def train(cls, user_id):
#默认加载data下的数据
print('user_id', user_id)
FLAGS = tf.flags.FLAGS
cls.my_data = dh.data_help(user_id=user_id)
#cls.my_data = dh.data_help()
checkpoint_file = tf.train.latest_checkpoint(
os.path.join(cls.my_data.file_path, 'runs', 'checkpoints'))
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
cls.sess = tf.Session(config=session_conf)
with cls.sess.as_default():
saver = tf.train.import_meta_graph(
"{}.meta".format(checkpoint_file))
saver.restore(cls.sess, checkpoint_file)
cls.input_x = graph.get_operation_by_name("input_x").outputs[0]
cls.dropout_keep_prob = graph.get_operation_by_name(
"dropout_keep_prob").outputs[0]
cls.predictions = graph.get_operation_by_name(
"output/predictions").outputs[0]
cls.probs = graph.get_operation_by_name(
"output/probs").outputs[0]
"Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False,
"Log placement of ops on devices")
parser = argparse.ArgumentParser()
parser.add_argument('-u',
'--user_id',
help='mysql user-id',
type=int,
default='2')
args = parser.parse_args()
FLAGS = tf.flags.FLAGS
print("user_id:%d" % (args.user_id))
my_data = data_help.data_help(user_id=args.user_id)
x_train, y_train, x_dev, y_dev = my_data.get_test_train()
#将测试数据写成pickle数据
my_data.write_to_pickle("test_data.pickle", x_dev, y_dev)
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
cnn = TextCNN(sequence_length=x_train.shape[1],
num_classes=y_train.shape[1],
vocab_size=my_data.Vocab_Size,
embedding_size=FLAGS.embedding_dim,
tf.flags.DEFINE_boolean("eval_train", True, "Evaluate on all training data")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True,
"Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False,
"Log placement of ops on devices")
FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
my_data = dh.data_help()
# CHANGE THIS: Load data. Load your own data here
if FLAGS.eval_train:
x_test, y_test = my_data.read_from_pickle("test_data.pickle")
y_test = np.argmax(y_test, axis=1)
else:
x_raw = ["a masterpiece four years in the making", "everything is off."]
y_test = [1, 0]
print("\nEvaluating...\n")
# Evaluation
# ==================================================
checkpoint_file = tf.train.latest_checkpoint(
os.path.join(my_data.file_path, 'runs', 'checkpoints'))
graph = tf.Graph()
tf.flags.DEFINE_boolean("log_device_placement", False,
"Log placement of ops on devices")
FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
Parameters = ''
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
temp = "{}={}".format(attr.upper(), value)
Parameters = temp + ' ' + Parameters
print(temp)
print("")
# Load data
print("Loading data...")
my_data = data_help.data_help()
y = my_data.build_labels()
x, ans, subject = my_data.build_vocab()
x_train, y_train, x_dev, y_dev = my_data.get_test_train(x, y)
#将测试数据写成pickle数据
my_data.write_to_pickle("test_data.pickle", x_dev, y_dev)
print(x_train.shape, x_dev.shape)
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
import json
import xlrd
import data_help
s = requests
data_lookup={"method":'lookup','id':1,'jsonrpc':2.0,'params':{'user_id':2}}
data_chat={"method":'chat','id':1,'jsonrpc':2.0,'params':{'user_id':2,"quest":"银联二维码支付怎么用"}}
data_retrain={"method":'retrain','id':1,'jsonrpc':2.0,'params':{'user_id':2}}
right=0
total=0
def send_json(quest):
data_chat={"method":'chat','id':1,'jsonrpc':2.0,'params':{'user_id':2,"quest":quest}}
r = s.post('http://127.0.0.1:8000/deep_chat', json.dumps(data_chat))
r.encoding = 'utf-8'
ans = eval(r.text)
return ans['result']['answer']
my_data=data_help.data_help(bProcessData=False,user_id=2)
for key,values in my_data.temp.items():
for value in values:
total+=1
if(values.index(value)==0):
right_answer=send_json(value)
else:
if right_answer == send_json(value):
right+=1
print("%d/%d"%(right,total))
