anhhaibkhn
commented
4 years ago I managed to get through the above error, by adding "self" to this line
def make_elmo_embedding(self,x) in the Class elmo_model
then after that, inside that Class too, I try to return the model for running function Summary() in "helper" but, it shows different error now, May I ask how to set the training process to not use "Elmo" and try out with other models first ?
Also, would it be possible to upgrade the scripts for tensorflow 2.x ?
Thank you for reading my message.
Model: "Elmo_network"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, None) 0
_________________________________________________________________
lambda_1 (Lambda) (None, None, 1024) 0
_________________________________________________________________
bidirectional_1 (Bidirection (None, None, 256) 1180672
_________________________________________________________________
bidirectional_2 (Bidirection (None, None, 256) 394240
_________________________________________________________________
global_max_pooling1d_1 (Glob (None, 256) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 256) 0
_________________________________________________________________
dense_1 (Dense) (None, 64) 16448
_________________________________________________________________
dense_2 (Dense) (None, 1) 65
=================================================================
Total params: 1,591,425
Trainable params: 1,591,425
Non-trainable params: 0
_________________________________________________________________
C:\Users\nguye\Anaconda3\envs\project_env\lib\site-packages\keras\callbacks\tensorboard_v2.py:92: UserWarning: The TensorBoard callback `batch_size` argument (for histogram computation) is deprecated with TensorFlow 2.0. It will be ignored.
warnings.warn('The TensorBoard callback `batch_size` argument '
C:\Users\nguye\Anaconda3\envs\project_env\lib\site-packages\keras\callbacks\tensorboard_v2.py:97: UserWarning: The TensorBoard callback does not support gradients display when using TensorFlow 2.0. The `write_grads` argument is ignored.
warnings.warn('The TensorBoard callback does not support '
Train on 397 samples, validate on 100 samples
Traceback (most recent call last):
File "main.py", line 35, in <module>
helper.exec()
File "E:\SecureCodeWithNLP\Projects\Function-level-Vulnerability-Detection\src\helper.py", line 275, in exec
class_weight = class_weights)
File "C:\Users\nguye\Anaconda3\envs\project_env\lib\site-packages\keras\engine\training.py", line 1239, in fit
validation_freq=validation_freq)
File "C:\Users\nguye\Anaconda3\envs\project_env\lib\site-packages\keras\engine\training_arrays.py", line 119, in fit_loop
callbacks.set_model(callback_model)
File "C:\Users\nguye\Anaconda3\envs\project_env\lib\site-packages\keras\callbacks\callbacks.py", line 68, in set_model
callback.set_model(model)
File "C:\Users\nguye\Anaconda3\envs\project_env\lib\site-packages\keras\callbacks\tensorboard_v2.py", line 116, in set_model
super(TensorBoard, self).set_model(model)
File "C:\Users\nguye\Anaconda3\envs\project_env\lib\site-packages\tensorflow_core\python\keras\callbacks.py", line 1532, in set_model
self.log_dir, self.model._get_distribution_strategy()) # pylint: disable=protected-access
AttributeError: 'Model' object has no attribute '_get_distribution_strategy'
DanielLin1986
cybercodeintelligence
Dear Sir, Great work! Thank you for sharing the project. I have been following your work for a while, it would be great to learn to extend this benchmark for other neural networks. Also, I ran into a bit of trouble when running step 1, the log is posted as the following part. After training the Word2Vec, it moves to Elmo model and somehow stuck there. Could you suggest what could be mistaken here?