├── .gitignore ├── LICENSE ├── README.md ├── codes.py ├── evaluate_mwpm.py ├── find_threshold.py ├── generate_training_data.py ├── neural.py └── train_network.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Data files and notes 2 | *.npz 3 | Notes.ipynb 4 | Scratch Pad.ipynb 5 | 6 | # Byte-compiled / optimized / DLL files 7 | __pycache__/ 8 | *.py[cod] 9 | *$py.class 10 | 11 | # C extensions 12 | *.so 13 | 14 | # Distribution / packaging 15 | .Python 16 | env/ 17 | build/ 18 | develop-eggs/ 19 | dist/ 20 | downloads/ 21 | eggs/ 22 | .eggs/ 23 | lib/ 24 | lib64/ 25 | parts/ 26 | sdist/ 27 | var/ 28 | *.egg-info/ 29 | .installed.cfg 30 | *.egg 31 | 32 | # PyInstaller 33 | # Usually these files are written by a python script from a template 34 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 35 | *.manifest 36 | *.spec 37 | 38 | # Installer logs 39 | pip-log.txt 40 | pip-delete-this-directory.txt 41 | 42 | # Unit test / coverage reports 43 | htmlcov/ 44 | .tox/ 45 | .coverage 46 | .coverage.* 47 | .cache 48 | nosetests.xml 49 | coverage.xml 50 | *,cover 51 | .hypothesis/ 52 | 53 | # Translations 54 | *.mo 55 | *.pot 56 | 57 | # Django stuff: 58 | *.log 59 | local_settings.py 60 | 61 | # Flask stuff: 62 | instance/ 63 | .webassets-cache 64 | 65 | # Scrapy stuff: 66 | .scrapy 67 | 68 | # Sphinx documentation 69 | docs/_build/ 70 | 71 | # PyBuilder 72 | target/ 73 | 74 | # IPython Notebook 75 | .ipynb_checkpoints 76 | 77 | # pyenv 78 | .python-version 79 | 80 | # celery beat schedule file 81 | celerybeat-schedule 82 | 83 | # dotenv 84 | .env 85 | 86 | # virtualenv 87 | venv/ 88 | ENV/ 89 | 90 | # Spyder project settings 91 | .spyderproject 92 | 93 | # Rope project settings 94 | .ropeproject 95 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Neural Network Decoders for Quantum Error Correcting Codes 2 | 3 | See: https://www.nature.com/articles/s41598-017-11266-1 4 | 5 | Make your own decoder with: 6 | 7 | ``` 8 | train_network.py 5 output.model \ 9 | --onthefly 10000000 50000 \ 10 | --Xstab --Zstab \ 11 | --epochs 10 --prob 0.9 \ 12 | --learningrate .000001 --normcenterstab --layers 4 4 4 4 4 4 4 13 | ``` 14 | Test a network by adding the `--eval` flag. 15 | 16 | See `train_network.py -h` for description of each flag. 17 | -------------------------------------------------------------------------------- /codes.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Tested only in interactive use with the Jupyter notebook. 3 | Some tools might fail without it due to the use of `tnrange` 4 | and `tqdm_notebook` from `tqdm`. Similarly `matplotlib` is 5 | used with its interactive interface, which might cause trouble 6 | if `ioff` is not called.''' 7 | 8 | import itertools 9 | 10 | import numpy as np 11 | import scipy.linalg 12 | import scipy.stats as stats 13 | import scipy.optimize as optimize 14 | 15 | try: 16 | import matplotlib.pyplot as plt 17 | except ImportError: 18 | pass 19 | 20 | import networkx as nx 21 | 22 | from tqdm import tqdm, trange 23 | 24 | try: 25 | from IPython import display 26 | except ImportError: 27 | pass 28 | 29 | 30 | class ToricCode: 31 | ''' 32 | 33 | :: 34 | 35 | Lattice: 36 | X00--Q00--X01--Q01--X02... 