def main(_):
args = params_setup(model_num=0)
args1 = params_setup(model_num=1)
args = check_mion_ray(args)
args1 = check_mion_ray(args1)
print("[args]: ", args)
if args.mode == 'train':
train(args)
elif args.mode == 'test':
predict(args)
elif args.mode == 'chat':
chat(args)
elif args.mode == 'fight':
fight(args, args1)
def main(_):
args = params_setup()
print("[args]: ", args)
if args.mode == 'train':
train(args)
elif args.mode == 'test':
predict(args)
elif args.mode == 'chat':
chat(args)
return "ok"
@app.route('/', methods=['GET'])
def home():
return render_template('index.html')
@app.route('/privacy', methods=['GET'])
def privacy():
return render_template('privacy.html')
#---------------------------
# Start Server
#---------------------------
if __name__ == '__main__':
# check ssl files
if not os.path.exists('ssl/server.crt'):
print("SSL certificate not found! (should placed in ./ssl/server.crt)")
elif not os.path.exists('ssl/server.key'):
print("SSL key not found! (should placed in ./ssl/server.key)")
else:
# initialize model
args = params_setup()
chatbot = ChatBot(args, debug=False)
# start server
context = ('ssl/server.crt', 'ssl/server.key')
app.run(host='0.0.0.0', port=443, debug=False, ssl_context=context)
import numpy as np
from lib.config import params_setup
from lib.seq2seq_model import Seq2Seq
from lib.utils import read_testing_sequences, word_id_to_song_id, cal_scores
def config_setup():
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
return config
if __name__ == "__main__":
para = params_setup()
if para.debug == 1:
para.num_units = 2
para.num_layers = 2
para.batch_size = 2
para.embedding_size = 2
if para.mode == 'test':
para.batch_size = 1
with tf.Graph().as_default():
initializer = tf.random_uniform_initializer(-para.init_weight,
para.init_weight)
with tf.variable_scope('model', reuse=None, initializer=initializer):
model = Seq2Seq(para)
try:
return "ok"
@app.route('/', methods=['GET'])
def home():
return render_template('index.html')
@app.route('/privacy', methods=['GET'])
def privacy():
return render_template('privacy.html')
#---------------------------
# Start Server
#---------------------------
if __name__ == '__main__':
# check ssl files
if not os.path.exists('ssl/server.crt'):
print("SSL certificate not found! (should placed in ./ssl/server.crt)")
elif not os.path.exists('ssl/server.key'):
print("SSL key not found! (should placed in ./ssl/server.key)")
else:
# initialize model
args = params_setup()
chatbot = ChatBot(args, debug=False)
# start server
context = ('ssl/server.crt', 'ssl/server.key')
app.run(host='0.0.0.0', port=443, debug=False, ssl_context=context)
import os
import tensorflow as tf
from lib.config import params_setup
from lib.utils import print_parameters
from lib.model_utils import create_model_dir, load_weights, create_graph
from lib.setup import config_setup, logging_config_setup
from lib.pretrain import pretrain
from lib.rl import policy_gradient
from lib.test import test
if __name__ == "__main__":
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
PARA = params_setup()
create_model_dir(PARA)
logging_config_setup(PARA)
print_parameters(PARA)
GRAPH, MODEL = create_graph(PARA)
with tf.Session(config=config_setup(), graph=GRAPH) as sess:
sess.run(tf.global_variables_initializer())
load_weights(PARA, sess, MODEL)
COORD = tf.train.Coordinator()
THREADS = tf.train.start_queue_runners(sess=sess, coord=COORD)
try:
if PARA.mode == 'pretrain':
pretrain(PARA, sess, MODEL)
