I have trained a deep learning model using tensorflow and i saved it as an "cnn.h5" file using keras. Now I have 3 files that have "cnn.h5" in their name but all of them contain a different extension. The three files are:
cnn.h5.meta
cnn.h5.index
CNN.h5.data-00000-of-00001
now can anyone tell me which one of the above files is the saved model? i have to load that model in my GUI for testing.
Thanks.
This blog post explains saving and restoring you can refer to it for details a snippet of explanation for the type of saved files is as below.
When saving the model, you'll notice that it takes 4 types of files to save it:
".meta" files: containing the graph structure
".data" files: containing the values of variables
".index" files: identifying the checkpoint
Related
I have fine-tuned the ViTForImageClassification (transformer library by HuggingFace) and now I save the module in the default way that create a folder with the 2 following files:
config.json (should contain the structure of the model)
tf_model.h5 (contains the weights)
but the point is that the .h5 doesn’t contain the model but just the weights.
There is a way to get the whole model in one .h5 model? It’s possible in keras so I think that there is a solution for it!
When trying to store ckpt file, two ckpt.data files data-00000-of-00002 and data-00001-of-00002 are saved. I understand this is because different parts of model are saved in different shards but I wonder how to load the files to guarantee the completeness of model?
So, is simple as the title of the question, is there a way to save the weights after training like this
model.save_weights("path")
and then load them on another project only with
model = load_weights("path")
model.predict(x)
is it possible ?
yes. it is possible if you call the right path
for instance, you have this path
- project1/cool.py
- project2/another_cool.py
you train with cool.py and the model is saved inside project1's folder. then you want to load the model in another_cool.py
just call load_model function with path ../project1/weigh.h5
If you only want to save/load the weights, you can use
model.save_weights("path/to/my_model_weights.hdf5")
and then to reload (potentially in another python project / in another interpreter, you just have to update the path accordingly)
other_model.load_weights("path/to/my_model_weights.hdf5")
However both models should have the same architecture (instances of the same class), and Python/tensorflow/keras versions should be the same. See the doc for more info.
You can save both weights and architecture through model.save("path/to/my_model.hdf5")for saving on disk and keras.models.load_model("path/to/my_model.hdf5")for loading from disk (once again, the documentation should provide details).
Once loaded in memory, you can retrain your model, or use predict on it, predictions should be identical between projects
I have a PyTorch model class and its statedict with the weights.
I'd like to save the model directly with its weight in a .pt file using torch.save(model, PATH) but that simply saves the state dict again.
How do I save the model with the loaded_weights in it?
What I'm currently doing
lin_model = ModelClass(args)
lin_model.load_state_dict(torch.load('state_dict.pt'))
torch.save(lin_model, PATH)
I want the newly saved model to be a fully loaded pt file. Please help me here,thanks in advance.
According to the pytorch documentation here, when you use torch.save(model, PATH) it saves the entire model with the class. But here is the problem. It doesn't work every time. You see, the saved model is in pickle format, but the pickle file does not save the exact directory structure but just a path to the file containing the model class. So this saving method can break in various ways when used in other projects.
I'm developing a RNN for a project and I need to train it on a computer and be able to predict on another. The solution I found is to save the model into a .h5 file using the code below:
... # Train the data etc....
model.save("model.h5")
My problem is that I need to store some meta-data from my training dataset and pre-process and be able to load it together with the model. (e.g. name of dataset file, size of the dataset file, number of characters, etc...)
I don't want to store this information in a second file (e.g. a .txt file) because I would have to use two files. I don't want to use any additional library or framework for this task.
I was thinking (brainstorming) a code like this:
model.save("model.h5", metaData={'myVariableName': myVariable})
And to load would be:
myVariable = model.load("model.h5").getMetaData('myVariableName')
I know this is not possible in the current version and I already read Keras doc, but I couldn't find any efficient method to do that. Notice that what I'm asking is different from custom_object because want to save and load my own variables.
Is there a smarter approach to solve this problem?