I have found this Git Hub code for intent classification. Could someone please tell me how to check the model accuracy of this?
https://colab.research.google.com/github/ShawonAshraf/nlu-jointbert-dl2021/blob/main/notebooks/nlu_jointbert_dl21.ipynb#scrollTo=Uae9vd77VT84
I have tried to add the joint_model.predict()
But not get the answer
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I am trying to implement TFLite model for food detection and segmentation. This is the model i chose suitable for my food images dataset: [https://tfhub.dev/s?deployment-format=lite&q=inception%20resnet%20v2].
I searched over google to understand how the images are required to be annotated, but only end up in confusion. I understand the dataset is converted to TFRecords and then fed to the pretrained model. But for training the model with custom dataset, does not it require an annotation file? I dont see any info about this on TF hub either.
Please can anyone help me on this!
The answer to your question is depends on what model do you plan to train.
In the case of a model for food detection and segmentation you do need annotations when training. If you do not provide the model with labeled training data as it is a supervised learning model it cannot learn from them.
If you were to train an autoencoder the data does not need to be annotated. Hope the keywords used in this answer help you out search for more information about the topic.
I am trying to understand the BERT weight calculation. Please suggest me some article which can help me to understand the internal workings of BERT. I have read articles from Medium.
https://towardsdatascience.com/deconstructing-bert-distilling-6-patterns-from-100-million-parameters-b49113672f77
https://towardsdatascience.com/deconstructing-bert-part-2-visualizing-the-inner-workings-of-attention-60a16d86b5c1
I am doing a small project to understand the Bert pretraining and fine-tuning from different sources. My idea is to calculate the weights of each token in their own sources and find avg of all weights to get a global model. Then this global model can be used to fine-tune in different sources.
How can I find these weights, and how can average these weights from multiple sources?
can I visualise it? Then how?
Also, note that I am trying to use Tensorflow version of the Bert implementation and planning to fine-tune for the NER task.
I'm trying to do domain adversarial training using gradient-reversal procedure. I have a deep-learning model architecture consisting of 5 dense layers. Now I want to extract the gradient, reverse them and then update the weights. I am not sure how to extract the gradients and finally how to add them to update the weights. I have gone through some example codes but I am still pretty doubtful regarding using Keras backend. Any help with some toy code or example with explanation is really appreciated.
Thank you,
I am new to neural networks so I tried my first neural network which is pretty close to one at keras learn page,given below:
https://github.com/aakarsh1011/Neural-Network/blob/master/MNSIT%20classification.ipynb
Kindlly look at the ending where I red a random image and tried to predict it which comes out as a bag, and when trained at epocs=5 it predicted it as a sandal.
Is something wrong with my code or labeling.
UPDATE - Being new to the field I didn't know the importance of epochs so I asked this question, I was afraid that I don't over-fit the model or train train too much. But there is no definite way to do this, it's all try and error. GOOD LUCK!
First of all, as far as I can see, your code is correct. Your model predicting the wrong item can be caused by the model not being trained for long enough. I would highly recommend you to set epochs=100 and you will be able to see the model's accuracy rise. You should generally always try to give your model as many epochs as possible for training. It will simply take some time. Try out some different numbers of epochs to find the one not taking too long, but still giving an acceptable result.
I am new to Tensorflow. I trained a model with 900 images of shoes. I put 20%(180 images) into test folder and 80%(720 images) into train folder. But after training, my trained model is detecting other objects also as shoes. Attached below is the screen shot of the prediction by my model.
Question1: Can anyone can help me please, where am i going wrong?
I am training this model on MAC Machine, and using faster_rcnn_inception_v2_coco_2018_01_28 model to train my model.
I follow the link to train model:
https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10/tree/d1c5b59803543e48362c27c48d704d4b0d92d135
Question 2: And more thing when i run this model on webcam it is very slow why?
Thanks in advance...
Screenshots
Please check this Image detecting wrong object as shoe
Please check this image detecting shoe