def __init__(self, generate_num=80, temperature=0.4, top_p=0.9, censor=False, cut_actions=True): self.generate_num = generate_num self.default_gen_num = generate_num self.temp = temperature #self.top_k = top_k self.top_p = top_p self.censor = censor self.cut_actions = cut_actions self.model_name = "model_v5" self.model_dir = "generator/gpt2/models" self.checkpoint_path = os.path.join(self.model_dir, self.model_name) self.batch_size = 1 self.samples = 1 models_dir = os.path.expanduser(os.path.expandvars(self.model_dir)) self.enc = encoder.get_encoder(self.model_name, models_dir) config = tf.compat.v1.ConfigProto() config.gpu_options.allow_growth = True self.sess = tf.compat.v1.Session(config=config) self.context = tf.placeholder(tf.int32, [self.batch_size, None]) # np.random.seed(seed) # tf.set_random_seed(seed) self.gen_output() saver = tf.train.Saver() ckpt = tf.train.latest_checkpoint(os.path.join(models_dir, self.model_name)) saver.restore(self.sess, ckpt)
def __init__(self, generate_num=120, temperature=0.4, top_k=None, top_p=0.9, penalty=.2): self.generate_num = generate_num self.temp = temperature self.top_k = top_k self.top_p = top_p self.penalty = penalty self.model_name = "model_v5" self.model_dir = "generator/gpt2/models" self.checkpoint_path = os.path.join(self.model_dir, self.model_name) models_dir = os.path.expanduser(os.path.expandvars(self.model_dir)) self.batch_size = 1 self.samples = 1 self.enc = encoder.get_encoder(self.model_name, models_dir) hparams = model.default_hparams() with open(os.path.join(models_dir, self.model_name, 'hparams.json')) as f: hparams.override_from_dict(json.load(f)) config = tf.compat.v1.ConfigProto() config.gpu_options.allow_growth = True self.sess = tf.compat.v1.Session(config=config) self.context = tf.placeholder(tf.int32, [self.batch_size, None]) # np.random.seed(seed) # tf.set_random_seed(seed) self.output = sample.sample_sequence(hparams=hparams, length=self.generate_num, context=self.context, batch_size=self.batch_size, temperature=temperature, top_k=top_k, top_p=top_p, penalty=penalty) self.top_output = sample.sample_sequence(hparams=hparams, length=self.generate_num, context=self.context, batch_size=self.batch_size, temperature=0.1, top_k=top_k, top_p=top_p) saver = tf.train.Saver() ckpt = tf.train.latest_checkpoint( os.path.join(models_dir, self.model_name)) saver.restore(self.sess, ckpt)