103、rasa_nlu 集成tf.estimator 做分类器 tf.data 做ETL部分，
最近一直在看tf dev summit 2018 , tf dev summit 2019
人家说一个东西从无到有刚开始可能有些神奇，比较好发展。但当时间一长的时候，再能把它做好就不容易啦
今天就来介绍一下tensorflow 的一些高级api，并且结合rasa_nlu 实际操作一下，构建一个分类器学习一下tf高级
API的用法。
主要使用的组件有
tf.estimator : (1) 谷歌说这个支持大规模分布式机器学习
(2) 并且很容易的就可以转换到其它方法上去，而且不需要修改很多代码
tf.data :(1) 可以很快速地加载数据，支持并行加载数据
(2)支持ETL过程。extract , transform,load (分batch_size,shuffle,repeat等方法)
(3)支持prefetch 当GPU在训练数据的时候可以调用一部分的cpu资源将数据prefetch到GPU上面
tf.saved_model:
(1)可以导出*.pb类型的模型文件，这种类型文件支持tensorflow serving, tensorflow lite.
(2)与*.ckpt文件不同的是这种文件同时保存了图结构和参数，而ckpt文件只有结点的参数和权重
tf.example:
(1)构建一个序列化对象传给tensorflow serving 或者predictior进行预测
tf.feature_column:
(1)用来描述定义训练数据的结构，以便在后边解析训练数据和预测的时候解析预测数据使用
下边是主要修改的代码部分
训练过程:
X, Y, intents_for_X = self._prepare_data_for_training(training_data, intent_dict)
num_classes = len(intent_dict)
# define classes number to classified
head = tf.contrib.estimator.multi_class_head(n_classes=num_classes)
# define feature spec for input x parsing
feature_names = ['a_in']
self.feature_columns = [tf.feature_column.numeric_column(key=k,shape=[1, X.shape[1]]) for k in feature_names]
x_tensor = {'a_in': X}
intents_for_X = intents_for_X.astype(np.int32)
# set gpu and tf graph confing
tf.logging.set_verbosity(tf.logging.INFO)
config_proto = self.get_config_proto(self.component_config)
# sparse_softmax_cross_entropy , build linear classified model
self.estimator = tf.contrib.estimator.LinearEstimator(
head = head,
feature_columns=self.feature_columns,
optimizer='Ftrl',
config=tf.estimator.RunConfig(session_config=config_proto)
)
# train model
self.estimator.train(input_fn=lambda: self.input_fn(x_tensor,
intents_for_X,
self.batch_size,
shuffle_num=1000,
mode = tf.estimator.ModeKeys.TRAIN),
max_steps=2000)
# evaluate model
results = self.estimator.evaluate(input_fn=lambda: self.input_fn(x_tensor,
intents_for_X,
self.batch_size,
shuffle_num=1000,
mode = tf.estimator.ModeKeys.PREDICT))
(2)保存模型的过程:
首先定义将预测数据解析为标准结构的函数
def input_fn(self,features, labels, batch_size, shuffle_num, mode):
"""
build tf.data set for input pipeline
:param features: type dict() , define input x structure for parsing
:param labels: type np.array input label
:param batch_size: type int number ,input batch_size
:param shuffle_num: type int number , random select the data
:param mode: type string ,tf.estimator.ModeKeys.TRAIN or tf.estimator.ModeKeys.PREDICT
:return: set() with type of (tf.data , and labels)
"""
dataset = tf.data.Dataset.from_tensor_slices((features, labels))
if mode == tf.estimator.ModeKeys.TRAIN:
dataset = dataset.shuffle(shuffle_num).batch(batch_size).repeat(self.epochs)
else:
dataset = dataset.batch(batch_size)
iterator = dataset.make_one_shot_iterator()
data, labels = iterator.get_next()
return data, labels
然后进行训练
def train(self, training_data, cfg=None, **kwargs):
# type: (TrainingData, Optional[RasaNLUModelConfig], **Any) -> None
"""Train the embedding intent classifier on a data set."""
intent_dict = self._create_intent_dict(training_data)
if len(intent_dict) < 2:
logger.error("Can not train an intent classifier. "
"Need at least 2 different classes. "
"Skipping training of intent classifier.")
