saver.restore(sess, ckpt.model_checkpoint_path)
current_step = int(
os.path.basename(ckpt.model_checkpoint_path).split('-')[1])
print(current_step)
else:
print('Created new model parameters..')
current_step = 0
total_loss = 0
total_bleu = 0
total_perpl = 0
summary_writer = tf.summary.FileWriter(config.LOGS, graph=sess.graph)
for e in range(config.EPOCHS):
print("----- Epoch {}/{} -----".format(e + 1, config.EPOCHS))
batches = getBatches(trainingSamples, config.BATCH_SIZE)
if e != 0:
checkpoint_path = os.path.join(config.MODEL_DIR, config.MODEL_NAME)
saver.save(sess, checkpoint_path, e)
for nextBatch in tqdm(batches, desc="Training"):
if current_step == 150000:
break
else:
loss, summary, pred, target = model.train(sess, nextBatch)
bleu = get_bleu(target, pred)
total_loss += loss
total_perpl += 2**(float(loss))
total_bleu += bleu
current_step += 1
tf.app.flags.DEFINE_string('model_name', 'chatbot.ckpt', 'File name used for model checkpoints')
FLAGS = tf.app.flags.FLAGS
data_path = '/Users/shengwan/Desktop/seq2seq_chatbot_new-master/data/dataset-cornell-length10-filter1-vocabSize40000.pkl'
word2id, id2word, trainingSamples = loadDataset(data_path)
with tf.Session() as sess:
model = Seq2SeqModel(FLAGS.rnn_size, FLAGS.num_layers, FLAGS.embedding_size, FLAGS.learning_rate, word2id,
mode='train', use_attention=True, beam_search=False, beam_size=5, max_gradient_norm=5.0)
ckpt = tf.train.get_checkpoint_state(FLAGS.model_dir)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print('Reloading model parameters from ', ckpt.model_checkpoint_path)
sess = model.restore_last_session()
else:
print('Created new model parameters..')
sess.run(tf.global_variables_initializer())
current_step = 0
summary_writer = tf.summary.FileWriter(FLAGS.model_dir, graph=sess.graph)
for e in range(FLAGS.numEpochs):
print("----- Epoch {}/{} -----".format(e + 1, FLAGS.numEpochs))
batches = getBatches(trainingSamples, FLAGS.batch_size)
for nextBatch in tqdm(batches, desc="Training"):
loss, summary = model.train(sess, nextBatch)
current_step += 1
if current_step % FLAGS.steps_per_checkpoint == 0:
perplexity = math.exp(float(loss)) if loss < 300 else float('inf')
tqdm.write("----- Step %d -- Loss %.2f -- Perplexity %.2f" % (current_step, loss, perplexity))
summary_writer.add_summary(summary, current_step)
checkpoint_path = os.path.join(FLAGS.model_dir, FLAGS.model_name)
model.saver.save(sess, checkpoint_path, global_step=current_step)
ckpt = tf.train.get_checkpoint_state(model_dir)
# 如果模型存在
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print('Reloading model parameters..')
# 使用saver.restore()方法恢复变量
model.saver.restore(sess, ckpt.model_checkpoint_path)
else: # 找不到模型
raise ValueError('No such file:[{}]'.format(model_dir)) # 报错
else: # 从零开始训练模型
sess.run(tf.global_variables_initializer())
for e in range(epochs): # 对于每一个epoch
# 打印训练Epoch信息
print("----- Epoch {}/{} -----".format(e + 1, epochs))
# 将一个epoch中的数据制作成一个个batch，具体方法进入data_helpers.py查看
batches = getBatches(sources_data, targets_data, batch_size)
step = 0 # 记录训练了多少个batch
for nextBatch in batches: # 遍历batches中的每个batch
# 将这个batch的数据喂给网络进行训练
loss, summary = model.train(nextBatch)
if step % display == 0: # 每隔display个batch打印一次训练信息
# math.exp(x)返回e的x次方
# inf表示正无穷
# 计算困惑度
perplexity = math.exp(
float(loss)) if loss < 300 else float('inf')
# 打印loss和困惑度
print("----- Loss %.2f -- Perplexity %.2f" %
(loss, perplexity))
step += 1 # 计数，记录训练了多少个batch
# 保存当前网络模型参数
