I have a pre-trained model file "model.h5". I am trying to train the model again(fine-tuning) using small dataset. How to do that in Keras?
Just taking,
model = load_model('Model.h5)
model = load_wights('Model.h5) are both giving bad results.
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I am using a transformer model that was extended from huggingface (DNABERT). This is a pretrained classification model whose output I would like to convert to regression, then fine-tune that model on my own data. I imagine this process would be roughly the same for any BERT-based huggingface classification model. How would I go about doing this?
i would like to know when people say pretrained bert model, is it only the final classification neural network is trained
Or
Is there any update inside transformer through back propagation along with classification neural network
During pre-training, there is a complete training if the model (updation of weights). Moreover, BERT is trained on Masked Language Model objective and not classification objective.
In pre-training, you usually train a model with huge amount of generic data. Thus, it has to be fine-tuned with the task-specific data and task-specific objective.
So, if your task is classification on a dataset X. You fine-tune BERT accordingly. And now, you will be adding a task-specific layer (classification layer, in BERT they have used dense layer over [CLS] token). While fine-tuning, you update the pre-trained model weights as well as the new task-specific layer.
How do I use a pre-trained BERT model like bert-base-uncased as weights in the Embedding layer in Keras?
Currently, I am generating word embddings using BERT model and it takes a lot of time. And I am assigning those weights like in the cide shown below
model.add(Embedding(307200, 1536, input_length=1536, weights=[embeddings]))
I searched on internet but the method is given in PyTorch. I need to do it in Keras. Please help.
I'm using faster_rcnn_resnet50 to train a model which will detect corrosions in images and I want to train a model from scratch instead of using transfer learning.
I don't know if this right but the reason I want to do this is that the already existing weights (which are trained on COCO) will affect my model trained on corrosion images.
One way I would like to do this is randomize or unfreeze the weights of the feature extractor on the resnet50 and then train the model on my images.
but there's no function or an option in the resnet50 config file to randomize or unfreeze weights.
I've made a new labelmap with a single label and tried it with transfer learning. It's working but I would like to have a model is trained just on my images and the previous weights shouldn't affect my predictions.
This is the first time I'm working with object detection and transfer learning. Will the weights of the pre-trained model on COCO affect my model which is trained on custom images of corrosion? How do you use tensorflow object-detection API without transfer learning?
I built a LSTM model for text classification using Keras. Now I have new data to be trained. instead of appending to the original data and retrain the model, I thought of training the data using the model weights. i.e. making the weights to get trained with the new data.
However, irrespective of the volume i train, the model is not predicting the correct classification (even if i give the same sentence for prediction). What could be the reason?
Kindly help me.
Are you using the following to save the trained model?
model.save('model.h5')
model.save_weights('model_weights.h5')
And the following to load it?
from keras.models import load_model
model = load_model('model.h5') # Load the architecture
model = model.load_weights('model_weights.h5') # Set the weights
# train on new data
model.compile...
model.fit...
The model loaded is the exact same as the model being saved here. If you are doing this, then there must be something different in the data (in comparison with what it is trained on).