CS294 | homework for CS294 Fall | Machine Learning library

by Observerspy Python Version: Current License: No License

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kandi X-RAY | CS294 Summary

CS294 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Numpy, Neural Network applications. CS294 has no vulnerabilities and it has low support. However CS294 has 2 bugs and it build file is not available. You can download it from GitHub.

homework for CS294 Fall 2017.

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Quality

Security

License

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Support

CS294 has a low active ecosystem.

It has 161 star(s) with 41 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 2 have been closed. On average issues are closed in 320 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CS294 is current.

Quality

CS294 has 2 bugs (2 blocker, 0 critical, 0 major, 0 minor) and 50 code smells.

Security

CS294 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

CS294 code analysis shows 0 unresolved vulnerabilities.

There are 7 security hotspots that need review.

License

CS294 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.

Reuse

CS294 releases are not available. You will need to build from source code and install.

CS294 has no build file. You will be need to create the build yourself to build the component from source.

CS294 saves you 1182 person hours of effort in developing the same functionality from scratch.

It has 2665 lines of code, 212 functions and 24 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed CS294 and discovered the below as its top functions. This is intended to give you an instant insight into CS294 implemented functionality, and help decide if they suit your requirements.

Train the model
Get a TensorFlow Session
Colorize a string
Configure output directory
Dump the log to a table
Loads a GaussianPolicy from a pickle file
Create a function from inputs
R Lrelu
Constructs an Adam learning optimizer
Encode a recent observation
Perform a single step
WNDense tensor
Build the graph
Plot data
Get a tf session
Reset the observation buffer
Dropout
Shuffle training data
Dump the contents of the log file
Given a list of outputs return the topology
Get all datasets from a given path
2D convolutional layer
Calculate the action given a state
Batch normalization
Run an environment
Reset the environment
Create a gym env

Get all kandi verified functions for this library.

CS294 Key Features

No Key Features are available at this moment for CS294.

CS294 Examples and Code Snippets

No Code Snippets are available at this moment for CS294.

Community Discussions

Trending Discussions on CS294

Implementing simple probabilistic model with negative log likelihood loss

QUESTION

Implementing simple probabilistic model with negative log likelihood loss

Asked 2019-Nov-26 at 08:09

First a quick disclaimer would be that I posted this question on Reddit, in the Deep Learning and Learning Machine Learning first, but I thought I might also request your expertise here too. Without further ado:

I am currently challenging myself on this year Deep Unsupervised Learning Course of Berkeley University and although I just started the warmup exercise of week 1, I am already having 'technical' difficulties.

The exercise in question is the "1. Warmup" in the following document: Week 1 Exercises. (My apologies as I am not familiar enough with Reddit formating to seemlessly include images.

In my understanding, we have a variable x which can take values from 1..100 which a specific probability of being sampled ( defined in sample_data() function). The task is therefore to fit a vector of parameters theta which is passed to a softmax function, and is supposed to give the likelihood of a specific element x_i to be sampled. Namely, theta_1 should the parameter which "bumps up" the soft-max value corresponding to the variable x = 1 and so on.

Using Tensorflow, I think I was able to create such a model, but when it comes to training, I believe I am missing a crucial point as the program cannot compute gradients with respect to the theta parameters.

I would like to know if am not misunderstanding the task, and if there is any better method to achieve the result of the exercise.

Here is the code, where the failing par is located from the # Computing gradients.

...

ANSWER

Answered 2019-Nov-26 at 08:09

Pretty sure nobody is gonna see this, but I thought I might as well bring some closure to this.

First of all, I calculated the gradients by directly deriving its expression from the negative log likelihood of the soft-max value, thus dropping the Tensorflow framework by the same occasion.

Although the results are a little bit under my expectations, the program was able to fit the model to a distribution somewhat similar to the empirical distribution of the sampled data. I guess this is due to the fact that just a 1 dimensional theta parameter vector is not enough to fully model the real data distribution, as well as the finite amount of sampled data.

An updated version of the code:

Source https://stackoverflow.com/questions/59023033

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

Vulnerabilities

No vulnerabilities reported

Install CS294

You can download it from GitHub.
You can use CS294 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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