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']) 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()