37 | | | | 38 | Q10 Z00 Q11 Z01 Q12 39 | | | | 40 | X10--Q20--X11--Q21--X12... 41 | . . . 42 | ''' 43 | def __init__(self, L): 44 | '''Toric code of ``2 L**2`` physical qubits and distance ``L``.''' 45 | self.L = L 46 | self.Xflips = np.zeros((2*L,L), dtype=np.dtype('b')) # qubits where an X error occured 47 | self.Zflips = np.zeros((2*L,L), dtype=np.dtype('b')) # qubits where a Z error occured 48 | self._Xstab = np.empty((L,L), dtype=np.dtype('b')) 49 | self._Zstab = np.empty((L,L), dtype=np.dtype('b')) 50 | 51 | @property 52 | def flatXflips2Zstab(self): 53 | L = self.L 54 | _flatXflips2Zstab = np.zeros((L**2, 2*L**2), dtype=np.dtype('b')) 55 | for i, j in itertools.product(range(L),range(L)): 56 | _flatXflips2Zstab[i*L+j, (2*i )%(2*L)*L+(j )%L] = 1 57 | _flatXflips2Zstab[i*L+j, (2*i+1)%(2*L)*L+(j )%L] = 1 58 | _flatXflips2Zstab[i*L+j, (2*i+2)%(2*L)*L+(j )%L] = 1 59 | _flatXflips2Zstab[i*L+j, (2*i+1)%(2*L)*L+(j+1)%L] = 1 60 | return _flatXflips2Zstab 61 | 62 | @property 63 | def flatZflips2Xstab(self): 64 | L = self.L 65 | _flatZflips2Xstab = np.zeros((L**2, 2*L**2), dtype=np.dtype('b')) 66 | for i, j in itertools.product(range(L),range(L)): 67 | _flatZflips2Xstab[(i+1)%L*L+(j+1)%L, (2*i+1)%(2*L)*L+(j+1)%L] = 1 68 | _flatZflips2Xstab[(i+1)%L*L+(j+1)%L, (2*i+2)%(2*L)*L+(j )%L] = 1 69 | _flatZflips2Xstab[(i+1)%L*L+(j+1)%L, (2*i+3)%(2*L)*L+(j+1)%L] = 1 70 | _flatZflips2Xstab[(i+1)%L*L+(j+1)%L, (2*i+2)%(2*L)*L+(j+1)%L] = 1 71 | return _flatZflips2Xstab 72 | 73 | @property 74 | def flatXflips2Zerr(self): 75 | L = self.L 76 | _flatXflips2Zerr = np.zeros((2, 2*L**2), dtype=np.dtype('b')) 77 | for k in range(L): 78 | _flatXflips2Zerr[0, (2*k+1)%(2*L)*L+(0 )%L] = 1 79 | _flatXflips2Zerr[1, (2*0 )%(2*L)*L+(k )%L] = 1 80 | return _flatXflips2Zerr 81 | 82 | @property 83 | def flatZflips2Xerr(self): 84 | L = self.L 85 | _flatZflips2Xerr = np.zeros((2, 2*L**2), dtype=np.dtype('b')) 86 | for k in range(L): 87 | _flatZflips2Xerr[0, (2*0+1)%(2*L)*L+(k )%L] = 1 88 | _flatZflips2Xerr[1, (2*k )%(2*L)*L+(0 )%L] = 1 89 | return _flatZflips2Xerr 90 | 91 | def H(self, Z=True, X=False): 92 | H = [] 93 | if Z: 94 | H.append(self.flatXflips2Zstab) 95 | if X: 96 | H.append(self.flatZflips2Xstab) 97 | H = scipy.linalg.block_diag(*H) 98 | return H 99 | 100 | def E(self, Z=True, X=False): 101 | E = [] 102 | if Z: 103 | E.append(self.flatXflips2Zerr) 104 | if X: 105 | E.append(self.flatZflips2Xerr) 106 | E = scipy.linalg.block_diag(*E) 107 | return E 108 | 109 | def Zstabilizer(self): 110 | '''Return all measurements of the Z stabilizer with ``true`` marking non-trivial.''' 111 | stab = self._Zstab 112 | X = self.Xflips 113 | stab[0:-1,0:-1] = X[0:-2:2,0:-1:] ^ X[1:-1:2,0:-1:] ^ X[2::2,0:-1:] ^ X[1:-1:2,1::] 114 | stab[ -1,0:-1] = X[ -2 ,0:-1:] ^ X[ -1 ,0:-1:] ^ X[ 0 ,0:-1:] ^ X[ -1 ,1::] 115 | stab[0:-1, -1] = X[0:-2:2, -1 ] ^ X[1:-1:2, -1 ] ^ X[2::2, -1 ] ^ X[1:-1:2, 0] 116 | stab[ -1, -1] = X[ -2 , -1 ] ^ X[ -1 , -1 ] ^ X[ 0 , -1 ] ^ X[ -1 , 0] 117 | return stab 118 | 119 | def Xstabilizer(self): 120 | '''Return all measurements of the X stabilizer with ``true`` marking non-trivial.''' 