DeepMind-Teaching-Machines-to-Read-and-Comprehend | Teaching Machines to Read and Comprehend | Machine Learning library

by thomasmesnard Python Version: Current License: MIT

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kandi X-RAY | DeepMind-Teaching-Machines-to-Read-and-Comprehend Summary

DeepMind-Teaching-Machines-to-Read-and-Comprehend is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. DeepMind-Teaching-Machines-to-Read-and-Comprehend has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However DeepMind-Teaching-Machines-to-Read-and-Comprehend build file is not available. You can download it from GitHub.

DeepMind : Teaching Machines to Read and Comprehend.

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DeepMind-Teaching-Machines-to-Read-and-Comprehend has a low active ecosystem.

It has 413 star(s) with 107 fork(s). There are 29 watchers for this library.

It had no major release in the last 6 months.

There are 7 open issues and 4 have been closed. On average issues are closed in 22 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DeepMind-Teaching-Machines-to-Read-and-Comprehend is current.

Quality

DeepMind-Teaching-Machines-to-Read-and-Comprehend has 0 bugs and 0 code smells.

Security

DeepMind-Teaching-Machines-to-Read-and-Comprehend has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

DeepMind-Teaching-Machines-to-Read-and-Comprehend code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DeepMind-Teaching-Machines-to-Read-and-Comprehend is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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DeepMind-Teaching-Machines-to-Read-and-Comprehend releases are not available. You will need to build from source code and install.

DeepMind-Teaching-Machines-to-Read-and-Comprehend has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

DeepMind-Teaching-Machines-to-Read-and-Comprehend saves you 235 person hours of effort in developing the same functionality from scratch.

It has 573 lines of code, 20 functions and 12 files.

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

Top functions reviewed by kandi - BETA

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

This function is called when the method is called
Load parameters from pickle file
Save the model parameters to file

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DeepMind-Teaching-Machines-to-Read-and-Comprehend Key Features

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DeepMind-Teaching-Machines-to-Read-and-Comprehend Examples and Code Snippets

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Community Discussions

Trending Discussions on DeepMind-Teaching-Machines-to-Read-and-Comprehend

What's the different between the state_keep_prob and output_keep_prob parameters of tf.contrib.rnn.DropoutWrapper

QUESTION

What's the different between the state_keep_prob and output_keep_prob parameters of tf.contrib.rnn.DropoutWrapper

Asked 2018-Apr-11 at 13:07

According to the API of tf.contrib.rnn.DropoutWrapper:

output_keep_prob: unit Tensor or float between 0 and 1, output keep probability; if it is constant and 1, no output dropout will be added.
state_keep_prob: unit Tensor or float between 0 and 1, output keep probability; if it is constant and 1, no output dropout will be added. State dropout is performed on the output states of the cell.

the description of these two parameters are almost the same, right?

I set output_keep_prob as default and state_keep_prob=0.2, the loss is always around 11.3 after 400 mini-batches' training, while I set output_keep_prob=0.2 and state_keep_prob as default, the loss returned by my model quickly down to around 6.0 after 20 mini-batches! It cost me 4 days to find this bug, really magic, can anyone explain the difference between these two parameters? Thanks a lot!

hyper parameters:

lr = 5E-4
batch_size = 32
state_size = 256
multirnn_depth = 2

Here is the dataset.

...

ANSWER

Answered 2017-Aug-14 at 15:22

state_keep_prob is the dropout added to the RNN's hidden states. The dropout added to the state of time step i will influence the calculation of states i+1, i+2, ... . As you have discovered, this propagation effect is often detrimental to the learning process.
output_keep_prob is the dropout added to the RNN's outputs, the dropout will have no effect on the calculation of the subsequent states.

Source https://stackoverflow.com/questions/45674857

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Vulnerabilities

No vulnerabilities reported

Install DeepMind-Teaching-Machines-to-Read-and-Comprehend

You can download it from GitHub.
You can use DeepMind-Teaching-Machines-to-Read-and-Comprehend 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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