return
self.inv_intent_dict = {v: k for k, v in intent_dict.items()}
self.encoded_all_intents = self._create_encoded_intents(intent_dict)
X, Y, intents_for_X = self._prepare_data_for_training(training_data, intent_dict)
num_classes = len(intent_dict)
# define classes number to classified
head = tf.contrib.estimator.multi_class_head(n_classes=num_classes)
# define feature spec for input x parsing
feature_names = ['a_in']
self.feature_columns = [tf.feature_column.numeric_column(key=k,shape=[1, X.shape[1]]) for k in feature_names]
x_tensor = {'a_in': X}
intents_for_X = intents_for_X.astype(np.int32)
# set gpu and tf graph confing
tf.logging.set_verbosity(tf.logging.INFO)
config_proto = self.get_config_proto(self.component_config)
# sparse_softmax_cross_entropy , build linear classified model
self.estimator = tf.contrib.estimator.LinearEstimator(
head = head,
feature_columns=self.feature_columns,
optimizer='Ftrl',
config=tf.estimator.RunConfig(session_config=config_proto)
)
# train model
self.estimator.train(input_fn=lambda: self.input_fn(x_tensor,
intents_for_X,
self.batch_size,
shuffle_num=1000,
mode = tf.estimator.ModeKeys.TRAIN),
max_steps=2000)
# evaluate model
results = self.estimator.evaluate(input_fn=lambda: self.input_fn(x_tensor,
intents_for_X,
self.batch_size,
shuffle_num=1000,
mode = tf.estimator.ModeKeys.PREDICT))
print(results)
（3）保存模型的过程
def persist(self, model_dir):
# type: (Text) -> Dict[Text, Any]
"""Persist this model into the passed directory.
Return the metadata necessary to load the model again."""
if self.estimator is None:
return {"classifier_file": None}
# build feature spec for tf.example parsing
feature_spec = tf.feature_column.make_parse_example_spec(self.feature_columns)
# build tf.example parser
serving_input_receiver_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
# export tf model
path = self.estimator.export_savedmodel(model_dir, serving_input_receiver_fn)
# decode model path to string
file_dir = os.path.basename(path).decode('utf-8')
with io.open(os.path.join(
model_dir,
self.name + "_inv_intent_dict.pkl"), 'wb') as f:
pickle.dump(self.inv_intent_dict, f)
with io.open(os.path.join(
model_dir,
self.name + "_encoded_all_intents.pkl"), 'wb') as f:
pickle.dump(self.encoded_all_intents, f)
return {"classifier_file": file_dir}
(3) 加载模型的过程:
from tensorflow.contrib import predictor as Pred
predict = Pred.from_saved_model(export_dir=os.path.join(model_dir,file_name),config=config_proto)
据说使用tensorflow.contrib.predictor加载模型进行预测比普通的加载模型的方式快10x
(4)再次进行预测的过程
X = message.get("text_features").tolist()
examples = []
feature = {}
# convert input x to tf.feature with float feature spec
feature['a_in'] = tf.train.Feature(float_list=tf.train.FloatList(value=X))
# build tf.example for prediction
example = tf.train.Example(
features=tf.train.Features(
feature=feature
)
)
# serialize tf.example to string
examples.append(example.SerializeToString())