121 | stab = self._Xstab 122 | Z = self.Zflips 123 | stab[1:,1:] = Z[1:-2:2,1:] ^ Z[2:-1:2,0:-1] ^ Z[3::2,1:] ^ Z[2:-1:2,1:] 124 | stab[0 ,1:] = Z[ -1 ,1:] ^ Z[ 0 ,0:-1] ^ Z[ 1 ,1:] ^ Z[ 0 ,1:] 125 | stab[1:,0 ] = Z[1:-2:2,0 ] ^ Z[2:-1:2, -1] ^ Z[3::2,0 ] ^ Z[2:-1:2,0 ] 126 | stab[0 ,0 ] = Z[ -1 ,0 ] ^ Z[ 0 , -1] ^ Z[ 1 ,0 ] ^ Z[ 0 ,0 ] 127 | return stab 128 | 129 | def _plot_flips(self, s, flips_yx, label): 130 | '''Given an array of yx coordiante plot qubit flips on subplot ``s``.''' 131 | if not len(flips_yx): return 132 | y, x = flips_yx 133 | x = x.astype(float) 134 | x[y%2==0] += 0.5 135 | x = np.concatenate([x, x-self.L, x]) 136 | y = np.concatenate([y/2., y/2., y/2.-self.L]) 137 | s.plot(x, y,'o', ms=50/self.L, label=label) 138 | 139 | def plot(self, legend=True, stabs=True): 140 | '''Plot the state of the system (including stabilizers).''' 141 | f = plt.figure(figsize=(5,5)) 142 | s = f.add_subplot(1,1,1) 143 | self._plot_legend = legend 144 | 145 | self._plot_flips(s, self.Xflips.nonzero(), label='X') 146 | self._plot_flips(s, self.Zflips.nonzero(), label='Z') 147 | self._plot_flips(s, (self.Xflips & self.Zflips).nonzero(), label='Y') 148 | 149 | if stabs: 150 | y, x = self.Zstabilizer().nonzero() 151 | x = np.concatenate([x+0.5, x+0.5-self.L, x+0.5, x+0.5-self.L]) 152 | y = np.concatenate([y+0.5, y+0.5, y+0.5-self.L, y+0.5-self.L]) 153 | s.plot(x,y,'s', mew=0, ms=190/self.L, label='plaq') 154 | 155 | y, x = self.Xstabilizer().nonzero() 156 | s.plot(x, y, '+', mew=100/self.L, ms=200/self.L, label='star') 157 | 158 | s.set_xticks(range(0,self.L)) 159 | s.set_yticks(range(0,self.L)) 160 | s.set_xlim(-0.6,self.L-0.4) 161 | s.set_ylim(-0.6,self.L-0.4) 162 | s.invert_yaxis() 163 | for tic in s.xaxis.get_major_ticks(): 164 | tic.tick1On = tic.tick2On = False 165 | tic.label1On = tic.label2On = False 166 | for tic in s.yaxis.get_major_ticks(): 167 | tic.tick1On = tic.tick2On = False 168 | tic.label1On = tic.label2On = False 169 | s.grid() 170 | if legend: s.legend() 171 | return f, s 172 | 173 | def _wgraph(self, operator): 174 | g = nx.Graph() 175 | if operator == 'Z': 176 | nodes = zip(*self.Zstabilizer().nonzero()) 177 | elif operator == 'X': 178 | nodes = zip(*self.Xstabilizer().nonzero()) 179 | def dist(node1, node2): 180 | dy = abs(node1[0]-node2[0]) 181 | dy = min(self.L-dy, dy) 182 | dx = abs(node1[1]-node2[1]) 183 | dx = min(self.L-dx, dx) 184 | return dx+dy 185 | g.add_weighted_edges_from((node1, node2, -dist(node1, node2)) 186 | for node1, node2 in itertools.combinations(nodes, 2)) 187 | return g 188 | 189 | def Zwgraph(self): 190 | '''The distance graph for non-trivial Z stabilizer.''' 191 | return self._wgraph('Z') 192 | 193 | def Xwgraph(self): 194 | '''The distance graph for non-trivial X stabilizer.''' 195 | return self._wgraph('X') 196 | 197 | def Zcorrections(self): 198 | '''Qubits on which to apply Z operator to fix the X stabilizer.''' 