I am working with VLFeat and I have code that trains a linear SVM on my dataset. I would like to save the SVM to a file somehow so that I can load it up later and test it on several other datasets. What is the best way to do this?
edit: I am using the C++ API. Is it enough to save the model by using vl_svm_get_model? Would a simple serialization of the bytes of the svm struct work?
I am not using this approach however it should be sufficient to save the model and bias terms to a file for later testing
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I'm training a neural network using keras but I'm not sure how to feed the training data into the model in the way that I want.
My training data set is effectively infinite, I have some code to generate training examples as needed, so I just want to pipe a continuous stream of novel data into the network. keras seems to want me to specify my entire dataset in advance by creating a numpy array with everything in it, but this obviously wont work with my approach.
I've experimented with creating a generator class based on keras.utils.Sequence which seems like a better fit, but it still requires me to specify a length via the __len__ method which makes me think it will only create that many examples before recycling them. Can someone suggest a better approach?
I'm deploying a deep learning model and saved the keras model as .h5 file. I think complex model will make it big in size and hence slow interaction at the server, but is there a way other than reducing the layers in the model that I can do? Is there a sort of compressing the .h5 file in order to load it faster for the server?
Thank you
There is a way to do that.
What you are looking for is called quantization.
Not necessarily reducing the layers which is equivalent to model-pruning, quantization reduces both the size and the latency of the model by modifying the precision of the weights (or even activations in some cases).
For more detailed information, read this page on the official TensorFlow documentation: https://www.tensorflow.org/lite/performance/post_training_quantization
Are there ways to save a model after training and sharing just the model with others? Like a regular script? Since the network is a collection of float matrices, is it possible to just extract these trained weights and run it on new data to make predictions, instead of requiring the users to install these frameworks too? I am new to these frameworks and will make any clarifications as needed.
PyTorch: As explained in this post, you can save a model's parameters as a dictionary, or load a dictionary to set your model's parameters.
You can also save/load a PyTorch model as an object.
Both procedures require the user to have at least one tensor computation framework installed, e.g. for efficient matrix multiplication.
from Tensorflow's documentation, there seems to be a large array of options for "running", serving, testing, and predicting using a Tensorflow model. I've made a model very similar to MNIST, where it outputs a distribution from an image. For a beginner, what would be the easiest way to take one or a few images, and send them through the model, getting an output prediction? It is mostly for experimentation purposes. Sorry if this is too redundant, but all my research has led me to so many different ways of doing this and the documentation doesn't really give any info on the pros and cons of the different methods. Thanks
I guess you are using placeholders for your model input and then using feed_dict to feed values into your model.
If that's the case the simplest way would be after you have a trained model you save it using tf.saver. Then you can have a test script where you restore your model and then sess.run on your output variable with a feed_dict of whatever you want your input to be.
I have a Torch Model which is trained on a large scale dataset (Places Dataset) and it's authors uploaded it on github, i am working on a similar project and i want to make use of it and use it's trained weights instead of use the large dataset to train it and save time and efforts, it is possible ? how can i know the only the trained filters weights? i don't want to copy the code, i only want to make use of it and save time and efforts.
NOTE: I use Tensoflow in my implementation.