prepare_dirs_and_logger(config)

if not config.task.lower().startswith('tsp'):

raise Exception("[!] Task should starts with TSP")

if config.max_enc_length is None:

config.max_enc_length = config.max_data_length

if config.max_dec_length is None:

config.max_dec_length = config.max_data_length

rng = np.random.RandomState(config.random_seed)

tf.set_random_seed(config.random_seed)

model = Model(config)

batch_size = config.batch_size

train_enc_seq, train_target_seq, train_enc_seq_length, train_target_seq_length = gen_data('data/tsp10.txt')

eval_enc_seq,eval_target_seq,eval_enc_seq_length,eval_target_seq_length = train_enc_seq[-batch_size:], \

train_target_seq[-batch_size:], \

train_enc_seq_length[-batch_size:], \

train_target_seq_length[-batch_size:]

train_enc_seq, train_target_seq, train_enc_seq_length, train_target_seq_length= train_enc_seq[: -batch_size], \

train_target_seq[:-batch_size], \

train_enc_seq_length[:-batch_size], \

train_target_seq_length[:-batch_size]

test_enc_seq, test_target_seq, test_enc_seq_length, test_target_seq_length = gen_data('data/tsp10_test.txt')

with tf.Session() as sess:

sess.run(tf.global_variables_initializer())

for step in range(min(config.max_step,len(train_enc_seq)//batch_size)):

train_batch={

'enc_seq': train_enc_seq[step * batch_size:(step + 1) * batch_size],

'enc_seq_length': train_enc_seq_length[step * batch_size:(step + 1) * batch_size],

'target_seq': train_target_seq[step * batch_size:(step + 1) * batch_size],

'target_seq_length': train_target_seq_length[step * batch_size:(step + 1) * batch_size]

}

loss = model.train(sess,train_batch)

print(str(step) + " train loss : " + str(loss))

if step > 0 and step % config.eval_step == 0:

eval_batch = {

'enc_seq': eval_enc_seq,

'enc_seq_length': eval_enc_seq_length,

'target_seq': eval_target_seq,

'target_seq_length': eval_target_seq_length

}

eval_loss = model.eval(sess,eval_batch)