decoder = Decoder(14, 512, num_layers=3).cuda() learning_rate = 0.001 teacher_forcing_ratio = 0.3 # value doesn't matter; immediately overwritten # create encoder outputs # given some input sequence - input # load data data = [] dat = open(sys.argv[1]) for l in dat: p = l.split() targ_prog = p[0] targ_prog_f = e.eval_init(targ_prog) in_data = [int(p[1][2 * b:2 * b + 2], base=16) for b in range(256)] data.append((targ_prog, in_data, targ_prog_f)) dat.close() # shuffled lists for data sampling in_sample = range(256) data_sample = range(len(data)) # test accuracy on full data TEST_ACC_SAMP = 30
start = time.time() for i in range(N): test.sess_open(randos[i], progs[i]) opened = time.time() - start for i in order: assert test.cand_query(randos[i], progs[i]) == (100000.) done = time.time() - start print "open after " print opened print "done after " print done print test.read_odom() order = random.sample(range(N), N) queries = [] for i in range(N): queries += [test.eval_init(progs[i])] for i in order: assert queries[i](progs[i]) == (100000.) for i in range(4): print queries[order[i]](progs[i])