When saving a Lambda layer, the arguments passed in will also be saved. In this case, it contains two tf.Tensors. It seems that Keras does not support serializing tf.Tensor in the model config right now. However, numpy arrays can be serialized without problem. So instead of passing tf.Tensor in arguments, you can pass in numpy … Read more
The problem you’ve got here is that, by using -mcProfile, the module __main__ is cProfile (the actual entry point of the code), not your script. cProfile tries to fix this by ensuring that when your script runs, it sees __name__ as “__main__”, so it knows it’s being run as a script, not imported as a … Read more
Thanks to John Montgomery I think I have found a solution, and I think it has less overhead than converting every number in possibly huge arrays to strings: I can create a byte-view of the arrays and use these to update the hash. And somehow this seems to give the same digest as directly updating … Read more
It is because you are setting Test.A as a class attribute instead of an instance attribute. Really what is happening is that with the test1.py, the object being read back from the pickle file is the same as test2.py, but its using the class in memory where you had originally assigned x.A. When your data … Read more
The pickle module implements a fundamental, but powerful algorithm for serializing and de-serializing a Python object structure. Pickling – is the process whereby a Python object hierarchy is converted into a byte stream, and Unpickling – is the inverse operation, whereby a byte stream is converted back into an object hierarchy. Pickling (and unpickling) is … Read more
Use the latest protocol that supports the lowest Python version you want to support reading the data. Newer protocol versions support new language features and include optimisations. From the pickle module data format documentation: There are currently 6 different protocols which can be used for pickling. The higher the protocol used, the more recent the … Read more
For pandas users who saved a dataframe to a pickle file with protocol 5 in python 3.8 and need to load it into python 3.6 which only supports protocol 4 (I’m looking at you google colab): !pip3 install pickle5 import pickle5 as pickle with open(path_to_protocol5, “rb”) as fh: data = pickle.load(fh) Could also save into … Read more
joblib is usually significantly faster on large numpy arrays because it has a special handling for the array buffers of the numpy datastructure. To find about the implementation details you can have a look at the source code. It can also compress that data on the fly while pickling using zlib or lz4. joblib also … Read more
Pickling will serialize your list (convert it, and it’s entries to a unique byte string), so you can save it to disk. You can also use pickle to retrieve your original list, loading from the saved file. So, first build a list, then use pickle.dump to send it to a file… Python 3.4.1 (default, May … Read more
Some uses that I have come across: 1) saving a program’s state data to disk so that it can carry on where it left off when restarted (persistence) 2) sending python data over a TCP connection in a multi-core or distributed system (marshalling) 3) storing python objects in a database 4) converting an arbitrary python … Read more