def __init__(self, host='0.0.0.0', port=8000, server='paste'): import bottle self.host = host self.port = port self.server = server self.graph = tf.get_default_graph() self.sess = K.get_session() self.set_session = K.set_session self.bottle = bottle
def __init__(self, host='0.0.0.0', port=8000, server='paste'): import tensorflow as tf from bert4keras.backend import K import bottle self.host = host self.port = port self.server = server self.graph = tf.get_default_graph() self.sess = K.get_session() self.set_session = K.set_session self.bottle = bottle
# bert4keras加载CDial-GPT import numpy as np from bert4keras.models import build_transformer_model from bert4keras.tokenizers import Tokenizer from bert4keras.snippets import AutoRegressiveDecoder from bert4keras.snippets import uniout import tensorflow as tf from bert4keras.backend import K from flask import Flask, request, render_template, send_file app = Flask(__name__) graph = tf.get_default_graph() sess = K.get_session() set_session = K.set_session config_path = r'GPT_large-tf\gpt_config.json' checkpoint_path = r'GPT_large-tf\gpt_model.ckpt' dict_path = r'GPT_large-tf\vocab.txt' tokenizer = Tokenizer(dict_path, do_lower_case=True) # 建立分词器 speakers = [ tokenizer.token_to_id('[speaker1]'), tokenizer.token_to_id('[speaker2]') ] model = build_transformer_model(config_path=config_path, checkpoint_path=checkpoint_path, model='GPT_OpenAI') # 建立模型,加载权重