199 | L = self.L 200 | graph = self.Xwgraph() 201 | matches = {tuple(sorted(_)) for _ in 202 | nx.max_weight_matching(graph, maxcardinality=True).items()} 203 | qubits = set() 204 | for (y1, x1), (y2, x2) in matches: 205 | ym, yM = 2*min(y1, y2), 2*max(y1, y2) 206 | if yM-ym > L: 207 | ym, yM = yM, ym+2*L 208 | horizontal = yM if (x2-x1)*(y2-y1)<0 else ym 209 | else: 210 | horizontal = ym if (x2-x1)*(y2-y1)<0 else yM 211 | xm, xM = min(x1, x2), max(x1, x2) 212 | if xM-xm > L/2: 213 | xm, xM = xM, xm+L 214 | vertical = xM 215 | else: 216 | vertical = xm 217 | qubits.update((horizontal%(2*L), _%L) for _ in range(xm, xM)) 218 | qubits.update(((_+1)%(2*L), vertical%L) for _ in range(ym, yM, 2)) 219 | return matches, qubits 220 | 221 | def Xcorrections(self): 222 | '''Qubits on which to apply X operator to fix the Z stabilizer.''' 223 | L = self.L 224 | graph = self.Zwgraph() 225 | matches = {tuple(sorted(_)) for _ in 226 | nx.max_weight_matching(graph, maxcardinality=True).items()} 227 | qubits = set() 228 | for (y1, x1), (y2, x2) in matches: 229 | ym, yM = 2*min(y1, y2), 2*max(y1, y2) 230 | if yM-ym > L: 231 | ym, yM = yM, ym+2*L 232 | horizontal = yM if (x2-x1)*(y2-y1)<0 else ym 233 | else: 234 | horizontal = ym if (x2-x1)*(y2-y1)<0 else yM 235 | xm, xM = min(x1, x2), max(x1, x2) 236 | if xM-xm > L/2: 237 | xm, xM = xM, xm+L 238 | vertical = xM 239 | else: 240 | vertical = xm 241 | qubits.update(((horizontal+1)%(2*L), (_+1)%L) for _ in range(xm, xM)) 242 | qubits.update(((_+2)%(2*L), vertical%L) for _ in range(ym, yM, 2)) 243 | return matches, qubits 244 | 245 | def plot_corrections(self, s, plot_matches=False): 246 | '''Add to subplot ``s`` the corrections that have to be performed according to min. weight matching.''' 247 | def stitch_torus(y1, y2): 248 | if abs(y1-y2)>L/2: 249 | return (y1+L, y2-L) if y1nprandomuniform(size=npsum(propagated)) 176 | mismatch = stab!=npdot(H,sample)%2 177 | attempts += 1 178 | return attempts 179 | -------------------------------------------------------------------------------- /train_network.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | parser = argparse.ArgumentParser(description='Train a neural network to decode a code.', 4 | formatter_class=argparse.RawDescriptionHelpFormatter, 5 | epilog='''\ 6 | ''') 7 | parser.add_argument('dist', type=int, 8 | help='the distance of the code') 9 | parser.add_argument('out', type=str, 10 | help='the name of the output file (used as a prefix for the log file as well)') 11 | parser.add_argument('--trainset', type=str, 12 | help='the name of the training set file (generated by `generate_training_data.py`); if not specified --onthefly is assumed') 13 | parser.add_argument('--onthefly', type=int, nargs=2, default=[2000000, 50000], 14 | help='generate the training set on the fly, specify training and validation size (default: %(default)s)') 15 | parser.add_argument('--prob', type=float, default=0.9, 16 | help='the probability of no error on the physical qubit when generating training data (considered only if --onthefly is present) (default: %(default)s)') 17 | parser.add_argument('--load', type=str, default='', 18 | help='the file from which to load a pretrained model weights (optional, requires correct hyperparameters)') 19 | parser.add_argument('--eval', action='store_true', 20 | help='if present, calculate the fraction of successful corrections based on sampling the NN using the validation set') 21 | parser.add_argument('--giveup', type=int, default=1000000, 22 | help='after how many samples to give up decoding a given test error vector (considered only if --eval is present) (default: %(default)s)') 23 | parser.add_argument('--batch', type=int, default=512, 24 | help='the batch size (default: %(default)s)') 25 | parser.add_argument('--epochs', type=int, default=20, 26 | help='the number of epochs (default: %(default)s)') 27 | parser.add_argument('--learningrate', type=float, default=0.002, 28 | help='the learning rate (default: %(default)s)') 29 | parser.add_argument('--hact', type=str, default='tanh', 30 | help='the activation for hidden layers (default: %(default)s)') 31 | parser.add_argument('--act', type=str, default='sigmoid', 32 | help='the activation for the output layer (default: %(default)s)') 33 | parser.add_argument('--loss', type=str, default='binary_crossentropy', 34 | help='the loss to be optimized (default: %(default)s)') 35 | parser.add_argument('--layers', type=float, default=[4, 4, 4], nargs='+', 36 | help='the list of sizes of the hidden layers (as a factor of the output layer) (default: %(default)s)') 37 | parser.add_argument('--normcentererr', action='store_true', 38 | help='if present, zero-center and normalize the error training and evaluation data (should be done in advance for pregenerated data)') 39 | parser.add_argument('--normcenterstab', action='store_true', 40 | help='if present, zero-center and normalize the stabilizer training and evaluation data (should be done in advance for pregenerated data)') 41 | parser.add_argument('--batchnorm', type=float, default=0, 42 | help='if nonzero, batchnormalize each layer with the given momentum (default: %(default)s)') 43 | parser.add_argument('--Zstab', action='store_true', 44 | help='if present, include the Z stabilizer in the neural network') 45 | parser.add_argument('--Xstab', action='store_true', 46 | help='if present, include the X stabilizer in the neural network') 47 | 48 | args = parser.parse_args() 49 | print(args) 50 | 51 | from neural import create_model, data_generator, do_normcenterstab, undo_normcentererr, smart_sample 52 | from codes import ToricCode 53 | import numpy as np 54 | import tqdm 55 | 56 | 57 | if args.trainset: 58 | f = np.load(args.trainset) 59 | x_test = [] 60 | y_test = [] 61 | if args.Zstab: 62 | x_test.append(f['arr_4']) 63 | y_test.append(f['arr_5']) 64 | if args.Xstab: 65 | x_test.append(f['arr_6']) 66 | y_test.append(f['arr_7']) 67 | x_test = np.hstack(x_test) 68 | y_test = np.hstack(y_test) 69 | 70 | model = create_model(L=args.dist, 71 | hidden_sizes=args.layers, 72 | hidden_act=args.hact, 73 | act=args.act, 74 | loss=args.loss, 75 | Z=args.Zstab, X=args.Xstab, 76 | learning_rate=args.learningrate, 77 | normcentererr_p=args.prob if args.normcentererr else None, 78 | batchnorm=args.batchnorm 79 | ) 80 | L = args.dist 81 | code = ToricCode(L) 82 | out_dimZ = 2*L**2 * args.Zstab 83 | out_dimX = 2*L**2 * args.Xstab 84 | in_dim = L**2 * (args.Xstab+args.Zstab) 85 | H = code.H(args.Zstab,args.Xstab) 86 | 87 | 88 | if args.load: 89 | model.load_weights(args.load) 90 | if args.epochs: 91 | if args.trainset: 92 | raise