preselection=None,
labels=None):
return self.session.run([self.accuracy, self.loss],
self._prepare_data(steps, conjectures,
preselection, labels))
def predict(self, steps=None, conjectures=None, preselection=None):
#print("input shape: {}, {}".format(steps[1][0].shape, steps[1][1].shape))
predictions, logits = self.session.run([self.predictions, self.logits],
self._prepare_data(
steps, conjectures,
preselection))
minus, plus = np.hsplit(logits, 2)
return predictions, (plus - minus).flatten()
if __name__ == "__main__":
# when loaded alone, just try to construct a network
sys.excepthook = traceback_utils.shadow('/usr/')
network = Network()
vocab_size = 1996
network.construct(vocab_size,
128,
256,
use_conjectures=True,
extra_layer=True,
use_pooling=True)
isolated_predictions = np.array(isolated_predictions)
isolated_logits = np.array(isolated_logits)
print("In batch:")
print(" Logits: {}".format(logits))
print(" Prediction: {}".format(predictions))
print("Isolated:")
print(" Logits: {}".format(isolated_logits))
print(" Prediction: {}".format(isolated_predictions))
print("Difference:")
print(" Logits: {}".format(isolated_logits - logits))
print(" Prediction: {}".format(isolated_predictions - predictions))
logging.root.setLevel(logging.INFO)
sys.excepthook = traceback_utils.shadow(
'/usr/') # hide entrails of Tensorflow in error messages
cmd_parser = argparse.ArgumentParser(
prog='tree-holstep',
description='Run tree RNN network on Holstep dataset',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
cmd_parser.add_argument('--version',
action='version',
version='%(prog)s ' + version)
cmd_parser.add_argument('--quiet', dest='quiet', action='store_true')
cmd_parser.set_defaults(quiet=False)
cmd_parser.add_argument(
'--consistency_check',
dest='consistency_check',
action='store_true',
for graph_data, (lens, labels, (funcs, rels)) in data:
symbols = [
symbol_to_num[symbol]
for symbol in truncate_skolem(funcs + rels)
]
res_data.append((graph_data, (lens, labels, symbols)))
res.append(res_data)
return res
if __name__ == "__main__":
import traceback_utils
import sys
# hide entrails of Tensorflow in error messages
sys.excepthook = traceback_utils.shadow('/home/mirek/.local/')
with StopWatch("loading data"):
print("Loading data...")
sys.stdout.flush()
#test_data, train_data = load_data("deepmath/nndata2")
#test_data, train_data = load_data("bartosz/nndata/test", "bartosz/nndata/train")
test_data, train_data = load_data(
"enigma-2019-10/all-mzr02/test_sng",
"enigma-2019-10/all-mzr02/train_sng")
print("Enumerate symbols...")
symbol_to_num, test_data, train_data = enumerate_symbols(
test_data, train_data)
#total_symbols = len(symbol_to_num)
#print("{} symbols".format(total_symbols))