def train_epoch(self, epoch):
if self.train_problems_loader is None:
self.train_problems_loader = init_problems_loader(
self.opts.train_dir)
epoch_start = time.clock()
epoch_train_cost = 0.0
epoch_train_mat = ConfusionMatrix()
train_problems, train_filename = self.train_problems_loader.get_next()
for problem in train_problems:
d = self.build_feed_dict(problem)
_, logits, cost = self.sess.run(
[self.apply_gradients, self.logits, self.cost], feed_dict=d)
epoch_train_cost += cost
epoch_train_mat.update(problem.is_sat, logits > 0)
epoch_train_cost /= len(train_problems)
epoch_train_mat = epoch_train_mat.get_percentages()
epoch_end = time.clock()
learning_rate = self.sess.run(self.learning_rate)
self.save(epoch)
return (train_filename, epoch_train_cost, epoch_train_mat,
learning_rate, epoch_end - epoch_start)
def test(self, test_data_dir):
test_problems_loader = init_problems_loader(test_data_dir)
results = []
while test_problems_loader.has_next():
test_problems, test_filename = test_problems_loader.get_next()
epoch_test_cost = 0.0
epoch_test_mat = ConfusionMatrix()
for problem in test_problems:
d = self.build_feed_dict(problem)
logits, cost = self.sess.run([self.logits, self.cost], feed_dict=d)
epoch_test_cost += cost
epoch_test_mat.update(problem.is_sat, logits > 0)
epoch_test_cost /= len(test_problems)
epoch_test_mat = epoch_test_mat.get_percentages()
results.append((test_filename, epoch_test_cost, epoch_test_mat))
return results
def train_epoch(self, epoch):
if self.train_problems_loader is None:
self.train_problems_loader = init_problems_loader(self.opts.train_dir)
epoch_start = time.clock()
epoch_train_cost = 0.0
epoch_train_mat = ConfusionMatrix()
train_problems, train_filename = self.train_problems_loader.get_next()
for problem in train_problems:
d = self.build_feed_dict(problem)
self.batchnum = problem.n_vars//len(problem.is_sat)
acc,feature_map_out, _, logits, cost, image, issat, XX = self.sess.run(
[self.accuracy, self.all_votes, self.apply_gradients, self.logits, self.cost, self.image, self.is_sat, self.merged], feed_dict=d)
epoch_train_cost += cost
epoch_train_mat.update(problem.is_sat, logits > 0)
# print(acc)
epoch_train_cost /= len(train_problems)
epoch_train_mat = epoch_train_mat.get_percentages()
epoch_end = time.clock()
learning_rate = self.sess.run(self.learning_rate)
self.save(epoch)
self.train_writer.add_summary(XX, epoch)
return (train_filename, epoch_train_cost, epoch_train_mat, learning_rate, epoch_end - epoch_start)