgparser(__doc__)
args = parser.parse_args()
logger = logging.getLogger()
logger.setLevel(args.log_thresh)
# Set up batch iterator for training images
train = ImgMaster(repo_dir="spectroDataTmp", set_name="train", inner_size=400, subset_pct=100)
val = ImgMaster(
repo_dir="spectroDataTmp", set_name="validation", inner_size=400, subset_pct=100, do_transforms=False
)
test = ImgMaster(
repo_dir="spectroTestDataTmp", set_name="validation", inner_size=400, subset_pct=100, do_transforms=False
)
train.init_batch_provider()
test.init_batch_provider()
print "Constructing network..."
model = constuct_network()
model.load_weights(args.model_file)
# Optimizer
opt = Adadelta()
# configure callbacks
valmetric = TopKMisclassification(k=5)
callbacks = Callbacks(model, train, eval_set=val, metric=valmetric, **args.callback_args)
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
# flag = input("Press Enter if you want to begin training process.")
print "Training network..."
print args.epochs
model.fit(train, optimizer=opt, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
mets = model.eval(test, metric=valmetric)
print "Validation set metrics:"
print "LogLoss: %.2f, Accuracy: %.1f %%0 (Top-1), %.1f %% (Top-5)" % (
mets[0],
(1.0 - mets[1]) * 100,
(1.0 - mets[2]) * 100,
)
test.exit_batch_provider()
train.exit_batch_provider()
def main():
# parse the command line arguments
parser = NeonArgparser(__doc__)
args = parser.parse_args()
logger = logging.getLogger()
logger.setLevel(args.log_thresh)
#Set up batch iterator for training images
print "Setting up data batch loaders..."
train = ImgMaster(repo_dir='dataTmp', set_name='train', inner_size=120, subset_pct=100)
val = ImgMaster(repo_dir='dataTmp', set_name='train', inner_size=120, subset_pct=100, do_transforms=False)
test = ImgMaster(repo_dir='dataTestTmp', set_name='train', inner_size=120, subset_pct=100, do_transforms=False)
train.init_batch_provider()
val.init_batch_provider()
test.init_batch_provider()
print "Constructing network..."
#Create AlexNet architecture
model = constuct_network()
#model.load_weights(args.model_file)
# drop weights LR by 1/250**(1/3) at epochs (23, 45, 66), drop bias LR by 1/10 at epoch 45
weight_sched = Schedule([22, 44, 65, 90, 97], (1/250.)**(1/3.))
opt_gdm = GradientDescentMomentum(0.01, 0.9, wdecay=0.005, schedule=weight_sched)
opt_biases = GradientDescentMomentum(0.04, 1.0, schedule=Schedule([130],.1))
opt = MultiOptimizer({'default': opt_gdm, 'Bias': opt_biases})
# configure callbacks
valmetric = TopKMisclassification(k=5)
callbacks = Callbacks(model, train, eval_set=val, metric=valmetric, **args.callback_args)
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
#flag = input("Press Enter if you want to begin training process.")
print "Training network..."
model.fit(train, optimizer=opt, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
mets = model.eval(test, metric=valmetric)
print 'Validation set metrics:'
print 'LogLoss: %.2f, Accuracy: %.1f %%0 (Top-1), %.1f %% (Top-5)' % (mets[0],
(1.0-mets[1])*100,
(1.0-mets[2])*100)
test.exit_batch_provider()
val.exit_batch_provider()
train.exit_batch_provider()
if args.model_file:
import os
assert os.path.exists(args.model_file), '%s not found' % args.model_file
mlp.load_weights(args.model_file)
# configure callbacks
callbacks = Callbacks(mlp, train, output_file=args.output_file)
if args.validation_freq:
class TopKMetrics(Callback):
def __init__(self, valid_set, epoch_freq=args.validation_freq):
super(TopKMetrics, self).__init__(epoch_freq=epoch_freq)
self.valid_set = valid_set
def on_epoch_end(self, epoch):
self.valid_set.reset()
allmetrics = TopKMisclassification(k=5)
stats = mlp.eval(self.valid_set, metric=allmetrics)
print ", ".join(allmetrics.metric_names) + ": " + ", ".join(map(str, stats.flatten()))
callbacks.add_callback(TopKMetrics(test))
if args.save_path:
checkpoint_schedule = range(1, args.epochs)
callbacks.add_serialize_callback(checkpoint_schedule, args.save_path, history=2)
mlp.fit(train, optimizer=opt, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
test.exit_batch_provider()
train.exit_batch_provider()
if args.validation_freq:
class TopKMetrics(Callback):
def __init__(self, valid_set, epoch_freq=args.validation_freq):
super(TopKMetrics, self).__init__(epoch_freq=epoch_freq)
self.valid_set = valid_set
def on_epoch_end(self, epoch):
self.valid_set.reset()
allmetrics = TopKMisclassification(k=5)
stats = mlp.eval(self.valid_set, metric=allmetrics)
print ", ".join(allmetrics.metric_names) + ": " + ", ".join(
map(str, stats.flatten()))
callbacks.add_callback(TopKMetrics(test))
if args.save_path:
checkpoint_schedule = range(args.epochs)
callbacks.add_serialize_callback(checkpoint_schedule,
args.save_path,
history=2)
mlp.fit(train,
optimizer=opt,
num_epochs=args.epochs,
cost=cost,
callbacks=callbacks)
test.exit_batch_provider()
train.exit_batch_provider()
def main():
# parse the command line arguments
parser = NeonArgparser(__doc__)
args = parser.parse_args()
logger = logging.getLogger()
logger.setLevel(args.log_thresh)
#Set up batch iterator for training images
print "Setting up data batch loaders..."
