def __init__(self, model_file):
dat = np.load(model_file)
args = dat['argsd'].tolist()
rnn_param_values = dat['rnn_param_values'].tolist()
trn, dev, tst = get_sents(args['lang'])
repclass = getattr(rep, 'Rep' + args['rep'])
repobj = repclass()
for d in (trn, dev, tst):
for sent in d:
sent.update({
'cseq': repobj.get_cseq(sent),
'wiseq': repobj.get_wiseq(sent),
'tseq': repobj.get_tseq(sent)
})
trn = sorted(trn, key=lambda sent: len(sent['cseq']))
dev = sorted(dev, key=lambda sent: len(sent['cseq']))
tst = sorted(tst, key=lambda sent: len(sent['cseq']))
self.feat = featchar.Feat(args['feat'])
self.feat.fit(trn, dev, tst)
self.vdecoder = decoder.ViterbiDecoder(trn, self.feat)
batcher = Batcher(args['n_batch'], self.feat) # batch size 1
devdat = batcher.get_batches(dev)
tstdat = batcher.get_batches(tst)
rdnn = RNN(self.feat.NC, self.feat.NF, args)
cost, dev_predictions = rdnn.predict(devdat)
cost, tst_predictions = rdnn.predict(tstdat)
self.predictions = {}
self.predictions['dev'] = dev_predictions
self.predictions['tst'] = tst_predictions
self.dset = {}
self.dset['dev'] = dev
self.dset['tst'] = tst
self.repobj = repobj
self.reporter = exper.Reporter(self.feat, rep.get_ts_bio)
print rdnn.l_soft_out.get_params()
print rdnn.blayers[0][0].get_params()
params = lasagne.layers.get_all_param_values(rdnn.layers[-1])
print map(np.shape, params)
lasagne.layers.set_all_param_values(rdnn.layers[-1],
rnn_param_values[:len(params)])
logger = logging.getLogger()
logger.setLevel(logging.INFO)
shandler = logging.StreamHandler()
shandler.setLevel(logging.INFO)
logger.addHandler(shandler)
validator = Validator(trn, dev, tst, batcher, self.reporter)
validator.validate(rdnn, args, self.vdecoder)
def __init__(self, dset, feat):
self.feat = feat
self.tfunc = rep.get_ts_bio if dset.level == 'char' else lambda x, y: y
# self.tdecoder = decoder.ViterbiDecoder(dset.trn, feat) if dset.level == 'char' else decoder.MaxDecoder(dset.trn, feat)
self.tdecoder = decoder.ViterbiDecoder(
dset.trn, feat) if dset.level == 'char' else decoder.WDecoder(
dset.trn, feat)