def jobman(state, channel):
# load dataset
_train_data = ListSequences(path=state['path'],
pca=state['pca'],
subset=state['subset'],
which='train',
one_hot=False,
nbits=32)
train_data = _train_data.export_dense_format(
sequence_length=state['seqlen'],
overlap=state['overlap'])
valid_data = ListSequences(
path = state['path'],
pca=state['pca'],
subset=state['subset'],
which='valid',
one_hot=False,
nbits=32)
model = biRNN(
nhids=state['nhids'],
nouts=numpy.max(train_data.data_y)+1,
nins=train_data.data_x.shape[-1],
activ = TT.nnet.sigmoid,
seed = state['seed'],
bs = state['bs'],
seqlen = state['seqlen'])
algo = SGD(model, state, train_data)
main = MainLoop(train_data,valid_data, None, model, algo, state, channel)
main.main()
def jobman(state, channel):
rng = numpy.random.RandomState(state['seed'])
model = DBMinpainting(state)
data = DataMNIST(state['path'], state['mbs'], state['bs'], rng,
same_batch=state['samebatch'],
callback=model.callback)
algo = natSGD(model, state, data)
main = MainLoop(data, model, algo, state, channel)
main.main()
def jobman(state, channel):
rng = numpy.random.RandomState(state['seed'])
data = DataMNIST(state['path'], state['mbs'], state['bs'], rng,
state['unlabled'])
model = convMat(state, data)
if state['natSGD'] == 0:
algo = SGD(model, state, data)
else:
algo = natSGD(model, state, data)
main = MainLoop(data, model, algo, state, channel)
main.main()
def jobman(state, channel):
rng = numpy.random.RandomState(state['seed'])
model = DBMinpainting(state)
data = DataMNIST(state['path'],
state['mbs'],
state['bs'],
rng,
same_batch=state['samebatch'],
callback=model.callback)
algo = natSGD(model, state, data)
main = MainLoop(data, model, algo, state, channel)
main.main()
def jobman(state, channel):
rng = numpy.random.RandomState(state['seed'])
data = DataMNIST(
state['path'],
state['mbs'],
state['bs'],
rng,
state['unlabled'])
model = mlp(state, data)
if state['natSGD'] == 0:
algo = SGD(model, state, data)
else:
algo = natSGD(model, state, data)
main = MainLoop(data, model, algo, state, channel)
main.main()
def main():
args = parse_args()
state = getattr(experiments.nmt, args.proto)()
if args.state:
if args.state.endswith(".py"):
state.update(eval(open(args.state).read()))
else:
with open(args.state) as src:
state.update(cPickle.load(src))
for change in args.changes:
state.update(eval("dict({})".format(change)))
logging.basicConfig(level=getattr(logging, state['level']), format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
logger.debug("State:\n{}".format(pprint.pformat(state)))
rng = numpy.random.RandomState(state['seed'])
enc_dec = RNNEncoderDecoder(state, rng, args.skip_init)
enc_dec.build()
lm_model = enc_dec.create_lm_model()
logger.debug("Load data")
train_data = get_batch_iterator(state)
logger.debug("Compile trainer")
algo = eval(state['algo'])(lm_model, state, train_data)
logger.debug("Run training")
main = MainLoop(train_data, None, None, lm_model, algo, state, None,
reset=state['reset'],
hooks=[RandomSamplePrinter(state, lm_model, train_data)]
if state['hookFreq'] >= 0
else None,
valid=validate_translation)
if state['reload']:
main.load()
if state['loopIters'] > 0:
main.main()