# Make predictions.
result_dict = self.predictor({'inputs': examples})
result_score_list = result_dict['scores'][0]
max_score = np.max(result_dict['scores'][0])
max_index = np.argmax(result_dict['scores'][0])
运行的结果如下所示
整个项目的链接如下:
https://github.com/GaoQ1/rasa_nlu_gq
整个训练过程log如下:
training process :
C:\Users\weizhen.zhao\Documents\GitHub\rasa_nlu_gq\venv\Scripts\python.exe C:/Users/weizhen.zhao/Documents/GitHub/rasa_nlu_gq/rasa_nlu_gao/train.py -c sample_configs/config_embedding_bert_intent_estimator_classifier.yml --data data/examples/luis/HighTalkSQSWLuisAppStaging-GA-20180824.json --path projects/bert_gongan_v4
C:\Users\weizhen.zhao\Documents\GitHub\rasa_nlu_gq\rasa_nlu_gao\utils\__init__.py:236: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
return yaml.load(read_file(filename, "utf-8"))
server config:
max_batch_size = 256
prefetch_size = 10
fixed_embed_length = False
ventilator <-> sink = tcp://127.0.0.1:52870
num_process = 2
pooling_layer = [-2]
xla = False
cors = *
max_seq_len = 25
python_version = 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) [MSC v.1900 64 bit (AMD64)]
http_port = None
priority_batch_size = 16
zmq_version = 4.3.1
show_tokens_to_client = False
verbose = False
statistic = {'num_data_request': 0, 'num_sys_request': 1, 'num_active_client': 0, 'max_request_per_client': 1, 'num_total_seq': 0, 'num_total_request': 1, 'avg_request_per_client': 1.0, 'num_total_client': 1, 'num_max_request_per_client': 1, 'num_min_request_per_client': 1, 'min_request_per_client': 1}
device_map = []
num_worker = 1
tuned_model_dir = None
worker -> sink = tcp://127.0.0.1:52893
fp16 = False
cpu = False
client = 9d87beb2-78ba-4d70-b7e9-d2f257e598fb
config_name = bert_config.json
pyzmq_version = 18.0.1
mask_cls_sep = False
graph_tmp_dir = None
server_start_time = 2019-03-25 10:47:28.331322
ckpt_name = bert_model.ckpt
gpu_memory_fraction = 0.5
num_concurrent_socket = 8
server_current_time = 2019-03-25 10:54:09.843259
server_version = 1.8.4
model_dir = E:\chinese_L-12_H-768_A-12\chinese_L-12_H-768_A-12
port = 5555
port_out = 5556
tensorflow_version = ['1', '12', '0']
ventilator -> worker = ['tcp://127.0.0.1:52871', 'tcp://127.0.0.1:52872', 'tcp://127.0.0.1:52873', 'tcp://127.0.0.1:52874', 'tcp://127.0.0.1:52875', 'tcp://127.0.0.1:52876', 'tcp://127.0.0.1:52877', 'tcp://127.0.0.1:52878']
pooling_strategy = 2
http_max_connect = 10
Building prefix dict from the default dictionary ...
Loading model from cache C:\Users\WEIZHE~1.ZHA\AppData\Local\Temp\jieba.cache
Loading model cost 0.609 seconds.
Prefix dict has been built succesfully.
Epochs: 0%| | 0/17 [00:00<?, ?it/s]C:\Users\weizhen.zhao\AppData\Local\Programs\Python\Python35\lib\site-packages\bert_serving\client\__init__.py:285: UserWarning: some of your sentences have more tokens than "max_seq_len=25" set on the server, as consequence you may get less-accurate or truncated embeddings.
here is what you can do:
- disable the length-check by create a new "BertClient(check_length=False)" when you do not want to display this warning
- or, start a new server with a larger "max_seq_len"
'- or, start a new server with a larger "max_seq_len"' % self.length_limit)
Epochs: 100%|██████████| 17/17 [00:04<00:00, 3.97it/s]
2019-03-25 10:54:19 WARNING tensorflow - Using temporary folder as model directory: C:\Users\WEIZHE~1.ZHA\AppData\Local\Temp\tmp_9yu6pnr
2019-03-25 10:54:19.980918: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-03-25 10:54:20.231528: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7085
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.96GiB
2019-03-25 10:54:20.231786: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-03-25 10:54:23.162725: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-03-25 10:54:23.162866: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2019-03-25 10:54:23.162946: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2019-03-25 10:54:23.164279: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3072 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)
2019-03-25 10:54:37.740434: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-03-25 10:54:37.740607: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-03-25 10:54:37.740745: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2019-03-25 10:54:37.740828: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2019-03-25 10:54:37.740966: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3072 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)
{'average_loss': 0.077875145, 'loss': 0.076562986, 'global_step': 1800, 'accuracy': 0.9995338}
2019-03-25 10:54:56.449880: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-03-25 10:54:56.450049: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-03-25 10:54:56.450182: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2019-03-25 10:54:56.450264: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2019-03-25 10:54:56.450398: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3072 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)
2019-03-25 10:54:56 WARNING tensorflow - From C:\Users\weizhen.zhao\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\estimator\estimator.py:1044: calling SavedModelBuilder.add_meta_graph_and_variables (from tensorflow.python.saved_model.builder_impl) with legacy_init_op is deprecated and will be removed in a future version.
