def train():
print('Configuring RNN model...')
config = TRNNConfig()
config.dropout_keep_prob = 1.0
start_time = time.time()
# config.batch_size = 10
total_batch = 0 # 总批次
best_mse_val = 99999999 # 最佳验证集准确率
best_loss_val = 99999999 # 最佳验证集准确率
last_improved = 0 # 记录上一次提升批次
require_improvement = 5000 # 如果超过1000轮未提升，提前结束训练
count = 0
tensorboard_dir = config.tensorboard_dir
# 配置GPU内存分配方式
tfconfig = tf.ConfigProto(log_device_placement=True)
tfconfig.gpu_options.allow_growth = True
tfconfig.gpu_options.per_process_gpu_memory_fraction = 0.6
with tf.Graph().as_default(), tf.Session(config=tfconfig) as sess:
train_dir_list = os.listdir(config.train_dir_tf)
train_dir_list = [
os.path.join(config.train_dir_tf, i) for i in train_dir_list
]
queueTrain = tf.train.string_input_producer(
train_dir_list, num_epochs=config.num_epochs)
title_len, title, label, frame_weight = read_example(queueTrain)
title_len_batch, title_batch, label_batch, frame_weight_batch = tf.train.batch(
[title_len, title, label, frame_weight],
batch_size=config.batch_size,
capacity=100000,
num_threads=1)
with tf.variable_scope("model",
initializer=tf.random_uniform_initializer(
-1 * 1, 1)):
model = TextRNN(config=config,
input_x_len=title_len_batch,
input_x=title_batch,
input_y=label_batch,
frame_weight=frame_weight_batch)
tf.summary.scalar("loss", model.loss)
tf.summary.scalar("mse", model.mse)
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(tensorboard_dir, sess.graph)
fetches = [model.loss, model.mse]
feed_dict = {}
# init
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess.run(init_op)
# 配置 Saver
saver = tf.train.Saver(write_version=saver_pb2.SaverDef.V1)
if not config.retraining:
saver.restore(sess=sess, save_path=config.modelPath) # 读取保存的模型
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
if not os.path.exists(config.save_dir):
os.makedirs(config.save_dir)
try:
while not coord.should_stop():
# Run training steps or whatever
# titles, labels = sess.run([title_batch, label_batch])
if total_batch % config.save_per_batch == 0:
# 每多少轮次将训练结果写入tensorboard scalar
s = sess.run(merged_summary, feed_dict)
writer.add_summary(s, total_batch)
if total_batch % config.print_per_batch == 0:
# 每多少轮次输出在训练集和验证集上的性能
loss_val, mse_val = sess.run(fetches, feed_dict)
if mse_val < best_mse_val or loss_val < best_loss_val:
# 保存最好结果
best_mse_val = mse_val
best_loss_val = loss_val
last_improved = total_batch
improved_str = '*'
# saver.save(sess=sess, save_path=config.save_path)
if total_batch % config.save_per_batch == 0:
saver.save(sess,
config.save_path + '_%03d' %
(total_batch / config.save_per_batch),
write_meta_graph=False)
else:
improved_str = ''
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Val Loss: {1:>6.5}, Mse: {2:>6.5}, Time: {3} {4}'
print(
msg.format(total_batch, loss_val, mse_val, time_dif,
improved_str))
# print(embedding_inputs)
sess.run(model.optim, feed_dict)
total_batch += 1
if total_batch - last_improved > require_improvement:
# 验证集正确率长期不提升，提前结束训练
print("No optimization for a long time, auto-stopping...")
coord.should_stop()
break # 跳出循环
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
coord.request_stop()
coord.join(threads)
def test():
print('Configuring RNN model...')
config = TRNNConfig()
config.dropout_keep_prob = 1.0
config.num_epochs = 1
start_time = time.time()
config.batch_size = 10
count = 0
# 配置GPU内存分配方式
tfconfig = tf.ConfigProto(log_device_placement=True)
tfconfig.gpu_options.allow_growth = True
tfconfig.gpu_options.per_process_gpu_memory_fraction = 0.6
fw = file(config.test_dir_output, "w")
with tf.Graph().as_default(), tf.Session(config=tfconfig) as sess:
test_dir_list = os.listdir(config.test_dir_tf)
test_dir_list = [
os.path.join(config.test_dir_tf, i) for i in test_dir_list
]
queueTest = tf.train.string_input_producer(
test_dir_list, num_epochs=config.num_epochs)
text, title_len, title, label, frame_weight = read_example_test(
queueTest)
text_batch, title_len_batch, title_batch, label_batch, frame_weight_batch = tf.train.batch(
[text, title_len, title, label, frame_weight],
batch_size=config.batch_size,
capacity=50000,
num_threads=1)
with tf.variable_scope("model",
initializer=tf.random_uniform_initializer(
-1 * 1, 1)):
model = TextRNN(config=config,
input_x_len=title_len_batch,
input_x=title_batch,
input_y=label_batch,
frame_weight=frame_weight_batch)
fetches = [text_batch, model.input_x_len, model.y_pred, model.input_y]
feed_dict = {}
# init
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess.run(init_op)
# 配置 Saver
saver = tf.train.Saver()
saver.restore(sess=sess, save_path=config.modelPath) # 读取保存的模型
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
if not os.path.exists(config.save_dir):
os.makedirs(config.save_dir)
try:
while not coord.should_stop():
texts, x_len, y_pred, y_test = sess.run(fetches,
feed_dict=feed_dict)
texts = "".join(texts.values).split("\n")
for i in range(len(texts) - 1):
score = [str(int(j * 100))
for j in y_test[i]][:x_len[i][0]]
y_test_i = " ".join(score)
score = [str(int(j * 100))
for j in y_pred[i]][:x_len[i][0]]
y_pred_i = " ".join(score)
fw.write(texts[i] + "\ttarget:\t" + y_test_i +
"\tpredict\t" + y_pred_i + "\n")
count = count + 1
if count % 10000 == 0:
print(count)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
coord.request_stop()
coord.join(threads)
fw.close()
