maxl | Supervised Generalisation with Meta Auxiliary Learning | Machine Learning library
kandi X-RAY | maxl Summary
kandi X-RAY | maxl Summary
This repository contains the source code to support the paper: Self-Supervised Generalisation with Meta Auxiliary Learning, introduced by Shikun Liu, Andrew J. Davison and Edward Johns.
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- Forward the layer
- Layer layer layer
- Computes dense layer
- Compute unrolled unrolled multi - task
- Compute the Hessian
- Download the file
- Check integrity of training and test files
- Runs the prediction on the block
- Apply softmax to x
- Fit the model to the model
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maxl Examples and Code Snippets
Community Discussions
Trending Discussions on maxl
QUESTION
how to sort descending on Arraylist ? i don't know how to do it
my code ArrayList> data = new ArrayList>();
...ANSWER
Answered 2021-May-07 at 02:03You would do something like this:
QUESTION
I keep getting an error while trying to runserver(python manage.py runserver) The error is a nameError and says that Post is not defined.
this is from models.py
...ANSWER
Answered 2021-Mar-15 at 07:46The error is raised from the model since you are accessing the Post class in the class variable definitions ie. posts = Post.objects.all(). Removing this would resolve the NameError
QUESTION
I am using sklearn's GPR library, but occasionally run into this annoying warning:
...ANSWER
Answered 2020-Jun-15 at 01:45You want to extend and/or modify the behavior of an existing Python object, which sounds like a good use case for inheritance.
A solution could be to inherit from the scikit-learn implementation, and ensure that the usual optimizer is called with the arguments you'd like. Here's a sketch, but note that this is not tested.
QUESTION
Using the SBV library, I'm trying to satisfy conditions on a symbolic list of states:
...ANSWER
Answered 2020-Oct-27 at 15:50Solvers for the sequence logic, while quite versatile, are notoriously slow. For this particular problem, I'd recommend using regular boolean logic, which will perform much better. Here's how I'd code your problem:
QUESTION
I am trying to write constraints using ln and exp function, yet I received an error that Cplex can't extract the expression.
...ANSWER
Answered 2020-Oct-17 at 12:37You may use exp and log if you rely on Constraint Programming within CPLEX:
QUESTION
We are running the Bayesian Optimizer for hyper parameter tuning. By the way, I get this error. The same error occurs even if you experiment with changing all of the parameter ranges. Please answer what should be done.
...ANSWER
Answered 2020-Oct-06 at 18:06I know nothing of this Bayesian stuff, but in box bounded optimization it is a no-no to provide lower bounds greater than upper bounds:
‘gamma': (1, 0.01),
Not sure if this is your issue but it took me all of 7 seconds to see it.
QUESTION
I went through the OpenACC 2.6 supported features with PGI compilers, and encountered an issue with the memory management between CPU and GPU.
The following Fortran code is a modified version from the official document:
...ANSWER
Answered 2020-Sep-10 at 23:09The problem is in your initialize routine:
QUESTION
Here's the error code.
...ANSWER
Answered 2020-Jul-25 at 12:45I have managed to solve this myself. However, I will address the issue directly to the OpenCV developers so they can give me with the answer to it.
QUESTION
I have a function findMaxEval which I invoke in a following way:
eMax0,var0=findMaxEval(np.diag(eVal0),q,bWidth=.01)
where np.diag(eVal0) is an ndarray of shape (1000,), q is a number (10).
findMaxEval has the following definition:
ANSWER
Answered 2020-Jun-30 at 00:34Updated 6/29 ... I got it to run this way, which is strange because it is the same thing, must be a bug in the library or casting explicitly like this gets it into the precise format desired:
QUESTION
I have a custom neural network that I am training on data and seek to constrain the network's output values to always be between two arbitrary constants: [lower_bound,upper_bound]. Are there any best practices for encoding this constraint in a loss function?
Below I have written a minimal working example whereby I construct and train a neural network on generated data. Additionally, I place the arbitrary constraint that the output should be between [lower_bound,upper_bound] = [-0.5,0.75] in the loss function being optimized. But I attempt it using a relatively crude method of finding all instances where predicted values exceed the bounds, and then simply making the loss function for these terms a large value (and otherwise zero if the predicted value is within the given bounds):
ANSWER
Answered 2020-Jun-25 at 08:34The best way (IMHO) to do such a thing is to enforce it via the output activation function. We can use a tf.nn.sigmoid as a basis, which is bounded between [0, 1], and slightly shift and scale it.
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You can use maxl like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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