Instructions for updating:
Pass your op to the equivalent parameter main_op instead.
Process finished with exit code 0
预测过程的log如下
C:\Users\weizhen.zhao\Documents\GitHub\rasa_nlu_gq\venv\Scripts\python.exe C:/Users/weizhen.zhao/Documents/GitHub/rasa_nlu_gq/rasa_nlu_gao/server.py -c sample_configs/config_embedding_bert_intent_estimator_classifier.yml --path projects/bert_gongan_v4
2019-03-25 10:57:31 WARNING py.warnings - C:\Users\weizhen.zhao\Documents\GitHub\rasa_nlu_gq\rasa_nlu_gao\utils\__init__.py:236: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
return yaml.load(read_file(filename, "utf-8"))
2019-03-25 10:57:31+0800 [-] Log opened.
2019-03-25 10:57:31+0800 [-] Site starting on 5000
2019-03-25 10:57:31+0800 [-] Starting factory <twisted.web.server.Site object at 0x00000219FB3C4EB8>
2019-03-25 10:58:08+0800 [-] server config:
2019-03-25 10:58:08+0800 [-] zmq_version = 4.3.1
2019-03-25 10:58:08+0800 [-] priority_batch_size = 16
2019-03-25 10:58:08+0800 [-] http_port = None
2019-03-25 10:58:08+0800 [-] worker -> sink = tcp://127.0.0.1:52893
2019-03-25 10:58:08+0800 [-] ventilator -> worker = ['tcp://127.0.0.1:52871', 'tcp://127.0.0.1:52872', 'tcp://127.0.0.1:52873', 'tcp://127.0.0.1:52874', 'tcp://127.0.0.1:52875', 'tcp://127.0.0.1:52876', 'tcp://127.0.0.1:52877', 'tcp://127.0.0.1:52878']
2019-03-25 10:58:08+0800 [-] tuned_model_dir = None
2019-03-25 10:58:08+0800 [-] cpu = False
2019-03-25 10:58:08+0800 [-] pooling_strategy = 2
2019-03-25 10:58:08+0800 [-] server_version = 1.8.4
2019-03-25 10:58:08+0800 [-] fp16 = False
2019-03-25 10:58:08+0800 [-] http_max_connect = 10
2019-03-25 10:58:08+0800 [-] python_version = 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) [MSC v.1900 64 bit (AMD64)]
2019-03-25 10:58:08+0800 [-] statistic = {'avg_request_per_second': 3.7288529001263235, 'num_total_seq': 2145, 'num_total_client': 2, 'max_last_two_interval': 385.6274952, 'min_request_per_client': 1, 'avg_size_per_request': 112.5, 'min_request_per_second': 0.002593176089483362, 'num_active_client': 0, 'num_max_last_two_interval': 1, 'num_max_request_per_client': 1, 'avg_last_two_interval': 22.924579470588235, 'num_min_last_two_interval': 1, 'num_total_request': 19, 'max_request_per_second': 4.194699493825813, 'max_size_per_request': 128, 'max_request_per_client': 18, 'num_min_request_per_second': 1, 'min_size_per_request': 97, 'min_last_two_interval': 0.23839609999998856, 'num_data_request': 17, 'num_max_request_per_second': 1, 'avg_request_per_client': 9.5, 'num_min_size_per_request': 1, 'num_sys_request': 2, 'num_max_size_per_request': 1, 'num_min_request_per_client': 1}
2019-03-25 10:58:08+0800 [-] num_worker = 1
2019-03-25 10:58:08+0800 [-] device_map = []
2019-03-25 10:58:08+0800 [-] server_start_time = 2019-03-25 10:47:28.331322
2019-03-25 10:58:08+0800 [-] client = 140dba62-641d-486b-a605-7dabadacba09
2019-03-25 10:58:08+0800 [-] config_name = bert_config.json
2019-03-25 10:58:08+0800 [-] cors = *
2019-03-25 10:58:08+0800 [-] pooling_layer = [-2]
2019-03-25 10:58:08+0800 [-] port_out = 5556
