kandi X-RAY | Gossip-Learning-Framework Summary
kandi X-RAY | Gossip-Learning-Framework Summary
This open source benchmarking framework allows you to build your own P2P learning algorithm and evaluate it in a simulated but realistic -- where you can model message delay, drop or churn -- networked environment. Moreover it contains the prototype implementations of some well-known machine learning algorithms like SVM and Logistic Regression.
Top functions reviewed by kandi - BETA
- Updates the weak learner
- Removes the weak learner at the specified index
- Store the week model
- Compute the alpha values for the given weak learner
- Update the vector
- Returns the distribution of the sparse vector
- Initializes the vector of class labels
- Initializes the neural network
- Initialize the tas
- Updates the model
- Performs the benchmark
- Run the simulation
- Returns the value of Gauss error function or cumulative distribution function
- Evaluate the indices of the nodes
- Evaluate and evaluate the indices for each node
- Update model
- Parses the given file
- Main method for testing
- Main entry point
- Main method for testing
- Executes the evaluation
- Updates the distribution for the given instance
- Starts the active thread
- Compute the errors in the model
- Updates the optimizer
- Performs evaluation
Gossip-Learning-Framework Key Features
Gossip-Learning-Framework Examples and Code Snippets
Trending Discussions on Machine Learning
I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.
I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.
I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition....
ANSWERAnswered 2022-Feb-17 at 10:47
You should try to export the model using torch.onnx. The page gives you an example that you can start with.
An alternative is to use TorchScript, but that requires torch libraries.
Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html
Just modifying a little your example to go over the errors I found
Notice that via tracing any if/elif/else, for, while will be unrolled
I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.
I'm using MNIST dataset....
ANSWERAnswered 2022-Jan-14 at 23:47
Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over
Flux.params(model) is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.
Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:
This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.Background
I would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.
Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.
After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?
An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image...
ANSWERAnswered 2021-Nov-24 at 13:26
What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of
I am trying to train a model using PyTorch. When beginning model training I get the following error message:
RuntimeError: CUDA out of memory. Tried to allocate 5.37 GiB (GPU 0; 7.79 GiB total capacity; 742.54 MiB already allocated; 5.13 GiB free; 792.00 MiB reserved in total by PyTorch)
I am wondering why this error is occurring. From the way I see it, I have 7.79 GiB total capacity. The numbers it is stating (742 MiB + 5.13 GiB + 792 MiB) do not add up to be greater than 7.79 GiB. When I check
nvidia-smi I see these processes running
ANSWERAnswered 2021-Nov-23 at 06:13
This is more of a comment, but worth pointing out.
The reason in general is indeed what talonmies commented, but you are summing up the numbers incorrectly. Let's see what happens when tensors are moved to GPU (I tried this on my PC with RTX2060 with 5.8G usable GPU memory in total):
Let's run the following python commands interactively:
I am a bit confusing with comparing best GridSearchCV model and baseline.
For example, we have classification problem.
As a baseline, we'll fit a model with default settings (let it be logistic regression):
ANSWERAnswered 2021-Nov-04 at 21:17
No, they aren't comparable.
Your baseline model used
X_train to fit the model. Then you're using the fitted model to score the
X_train sample. This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen.
The grid searched model is at a disadvantage because:
- It's working with less data since you have split the
- Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of
So your score for the grid search is going to be worse than your baseline.
Now you might ask, "so what's the point of
best_model.best_score_? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context.
So how should one go about conducting a fair comparison?
- Split your training data for both models.
I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.
but didn't fix it.
Thanks in advance....
ANSWERAnswered 2021-Aug-20 at 14:00
You should try this Google Notebook trouble shooting section about 524 errors : https://cloud.google.com/notebooks/docs/troubleshooting?hl=ja#opening_a_notebook_results_in_a_524_a_timeout_occurred_error
I am new to Machine Learning.
