def sample_pairs(self, gr, batch_size, shuffle=True, once=False):
""" A generator that produces random pairs from the dataset """
try:
id_N = int((len(gr['si03d']) + batch_size - 1) / batch_size)
ids = list(range(id_N))
while True:
if shuffle:
# XXX: We never swap samples between batches, does it matter?
random.shuffle(ids)
for i in ids:
sl = slice(i * batch_size, (i + 1) * batch_size)
ogr = graph_input_slice(gr, sl)
# s0, s1 are larger than the rest, unnerving keras
ogr.pop('s0', None)
ogr.pop('s1', None)
ogr['se03d'] = self.emb.map_jset(ogr['sj03d'])
ogr['se13d'] = self.emb.map_jset(ogr['sj13d'])
# print(sl)
# print('<<0>>', ogr['sj0'], ogr['se0'])
# print('<<1>>', ogr['sj1'], ogr['se1'])
yield ogr
if once:
break
except Exception:
traceback.print_exc()
def sample_pairs(self, gr, batch_size, shuffle=True, once=False):
""" A generator that produces random pairs from the dataset """
try:
id_N = int((len(gr['si03d']) + batch_size-1) / batch_size)
ids = list(range(id_N))
while True:
if shuffle:
# XXX: We never swap samples between batches, does it matter?
random.shuffle(ids)
for i in ids:
sl = slice(i * batch_size, (i+1) * batch_size)
ogr = graph_input_slice(gr, sl)
# s0, s1 are larger than the rest, unnerving keras
ogr.pop('s0', None)
ogr.pop('s1', None)
ogr['se03d'] = self.emb.map_jset(ogr['sj03d'])
ogr['se13d'] = self.emb.map_jset(ogr['sj13d'])
# print(sl)
# print('<<0>>', ogr['sj0'], ogr['se0'])
# print('<<1>>', ogr['sj1'], ogr['se1'])
yield ogr
if once:
break
except Exception:
traceback.print_exc()
def sample_pairs(self, gr, batch_size, shuffle=True, once=False):
""" A generator that produces random pairs from the dataset """
try:
id_N = int((len(gr['si0']) + batch_size - 1) / batch_size)
ids = list(range(id_N))
while True:
if shuffle:
# XXX: We never swap samples between batches, does it matter?
random.shuffle(ids)
for i in ids:
sl = slice(i * batch_size, (i + 1) * batch_size)
ogr = graph_input_slice(gr, sl)
ogr['se0'] = self.emb.map_jset(ogr['sj0'])
ogr['se1'] = self.emb.map_jset(ogr['sj1'])
# print(sl)
# print('<<0>>', ogr['sj0'], ogr['se0'])
# print('<<1>>', ogr['sj1'], ogr['se1'])
if 'score' in ogr:
y = ogr.pop('score')
else:
y = ogr.pop('classes')
yield (ogr, y)
if once:
break
except Exception:
traceback.print_exc()
def sample_pairs(gr, c, batch_size, once=False):
""" A generator that produces random pairs from the dataset """
# XXX: We drop the last few samples if (1e6 % batch_size != 0)
# XXX: We never swap samples between batches, does it matter?
ids = range(int(len(gr['si0']) / batch_size))
while True:
random.shuffle(ids)
for i in ids:
sl = slice(i * batch_size, (i+1) * batch_size)
ogr = graph_input_slice(gr, sl)
# TODO: Add support for discarding too long samples?
pad_graph(ogr)
yield ogr
if once:
break
def sample_pairs(self, gr, batch_size, shuffle=True, once=False):
""" A generator that produces random pairs from the dataset """
try:
id_N = int((len(gr['si0']) + batch_size-1) / batch_size)
ids = list(range(id_N))
while True:
if shuffle:
# XXX: We never swap samples between batches, does it matter?
random.shuffle(ids)
for i in ids:
sl = slice(i * batch_size, (i+1) * batch_size)
ogr = graph_input_slice(gr, sl)
# TODO: Add support for discarding too long samples?
pad_graph(ogr, s0pad=self.s0pad, s1pad=self.s1pad)
ogr['se0'] = self.emb.map_jset(ogr['sj0'])
ogr['se1'] = self.emb.map_jset(ogr['sj1'])
# print(sl)
# print('<<0>>', ogr['sj0'], ogr['se0'])
# print('<<1>>', ogr['sj1'], ogr['se1'])
yield ogr
if once:
break
except Exception:
traceback.print_exc()
