I have trained a U-Net model in keras with images in input. The model generates image outputs. Now I have to extract the output from an intermediate layer using the trained weights. I tried creating a new model with the desired intermediate layer as output and loading the saved weights. But then the trained weights can not be loaded as the layers don't match. How to generate the output from an intermediate layer using the saved trained weights?
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I am building a CNN using keras and I know that I can save the weights using model.save_weights() but how can I save the biases of the neural network? Am I missing something or is it that Keras just doesn't provide biases?
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 trained a LeNet architecture on a first dataset. I want to train a VGG architecture on an other dataset by initializing the weights of VGG with weights obtained from LeNet.
All initialization functions in keras are predefined and I do not find how to customize them. For example :
keras.initializers.Zeros()
Any idea how I can set the weights?
https://keras.io/layers/about-keras-layers/
According to the Keras documentation above:
layer.set_weights(weights) sets the weights of the layer from a list of Numpy arrays
layer.get_weights() returns the weights of the layer as a list of Numpy arrays
So, you can do this as follows:
model = Sequential()
model.add(Dense(32))
... building the model's layers ...
# access any nth layer by calling model.layers[n]
model.layers[0].set_weights( your_weights_here )
Of course, you'll need to make sure you are setting the weights of each layer to the appropriate shape they should be.
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).
I am using Keras newsgroup example code for text classification. I have saved the trained model using the h5py library. Will the embedding layer also get saved or should I write some extra code when loading the model to use the embedding layer?
Embedding layer is part of the model so it will be saved with the model. Check out this on saving the model.
Also one important addition, the Keras Embedding layer will be initialized with the random values at the very start of the training process, and the parameters will be learned in the training phase.