tf-seq2seq | Sequence to sequence learning using TensorFlow | Translation library

by jayparks Python Version: Current License: No License

X-Ray Key Features Code Snippets(1)Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | tf-seq2seq Summary

tf-seq2seq is a Python library typically used in Utilities, Translation, Deep Learning, Tensorflow, Neural Network applications. tf-seq2seq has no bugs, it has no vulnerabilities and it has low support. However tf-seq2seq build file is not available. You can download it from GitHub.

Sequence to sequence learning using TensorFlow.

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Quality

Security

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Support

tf-seq2seq has a low active ecosystem.

It has 388 star(s) with 116 fork(s). There are 25 watchers for this library.

It had no major release in the last 6 months.

There are 13 open issues and 8 have been closed. On average issues are closed in 28 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tf-seq2seq is current.

Quality

tf-seq2seq has 0 bugs and 46 code smells.

Security

tf-seq2seq has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tf-seq2seq code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tf-seq2seq 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

tf-seq2seq releases are not available. You will need to build from source code and install.

tf-seq2seq 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.

tf-seq2seq saves you 568 person hours of effort in developing the same functionality from scratch.

It has 1326 lines of code, 63 functions and 16 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed tf-seq2seq and discovered the below as its top functions. This is intended to give you an instant insight into tf-seq2seq implemented functionality, and help decide if they suit your requirements.

Builds the model
Build a single cell
Build the decoder cell
Builds the decoder and attention layer
Segment a sentence
Return a set of pair pairs from a word
Encode a string using bpe_codes
Train the model
Check that the inputs are valid
Load the inverse dictionary
Load a dictionary from file
Calculate the F1 score
Prune stats based on a given threshold
Runs the prediction
Extract n - grams from a string
Calculate pairwise pairs
Replaces the pair of pairs in the vocabulary
Evaluate the model
Create argument parser
Calculates the correct and total number of comparisons
Get the vocabulary
Calculate statistics for a given pair

Get all kandi verified functions for this library.

tf-seq2seq Key Features

No Key Features are available at this moment for tf-seq2seq.

tf-seq2seq Examples and Code Snippets

Create a tf . seq2seq .

python

Lines of Code : 39

License : Permissive (MIT License)

Copy

def _create_loss(self):
        print('Creating loss... \nIt might take a couple of minutes depending on how many buckets you have.')
        start = time.time()
        def _seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
            setattr(t

Community Discussions

Trending Discussions on tf-seq2seq

How to properly setup an RNN in Keras for sequence to sequence modelling?

How to get value of a tensor from a Tensorflow Mode

tensorflow: CUDA_ERROR_OUT_OF_MEMORY always happen

QUESTION

How to properly setup an RNN in Keras for sequence to sequence modelling?

Asked 2018-Aug-26 at 11:59

Although not new to Machine Learning, I am still relatively new to Neural Networks, more specifically how to implement them (In Keras/Python). Feedforwards and Convolutional architectures are fairly straightforward, but I am having trouble with RNNs.

My X data consists of variable length sequences, each data-point in that sequence having 26 features. My y data, although of variable length, each pair of X and y have the same length, e.g:

...

ANSWER

Answered 2018-Aug-26 at 11:59

The problem is that the decoder_gru layer does not return its state, therefore you should not use _ as the return value for the state (i.e. just remove , _):

Source https://stackoverflow.com/questions/52023702

QUESTION

How to get value of a tensor from a Tensorflow Mode

Asked 2017-Oct-08 at 19:39

I am using the following implementation of the Seq2Seq model. Now, if I want to pass some inputs and get the corresponding values of encoder's hidden state (self.encoder_last_state), how can I do it?

https://github.com/JayParks/tf-seq2seq/blob/master/seq2seq_model.py

...

ANSWER

Answered 2017-Oct-08 at 19:39

You need to first assemble input_feed, similar to the predict routine. Once you have that, just execute sess.run over the required hidden layer.

To assmeble the input_feed:

Source https://stackoverflow.com/questions/46634552

QUESTION

tensorflow: CUDA_ERROR_OUT_OF_MEMORY always happen

Asked 2017-Apr-18 at 11:26

I'm going to train a seq2seq model using tf-seq2seq package by 1080 ti (11GB) GPU. I always get the following error using different network's size (even nmt_small):

...

ANSWER

Answered 2017-Apr-18 at 09:36

you should notice some tips:

1- use memory growth, from tensorflow document: "in some cases it is desirable for the process to only allocate a subset of the available memory, or to only grow the memory usage as is needed by the process. TensorFlow provides two Config options on the Session to control this."

Source https://stackoverflow.com/questions/43467586

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

Vulnerabilities

No vulnerabilities reported

Install tf-seq2seq

You can download it from GitHub.
You can use tf-seq2seq 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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