I have trained a keras model and saved it to later make predictions. However, I loaded the saved model using:
from keras.models import load_model
#Restore saved keras model
restored_keras_model = load_model("C:/*******/saved_model.hdf5")
Now I would like to save an image of the loaded model so I can visualize it before using it before making predictions.
Is there a way of doing this in keras or is the use of another library required?
Yes, in addition to doing a restored_keras_model.summary(), you can save the model architecture as a png file using the plot_model API.
from keras.utils import plot_model
plot_model(restored_keras_model, to_file='model.png')
https://keras.io/visualization/#model-visualization
Related
I trained a BERTopic model on a GPU, and now for visualization purposes I want to load it on a CPU.
But when I tried to do that I got:
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
When I tried to use the suggested fix I got the same problem?
Saw some fix that suggests to save the model without its embeddings model, but don't want to retrain an resave unless its the last option, and would also love if someone could explain what's this embedding model and what's going on under the hood.
topic_model = torch.load(args.model, map_location=torch.device('cpu'))
When you want to save the BERTopic model without the embedding model, you can run the following:
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data']
# Train the model
embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
topic_model = BERTopic(embedding_model=embedding_model)
topics, probs = topic_model.fit_transform(docs)
# Save the model without the embedding model
topic_model.save("my_model", save_embedding_model=False)
This should prevent any issues with GPU/CPU if you are not using any of the cuML sub-models in BERTopic.
Saw some fix that suggests to save the model without its embeddings model, but don't want to retrain an resave unless its the last option, and would also love if someone could explain what's this embedding model and what's going on under the hood.
The embedding model is typically a pre-trained model that actually is not learning from the input data. There are options to make it learn during training but that requires a custom component in BERTopic. In other words, when you use a pre-trained model, it is no problem removing that pre-trained model when saving the topic model as there would be no need to re-train the model.
In other words, we would first save our topic model in our GPU environment without the embedding model:
topic_model.save("my_model", save_embedding_model=False)
Then, we load in our saved BERTopic model in our CPU environment and then pass the pre-trained embedding model:
from sentence_transformers import SentenceTransformer
embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
topic_model = BERTopic.load("my_model", embedding_model=embedding_model )
You can learn more about the role of the embedding model here.
Is there a way to save a Keras model (using pickle, joblib, model.to_json() or tf.keras.models.save_model) that has been incorporated into an sklearn or imblearn pipeline via a scikit-learn wrapper such as KerasClassifier?
After saving a Keras model, where is it saved? Is it saved locally? The below code is saving a model.
from keras.models import load_model
model.save('my_model.h5') # creates a HDF5 file 'my_model.h5
model = load_model('my_model.h5')
After saving the model can I use the load model again in another jupyter ipynb file?
When we do model.save, it will save model architecture and weight at the same time. It will save as .h5 locally and we can load it in another environment (local notebook, kaggle notebook, colab, GCP, etc). Using load_model will load fully model (arch + weight).
Please, read the official blog, you will find plenty of examples. https://www.tensorflow.org/guide/keras/save_and_serialize
I have a pre-trained Keras model with ".h5" and ".json" file. I want to know the architecture of the model used and the names of the layers. Is there anyway I can do that?
I am new to this so I don't really know where to start
I am expecting logs that you get when you load a tensorflow model.
Sure, first load the model and then produce a summary of the model:
from keras.models import load_model
model = load_model('your_model.hdf5')
model.summary()
The summary will contain layer names and input/output shapes.
I am new to machine learning technology and I have a simple question. I have trained a network on the fashion MNIST and saved it, and then I would like to import the saved model for further processing instead of training the network each time I use the code. Can anyone help?
I have used the import function, but it always gives me an error
model.save("model_1.h5py") # save the model
model.import("model_2.h5py") # when I try to import the #model alwys gives me a valid synthax