g-softmax | Pytorch package for geometric softmax | Machine Learning library

by arthurmensch Python Version: Current License: No License

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kandi X-RAY | g-softmax Summary

g-softmax is a Python library typically used in Artificial Intelligence, Machine Learning, Pytorch applications. g-softmax has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Pytorch package for geometric softmax

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g-softmax has a low active ecosystem.

It has 9 star(s) with 2 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

g-softmax has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of g-softmax is current.

Quality

g-softmax has no bugs reported.

Security

g-softmax has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

g-softmax does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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g-softmax releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed g-softmax and discovered the below as its top functions. This is intended to give you an instant insight into g-softmax implemented functionality, and help decide if they suit your requirements.

Performs a single step
Add gradients
Evaluate the function
Number of parameters
Evaluate training
Cluster given positions and weights
Compute the pairwise distance between two matrices
Sum a matrix A
Run the loss function
Compute the entropy and potentials
Compute the pred from the given z
Computes the potential for the potential
Fetches a SML dataset
Forward loss function
Compute the forward objective function
Compute the potential for the potential
Computes the symmetric symmetric symmetric function
Plot a score function
Compute logits for given logits
Calculate the pairwise distance between two vectors
Perform log transformation
Draw n samples from an image file
Displays samples
Compute the symmetric symmetric symmetric function
Draw a cv2 image
Step the loss function
Make unidimensional alpha matrix
Load checkpoint from file
Computes the entropy and potential
Calculate the pred_from_vec

Get all kandi verified functions for this library.

g-softmax Key Features

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g-softmax Examples and Code Snippets

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Community Discussions

Trending Discussions on g-softmax

What is the difference between softmax and log-softmax?

Could not determine shape of numpy array in a loop containing transpose operation

QUESTION

What is the difference between softmax and log-softmax?

Asked 2019-Jul-08 at 23:24

The difference between these two functions that has been described in this pytorch post: What is the difference between log_softmax and softmax? is: exp(x_i) / exp(x).sum() and log softmax is: log(exp(x_i) / exp(x).sum()).

But for the Pytorch code below why am I getting different output:

...

ANSWER

Answered 2019-Jul-08 at 23:12

By default, torch.log provides the natural logarithm of the input, so the output of PyTorch is correct:

Source https://stackoverflow.com/questions/49236571

QUESTION

Could not determine shape of numpy array in a loop containing transpose operation

Asked 2018-Mar-12 at 11:55

I have been trying to create a small neural network to learn softmax function with an article from the following website: https://mlxai.github.io/2017/01/09/implementing-softmax-classifier-with-vectorized-operations.html

It works well for a single iteration. But, when I create a loop for training the network with updated weights, I get the following error: ValueError: operands could not be broadcast together with shapes (5,10) (1,5) (5,10). I have attached a screenshot of the output here.

Debugging this issue, I found out that np.max() returns array of shape (5,1) and (1,5) at different iterations even though the axis is being set to 1. Please help me in identifying what went wrong in the following code.

...

ANSWER

Answered 2018-Mar-12 at 11:55

In your first iteration, W is an instance of np.ndarray with shape (D, C). f inherits ndarray, so when you do np.max(f, axis = 1), it returns a an ndarray of shape (D,), which np.matrix() turns into shape (1, D) which is then transposed to (D, 1)

But on your following iterations, W is an instance of np.matrix (which it inherits from dW in W = W - lr*dW). f then inherits np.matrix, and np.max(f, axis = 1) returns a np.matrix of shape (D, 1), which passes through np.matrix() unphased and turns into shape (1, D) after .T

To fix this, make sure you don't mix np.ndarray with np.matrix. Either define everything as np.matrix from the start (i.e. W = np.matrix(np.random.rand(D,C))) or use keepdims to maintain your axes like:

Source https://stackoverflow.com/questions/49233403

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install g-softmax

You can download it from GitHub.
You can use g-softmax 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.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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