def progress(steps, elapsed):
print '{} of {} processed ({} s)'.format(steps, len(queries),
elapsed)
util.metadata('steps', steps)
util.metadata('gb_used', util.gb_used())
sys.stdout.flush()
f.flush()
def control(self, experiment):
if experiment.epochs >= self.max_epochs:
print 'Halted after reaching max epochs.'
experiment.halt = True
if experiment.steps % self.report_wait == 0:
print 'steps: {}, epochs: {:.2f}'.format(experiment.steps,
experiment.epochs)
util.metadata('steps', experiment.steps, self.path)
util.metadata('epochs', experiment.epochs, self.path)
# report last seen
time_rep = datetime.now().strftime('%H:%M:%S %m/%d')
util.metadata('last_seen', time_rep, self.path)
# report memory used
util.metadata('gb_used', util.gb_used(), self.path)
if experiment.steps % self.save_wait == 0 and experiment.steps != 0:
print 'saving params...'
experiment.model.save_model(self.path)
def control(self, maximizer):
if maximizer.steps >= self.max_steps:
print 'Halted after reaching max steps.'
maximizer.halt = True
if maximizer.steps % self.report_wait == 0:
epochs = float(maximizer.steps * maximizer.batch_size) / len(maximizer.train)
print 'steps: {}, epochs: {:.2f}'.format(maximizer.steps, epochs)
util.metadata('steps', maximizer.steps)
util.metadata('epochs', epochs)
# report last seen
time_rep = datetime.now().strftime('%H:%M:%S %m/%d')
util.metadata('last_seen', time_rep)
# report memory used
util.metadata('gb_used', util.gb_used())
if maximizer.steps % self.save_wait == 0 and maximizer.steps != 0:
print 'saving params...'
with open('params.cpkl', 'w') as f:
# convert params to picklable format
params = SparseVector(maximizer.params.as_dict())
pickle.dump(params, f)
def control(self, maximizer):
if maximizer.steps >= self.max_steps:
print "Halted after reaching max steps."
maximizer.halt = True
if maximizer.steps % self.report_wait == 0:
epochs = float(maximizer.steps * maximizer.batch_size) / len(maximizer.train)
print "steps: {}, epochs: {:.2f}".format(maximizer.steps, epochs)
util.metadata("steps", maximizer.steps)
util.metadata("epochs", epochs)
# report last seen
time_rep = datetime.now().strftime("%H:%M:%S %m/%d")
util.metadata("last_seen", time_rep)
# report memory used
util.metadata("gb_used", util.gb_used())
if maximizer.steps % self.save_wait == 0 and maximizer.steps != 0:
print "saving params..."
with open("params.cpkl", "w") as f:
# convert params to picklable format
params = SparseVector(maximizer.params.as_dict())
pickle.dump(params, f)
def progress(steps, elapsed):
print '{} of {} processed ({} s)'.format(steps, len(queries), elapsed)
util.metadata('steps', steps)
util.metadata('gb_used', util.gb_used())
sys.stdout.flush()
f.flush()