2019-03-25 10:58:08+0800 [-] mask_cls_sep = False
2019-03-25 10:58:08+0800 [-] show_tokens_to_client = False
2019-03-25 10:58:08+0800 [-] prefetch_size = 10
2019-03-25 10:58:08+0800 [-] tensorflow_version = ['1', '12', '0']
2019-03-25 10:58:08+0800 [-] ckpt_name = bert_model.ckpt
2019-03-25 10:58:08+0800 [-] max_batch_size = 256
2019-03-25 10:58:08+0800 [-] gpu_memory_fraction = 0.5
2019-03-25 10:58:08+0800 [-] num_concurrent_socket = 8
2019-03-25 10:58:08+0800 [-] port = 5555
2019-03-25 10:58:08+0800 [-] ventilator <-> sink = tcp://127.0.0.1:52870
2019-03-25 10:58:08+0800 [-] model_dir = E:\chinese_L-12_H-768_A-12\chinese_L-12_H-768_A-12
2019-03-25 10:58:08+0800 [-] max_seq_len = 25
2019-03-25 10:58:08+0800 [-] fixed_embed_length = False
2019-03-25 10:58:08+0800 [-] graph_tmp_dir = None
2019-03-25 10:58:08+0800 [-] num_process = 2
2019-03-25 10:58:08+0800 [-] xla = False
2019-03-25 10:58:08+0800 [-] pyzmq_version = 18.0.1
2019-03-25 10:58:08+0800 [-] verbose = False
2019-03-25 10:58:08+0800 [-] server_current_time = 2019-03-25 10:58:08.595281
2019-03-25 10:58:08+0800 [-] bert model loaded
2019-03-25 10:58:08.707810: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-03-25 10:58:08.961376: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7085
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.96GiB
2019-03-25 10:58:08.961634: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-03-25 10:58:09.804785: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-03-25 10:58:09.804962: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2019-03-25 10:58:09.805046: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2019-03-25 10:58:09.805242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3072 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)
2019-03-25 10:58:12+0800 [-] Building prefix dict from the default dictionary ...
2019-03-25 10:58:12+0800 [-] Loading model from cache C:\Users\WEIZHE~1.ZHA\AppData\Local\Temp\jieba.cache
2019-03-25 10:58:13+0800 [-] Loading model cost 0.644 seconds.
2019-03-25 10:58:13+0800 [-] Prefix dict has been built succesfully.
2019-03-25 10:58:13+0800 [-] "127.0.0.1" - - [25/Mar/2019:02:58:12 +0000] "GET /parse?q=%E4%BB%8A%E5%A4%A9%E5%A4%A9%E6%B0%94%E6%80%8E%E4%B9%88%E6%A0%B7 HTTP/1.1" 200 1340 "-" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36"
2019-03-25 10:58:13+0800 [-] "127.0.0.1" - - [25/Mar/2019:02:58:12 +0000] "GET /favicon.ico HTTP/1.1" 404 233 "http://localhost:5000/parse?q=%E4%BB%8A%E5%A4%A9%E5%A4%A9%E6%B0%94%E6%80%8E%E4%B9%88%E6%A0%B7" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36"
2019-03-25 10:58:25+0800 [-] "127.0.0.1" - - [25/Mar/2019:02:58:24 +0000] "GET /parse?q=%E4%BB%8A%E5%A4%A9%E4%BD%A0%E5%90%83%E4%BA%86%E5%90%97 HTTP/1.1" 200 1334 "-" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36"
2019-03-25 10:58:39+0800 [-] "127.0.0.1" - - [25/Mar/2019:02:58:38 +0000] "GET /parse?q=%E5%A6%82%E4%BD%95%E5%8A%9E%E7%90%86%E7%A4%BE%E4%BF%9D%E5%8D%A1 HTTP/1.1" 200 1340 "-" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36"