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)