NotImplementedError("This is still using the OLD keras API. Update it!") 93 | x_train = [] 94 | y_train = [] 95 | if args.Zstab: 96 | x_train.append(f['arr_0']) 97 | y_train.append(f['arr_1']) 98 | if args.Xstab: 99 | x_train.append(f['arr_2']) 100 | y_train.append(f['arr_3']) 101 | x_train = np.hstack(x_train) 102 | y_train = np.hstack(y_train) 103 | hist = model.fit(x_train, y_train, 104 | nb_epoch=args.epochs, 105 | batch_size=args.batch, 106 | validation_data=(x_test, y_test) 107 | ) 108 | else: 109 | dat = data_generator(H, out_dimZ, out_dimX, in_dim, args.prob, args.batch, 110 | normcenterstab=args.normcenterstab, normcentererr=args.normcentererr) 111 | val = data_generator(H, out_dimZ, out_dimX, in_dim, args.prob, args.batch, 112 | normcenterstab=args.normcenterstab, normcentererr=args.normcentererr) 113 | hist = model.fit_generator(dat, args.onthefly[0]//args.batch, args.epochs, 114 | validation_data=val, validation_steps=args.onthefly[1]//args.batch) 115 | model.save_weights(args.out) 116 | with open(args.out+'.log', 'w') as f: 117 | f.write(str((hist.params, hist.history))) 118 | if args.eval: 119 | E = ToricCode(L).E(args.Zstab,args.Xstab) 120 | both = args.Zstab and args.Xstab 121 | if both: 122 | Hz = ToricCode(L).H(True, False) 123 | Hx = ToricCode(L).H(False, True) 124 | outlen = 2*L**2*(args.Zstab+args.Xstab) 125 | inlen = L**2*(args.Zstab+args.Xstab) 126 | c = cz = cx = 0 127 | if args.trainset: 128 | stabflipgen = zip(x_test, y_test) 129 | size = len(y_test) 130 | else: 131 | size = args.onthefly[1] 132 | stabflipgen = data_generator(H, out_dimZ, out_dimX, in_dim, args.prob, batch_size=1, size=size) 133 | full_log = np.zeros((size, E.shape[0]+args.Zstab+args.Xstab), dtype=int) 134 | for i, (stab, flips) in tqdm.tqdm(enumerate(stabflipgen), total=size): 135 | if args.normcenterstab: 136 | stab_normed = do_normcenterstab(stab, args.prob) 137 | pred = model.predict(stab_normed).ravel() 138 | else: 139 | pred = model.predict(stab).ravel() 140 | if args.normcentererr: 141 | pred = undo_normcentererr(pred, args.prob) 142 | stab = stab.ravel() 143 | flips = flips.ravel() 144 | sample = pred>np.random.uniform(size=outlen) 145 | if both: 146 | attemptsZ = smart_sample(Hz, stab[:inlen//2], pred[:outlen//2], sample[:outlen//2], args.giveup) 147 | attemptsX = smart_sample(Hx, stab[inlen//2:], pred[outlen//2:], sample[outlen//2:], args.giveup) 148 | else: 149 | attempts = smart_sample(H, stab, pred, sample, args.giveup) 150 | errors = E.dot((sample+flips)%2)%2 151 | if np.any(errors) or np.any(stab!=H.dot(sample)%2): 152 | c += 1 153 | if both: 154 | cz += np.any(errors[:len(errors)//2]) 155 | cx += np.any(errors[len(errors)//2:]) 156 | if both: 157 | full_log[i,:-2] = errors 158 | full_log[i,-2] = attemptsZ 159 | full_log[i,-1] = attemptsX 160 | else: 161 | full_log[i,:-1] = errors 162 | full_log[i,-1] = attempts 163 | with open(args.out+'.eval', 'w') as f: 164 | if both: 165 | f.write(str(((1-c/size),(1-cz/size),(1-cx/size)))) 166 | else: 167 | f.write(str(((1-c/size),))) 168 | np.savetxt(args.out+'.eval.log', full_log, fmt='%d') 169 | --------------------------------------------------------------------------------