Having followed the steps in this simple Maching Learning using the Brain.js library, it beats my understanding why I keep getting the error message below:
I have double-checked my code multiple times. This is particularly frustrating as this is the very first exercise!
Kindly point out what I am missing here!
Find below my code:...
ANSWERAnswered 2021-Sep-29 at 22:47
Turns out its just documented incorrectly.
In reality the export from brain.js is this:
IF we are not sure about the nature of categorical features like whether they are nominal or ordinal, which encoding should we use? Ordinal-Encoding or One-Hot-Encoding? Is there a clearly defined rule on this topic?
I see a lot of people using Ordinal-Encoding on Categorical Data that doesn't have a Direction. Suppose a frequency table:...
ANSWERAnswered 2021-Sep-04 at 06:43
You're right. Just one thing to consider for choosing
OneHotEncoder is that does the order of data matter?
Most ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases e.g., for ordered categories such as:
quality = ["bad", "average", "good", "excellent"]or
shirt_size = ["large", "medium", "small"]
but it is obviously not the case for the:
color = ["white","orange","black","green"]
column (except for the cases you need to consider a spectrum, say from white to black. Note that in this case,
white category should be encoded as
black should be encoded as the highest number in your categories), or if you have some cases for example, say, categories 0 and 4 may be more similar than categories 0 and 1. To fix this issue, a common solution is to create one binary attribute per category (One-Hot encoding)
I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language
by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.
ANSWERAnswered 2021-Aug-10 at 07:39
Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.
Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.
If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.
I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.
Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?
ANSWERAnswered 2021-Aug-11 at 15:55
No vulnerabilities reported
getting the source: First you have to download the source code of the framework. Probably the easiest way to do that is cloning this git repository by typing git clone git://github.com/RobertOrmandi/Gossip-Learning-Framework.git. Additional possibilities are to download as [zip archive](https://github.com/RobertOrmandi/Gossip-Learning-Framework/zipball/master) or as [tar.gz archive](https://github.com/RobertOrmandi/Gossip-Learning-Framework/tarball/master).
building it: The building process is supported with ant. To create a jar you have to type ant in the root directory of the project. This will produce gossipLearning.jar in the bin directory of the project. (All of the libraries which are necessary for building or running the project are included in the lib directory of the project.)
running a predefined simulation: To run a simulation applying one of the predefined scenarios on the [Iris](http://archive.ics.uci.edu/ml/datasets/Iris) dataset you have to type the following code snippet: res/script/run.sh training_db evaluation_db 100 scenario result (assuming a standard UNIX environment with java and gnuplot installed).The parameters of the run.sh are pretty intuitive and you can find examples in the package. The first two parameters refer to the training and evaluation datasets, respectively, presented in [SVMLight format](http://svmlight.joachims.org/). You can use the res/db/iris_setosa_versicolor_train.dat and res/db/iris_setosa_versicolor_eval.dat files respectively. The third parameter defines the number of iterations. The fourth one describes the simulation environment. Basically this is a [Peersim](http://peersim.sourceforge.net/) configuration file template (configuration file with some variables that are instantiated based on the used training set). Here you can use the res/config/no_failure_applying_more_learners_voting10.txt configuration file. The results are generated in the res/results directory given in the fifth parameter (it has to be created before the call of run.sh). Make sure to delete previously generated results before you rerun the simulation! In the res directory of the project you can find additional training datasets (db subdirectory) and other configuration templates (config subdirectory).
understanding the results: The result graphs can be found in the res/results/ directory. It should be similar to [this](http://www.inf.u-szeged.hu/rgai/~ormandi/iris_setosa_versicolor.png) figure. Each curve belongs to a certain type of learning algorithm (see labels) and each point of the curves corresponds to a point in time (see label of x-axis). Each point shows the averaged 0-1 error over the different machine learning models stored by the nodes of the network measured on a separate (i.e. not known by the learning algorithm) evaluation set. As you can see, each line drops down after a certain point in time which means each algorithm converges.
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