def validation_acc(model, dev_iters, epoch, epochs, node_dict, edge_dict,
max_nodes, cuda):
""" Evaluate the model on dev set"""
model.eval()
eval_st = time.time()
graphs, graph_corrects = 0, 0
for i, dev_it in enumerate(dev_iters):
if cuda:
samples = move_to_cuda(dev_it)
else:
samples = dev_it
_, _, _, batch_graph_correct = greedy_search(
model, samples["src_graph"], samples["src_text"],
samples["tgt_graph"], node_dict, edge_dict, max_nodes, cuda)
graph_corrects += batch_graph_correct
graphs += 1
acc = graph_corrects / graphs
eval_time = (time.time() - eval_st) / 60
eval_info = "[ Eval {:02}/{:02}]: accuracy={:.4f} elapse={:.4f} mins"
print(eval_info.format(epoch + 1, epochs, acc, eval_time))
model.train()
return acc
def BuscaGulosa(self):
table = self.get_table()
print(table)
initial_state = StateNode(Game8(table), None, None, 0, 0)
start = int(round(time.time() * 1000))
path = greedy_search(initial_state)
end = int(round(time.time() * 1000))
for state in path:
self.depth_label['text'] = 'Profundidade: ' + str(state.depth)
b = state.game.get_b_position()
state.game.table[b[0]][b[1]] = ''
self.set_table(state.game.table)
state.game.show_table()
time.sleep(1)
self.generated_nodes_label['text'] = 'Nós gerados: ' + str(
s.generated_nodes)
self.execution_time['text'] = 'Tempo de execução (ms): ' + str(end -
start)
def infer_search(src_tokenizer, dst_tokenizer, transformer, config, methord='beam_search'):
if methord == 'beam_search':
_, y_outputs, _, x_placeholder = beam_search(batch_size=1, beam_width=FLAGS.beam_width,
vocab_size=config.vocab_size, max_len=FLAGS.max_len,
hidden_size=config.hidden_size,
sos_id=dst_tokenizer.bos_id(),
eos_id=dst_tokenizer.eos_id(),
inst=transformer)
elif methord == 'greedy_search':
_, y_outputs, x_placeholder = greedy_search(batch_size=1,
max_len=FLAGS.max_len,
sos_id=dst_tokenizer.bos_id(),
eos_id=dst_tokenizer.eos_id(),
inst=transformer)
else:
raise NotImplementedError('尚未支持')
sess = tf.Session()
saver = tf.train.Saver()
model_file = tf.train.latest_checkpoint(FLAGS.model_dir)
saver.restore(sess=sess, save_path=model_file)
fpw = open(file=FLAGS.infer_file + '.dst', mode='w', encoding='utf-8')
with open(file=FLAGS.infer_file, mode='r', encoding='utf-8') as fp:
for line in fp:
line = line.strip()
idxs = src_tokenizer.encode_as_ids(input=line)
idxs = idxs[:FLAGS.max_len-1]
idxs.append(src_tokenizer.eos_id())
for i in range(len(idxs), FLAGS.max_len):
idxs.append(0)
y_idxs, = sess.run(
fetches=[y_outputs],
feed_dict={
x_placeholder: [idxs]
}
)
y_idxs_val = dst_tokenizer.decode_ids(ids=y_idxs[0].tolist())
fpw.write(y_idxs_val + '\n')
fpw.close()
elif grid[i][j] == u'diamond_block':
grid[i][j] = 'd'
elif grid[i][j] == u'emerald_block':
grid[i][j] = 'E'
elif grid[i][j] == u'redstone_block':
grid[i][j] = 'R'
else:
grid[i][j] = '?'
pretty_print_grid(grid)
problem = MazeProblem(grid)
if search_alg == 'bfs':
plan = breadth_first_search(problem)
elif search_alg == 'gs':
plan = greedy_search(problem)
if plan:
for action in plan:
print 'action: {0}'.format(action)
command = commands[action]
agent_host.sendCommand(command)
time.sleep(0.5)
world_state = agent_host.getWorldState()
if world_state.is_mission_running or len(
world_state.rewards
) == 0 or world_state.rewards[-1].getValue() < 100.0:
print 'Mission failed: did not reach goal state.'
break
else:
print 'Mission accomplished: goal state reached.'