def main():
global outdir
set_backend("gnumpy")
report_args = { 'verbose' : True,
'interval' : 3, # how many epochs between progress reports (larger is faster)
'interval_rate' : 1.35,
'visualize' : True,
'log_mode' : 'html_anim' }
data = load_mnist(digits=[5,6,8,9],
split=[30,10,0])
settings_name = "basic2"
######################################################
# Generate a list of training configurations to try,
# and loop over them.
#
settings,prefix = make_train_settings(settings_name)
settings = enumerate_settings(settings)
index = 801
index_end = 1000
settings = settings[index-1:index_end]
num_restarts = 1 # How many random restarts do we try with the same parameters
open_logfile("gen_trainees-%s" % prefix,"%d total variants; %d restarts each" % (len(settings),num_restarts))
settingsfile = '%s/%s-settings.pkl' % (outdir,prefix)
quickdump(settingsfile,settings)
for setting in settings:
print ('\n\n-------------------- %s-%d --------------------' % (prefix,index))
print setting['model']
print setting['train']
for rep in range(num_restarts):
# Create the model and scale the data if necessary
model = make_model(setting['model'],data.Xshape,data.Yshape)
data.rescale(model.ideal_domain(),model.ideal_range())
# Set up a callback to collect snapshots of the training run
snapshots = []
report_args['callback'] = lambda event,status: report_callback(setting,snapshots,model,event,status)
# Train the model
trainer = TrainingRun(model,data,report_args,**setting['train'])
trainer.train()
trainer = None # force gc on any Tk window objects
# Save the training run to disk
save_trainee(snapshots,setting,prefix,index);
index += 1
#####################################################
close_logfile()
raw_input()
def main():
set_backend("gnumpy")
#set_gradcheck_mode(True)
######################################################
# Load MNIST dataset
tic()
data = load_mnist(digits=range(10),
split=[85,0,15],
#split=[30,0,0] # for faster training when debugging
)
print ("Data loaded in %.1fs" % toc())
######################################################
# Create a neural network with matching input/output dimensions
cfg = NeuralNetCfg(L1=1e-7*0,init_scale=0.1**2)
cfg.input(data.Xshape,dropout=0.2)
cfg.hidden(800,"logistic",dropout=0.5,maxnorm=4.0)
cfg.hidden(800,"logistic",dropout=0.5,maxnorm=4.0)
cfg.output(data.Yshape,"softmax",maxnorm=4.0)
model = NeuralNet(cfg)
######################################################
# Rescale the data to match the network's domain/range
data.rescale(model.ideal_domain(),model.ideal_range())
######################################################
# Train the network
report_args = { 'verbose' : True,
'interval' : 10, # how many epochs between progress reports (larger is faster)
'visualize' : True}
trainer = TrainingRun(model,data,report_args,
learn_rate=10,
learn_rate_decay=.998,
momentum=[(1,.5),(400,0.99)],
batchsize=128)
tic()
trainer.train(3000)
print ("Training took %.1fs" % toc())
#####################################################
if get_gradcheck_mode():
model.gradcheck(data.train)
raw_input()
def main(viz=False):
tic()
data = load_mnist()
print ("Data loaded in %.1fs" % toc())
# Create a neural network with matching input/output dimensions
#
cfg = NeuralNetCfg(L1=1e-6,init_scale=0.05)
cfg.input(data.Xshape)
cfg.hidden(800,"logistic",dropout=0.5)
cfg.hidden(800,"logistic",dropout=0.25)
cfg.output(data.Yshape,"softmax")
model = NeuralNet(cfg)
# Rescale the data to match the network's domain/range
#
data.rescale(model.ideal_domain(),model.ideal_range())
# Train the network
#
report_args = { 'verbose' : True,
'interval' : 5, # how many epochs between progress reports (larger is faster)
'window_size' : "compact",
'visualize' : viz}
trainer = TrainingRun(model,data,report_args,
learn_rate=2,
learn_rate_decay=.995,
momentum=[(0,.5),(400,0.9)],
batchsize=64)
print "Memory available after data loaded:", memory_info(gc=True)
tic()
trainer.train(100) # train for several epochs
print ("Training took %.1fs" % toc())
