# -*- coding: utf-8 -*-
#/usr/bin/python2
'''
By kyubyong park. kbpark.linguist@gmail.com.
https://www.github.com/kyubyong/sudoku
'''
from __future__ import print_function
import tensorflow as tf
import numpy as np
from train import Graph
from data_load import load_data
from hyperparams import Hyperparams as hp
import os
def write_to_file(x, y, preds, fout):
'''Writes to file.
Args:
x: A 3d array with shape of [N, 9, 9]. Quizzes where blanks are represented as 0's.
y: A 3d array with shape of [N, 9, 9]. Solutions.
preds: A 3d array with shape of [N, 9, 9]. Predictions.
fout: A string. File path of the output file where the results will be written.
'''
with open(fout, 'w') as fout:
total_hits, total_blanks = 0, 0
for xx, yy, pp in zip(x.reshape(-1, 9*9), y.reshape(-1, 9*9), preds.reshape(-1, 9*9)): # sample-wise
fout.write("qz: {}\n".format("".join(str(num) if num != 0 else "_" for num in xx)))
fout.write("sn: {}\n".format("".join(str(num) for num in yy)))
fout.write("pd: {}\n".format("".join(str(num) for num in pp)))
expected = yy[xx == 0]
got = pp[xx == 0]
num_hits = np.equal(expected, got).sum()
num_blanks = len(expected)
fout.write("accuracy = %d/%d = %.2f\n\n" % (num_hits, num_blanks, float(num_hits) / num_blanks))
total_hits += num_hits
total_blanks += num_blanks
fout.write("Total accuracy = %d/%d = %.2f\n\n" % (total_hits, total_blanks, float(total_hits) / total_blanks))
def test():
x, y = load_data(type="test")
g = Graph(is_training=False)
with g.graph.as_default():
sv = tf.train.Supervisor()
with sv.managed_session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
# Restore parameters
sv.saver.restore(sess, tf.train.latest_checkpoint(hp.logdir))
print("Restored!")
# Get model name
mname = open(hp.logdir + '/checkpoint', 'r').read().split('"')[1] # model name
if not os.path.exists('results'): os.mkdir('results')
fout = 'results/{}.txt'.format(mname)
import copy
_preds = copy.copy(x)
while 1:
istarget, probs, preds = sess.run([g.istarget, g.probs, g.preds], {g.x:_preds, g.y: y})
probs = probs.astype(np.float32)
preds = preds.astype(np.float32)
probs *= istarget #(N, 9, 9)
preds *= istarget #(N, 9, 9)
probs = np.reshape(probs, (-1, 9*9)) #(N, 9*9)
preds = np.reshape(preds, (-1, 9*9))#(N, 9*9)
_preds = np.reshape(_preds, (-1, 9*9))
maxprob_ids = np.argmax(probs, axis=1) # (N, ) <- blanks of the most probable prediction
maxprobs = np.max(probs, axis=1, keepdims=False)
for j, (maxprob_id, maxprob) in enumerate(zip(maxprob_ids, maxprobs)):
if maxprob != 0:
_preds[j, maxprob_id] = preds[j, maxprob_id]
_preds = np.reshape(_preds, (-1, 9, 9))
_preds = np.where(x==0, _preds, y) # # Fill in the non-blanks with correct numbers
if np.count_nonzero(_preds) == _preds.size: break
write_to_file(x.astype(np.int32), y, _preds.astype(np.int32), fout)
if __name__ == '__main__':
test()
print("Done")
