def __init__(self, model_path):
self.voca_size = 30522
self.hp = hyperparams.HPFAD()
self.model_dir = cpath.model_path
self.task = transformer_next_sent(self.hp, 2, self.voca_size, False)
self.sess = init_session()
self.sess.run(tf.global_variables_initializer())
self.load_model_white(model_path)
self.batch_size = 64
def __init__(self, model_path, num_classes):
self.voca_size = 30522
load_names = ['bert', "output_bias", "output_weights"]
self.hp = hyperparams.HPFAD()
self.model_dir = cpath.model_path
self.task = transformer_logit(self.hp, num_classes, self.voca_size,
False)
self.sess = init_session()
self.sess.run(tf.global_variables_initializer())
self.load_model_white(model_path, load_names)
self.batch_size = 64
def train_adhoc_fad():
hp = hyperparams.HPFAD()
hp.batch_size = 16
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_{}".format("FAD")
e_config.num_epoch = 4
e_config.save_interval = 10 * 60 # 60 minutes
e_config.load_names = ['bert'] #, 'reg_dense']
vocab_size = 30522
data_loader = data_sampler.DataLoaderFromFile(hp.batch_size, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
#load_id = ("Adhoc_I2", 'model-290')
e.train_adhoc2(e_config, data_loader, load_id)
def predict_adhoc512():
hp = hyperparams.HPFAD()
hp.batch_size = 16
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_J_{}".format("512")
e_config.num_epoch = 4
e_config.save_interval = 10 * 60 # 60 minutes
e_config.load_names = ['bert', 'reg_dense']
vocab_size = 30522
payload_path = os.path.join(cpath.data_path, "robust_payload",
"enc_payload_512.pickle")
task_idx = int(sys.argv[2])
print(task_idx)
load_id = ("Adhoc_J_512", 'model-6180')
e.predict_robust(e_config, vocab_size, load_id, payload_path, task_idx)