train = ImgMaster(repo_dir='dataTmp',
set_name='train',
inner_size=120,
subset_pct=100)
val = ImgMaster(repo_dir='dataTmp',
set_name='train',
inner_size=120,
subset_pct=100,
do_transforms=False)
test = ImgMaster(repo_dir='dataTestTmp',
set_name='train',
inner_size=120,
subset_pct=100,
do_transforms=False)
train.init_batch_provider()
val.init_batch_provider()
test.init_batch_provider()
print "Constructing network..."
#Create AlexNet architecture
model = constuct_network()
#model.load_weights(args.model_file)
# drop weights LR by 1/250**(1/3) at epochs (23, 45, 66), drop bias LR by 1/10 at epoch 45
weight_sched = Schedule([22, 44, 65, 90, 97], (1 / 250.)**(1 / 3.))
opt_gdm = GradientDescentMomentum(0.01,
0.9,
wdecay=0.005,
schedule=weight_sched)
opt_biases = GradientDescentMomentum(0.04,
1.0,
schedule=Schedule([130], .1))
opt = MultiOptimizer({'default': opt_gdm, 'Bias': opt_biases})
# configure callbacks
valmetric = TopKMisclassification(k=5)
callbacks = Callbacks(model,
train,
eval_set=val,
metric=valmetric,
**args.callback_args)
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
#flag = input("Press Enter if you want to begin training process.")
print "Training network..."
model.fit(train,
optimizer=opt,
num_epochs=args.epochs,
cost=cost,
callbacks=callbacks)
mets = model.eval(test, metric=valmetric)
print 'Validation set metrics:'
print 'LogLoss: %.2f, Accuracy: %.1f %%0 (Top-1), %.1f %% (Top-5)' % (
mets[0], (1.0 - mets[1]) * 100, (1.0 - mets[2]) * 100)
test.exit_batch_provider()
val.exit_batch_provider()
train.exit_batch_provider()
def main():
# parse the command line arguments
parser = NeonArgparser(__doc__)
args = parser.parse_args()
logger = logging.getLogger()
logger.setLevel(args.log_thresh)
#Set up batch iterator for training images
train = ImgMaster(repo_dir='spectroDataTmp',
set_name='train',
inner_size=400,
subset_pct=100)
val = ImgMaster(repo_dir='spectroDataTmp',
set_name='validation',
inner_size=400,
subset_pct=100,
do_transforms=False)
test = ImgMaster(repo_dir='spectroTestDataTmp',
set_name='validation',
inner_size=400,
subset_pct=100,
do_transforms=False)
train.init_batch_provider()
test.init_batch_provider()
print "Constructing network..."
model = constuct_network()
model.load_weights(args.model_file)
#Optimizer
opt = Adadelta()
# configure callbacks
valmetric = TopKMisclassification(k=5)
callbacks = Callbacks(model,
train,
eval_set=val,
metric=valmetric,
**args.callback_args)
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
#flag = input("Press Enter if you want to begin training process.")
print "Training network..."
print args.epochs
model.fit(train,
optimizer=opt,
num_epochs=args.epochs,
cost=cost,
callbacks=callbacks)
mets = model.eval(test, metric=valmetric)
print 'Validation set metrics:'
print 'LogLoss: %.2f, Accuracy: %.1f %%0 (Top-1), %.1f %% (Top-5)' % (
mets[0], (1.0 - mets[1]) * 100, (1.0 - mets[2]) * 100)
test.exit_batch_provider()
train.exit_batch_provider()
