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))