neuralmonkey | source tool for sequence learning | Machine Learning library
kandi X-RAY | neuralmonkey Summary
kandi X-RAY | neuralmonkey Summary
neuralmonkey is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Neural Network applications. neuralmonkey has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However neuralmonkey has 9 bugs. You can download it from GitHub.
Neural Sequence Learning Using TensorFlow. The Neural Monkey package provides a higher level abstraction for sequential neural network models, most prominently in Natural Language Processing (NLP). It is built on TensorFlow. It can be used for fast prototyping of sequential models in NLP which can be used e.g. for neural machine translation or sentence classification. The higher-level API brings together a collection of standard building blocks (RNN encoder and decoder, multi-layer perceptron) and a simple way of adding new building blocks implemented directly in TensorFlow.
Neural Sequence Learning Using TensorFlow. The Neural Monkey package provides a higher level abstraction for sequential neural network models, most prominently in Natural Language Processing (NLP). It is built on TensorFlow. It can be used for fast prototyping of sequential models in NLP which can be used e.g. for neural machine translation or sentence classification. The higher-level API brings together a collection of standard building blocks (RNN encoder and decoder, multi-layer perceptron) and a simple way of adding new building blocks implemented directly in TensorFlow.
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Quality
Security
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Support
neuralmonkey has a low active ecosystem.
It has 402 star(s) with 107 fork(s). There are 34 watchers for this library.
It had no major release in the last 12 months.
There are 113 open issues and 283 have been closed. On average issues are closed in 183 days. There are 6 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of neuralmonkey is 0.2.5
Quality
neuralmonkey has 9 bugs (0 blocker, 0 critical, 6 major, 3 minor) and 289 code smells.
Security
neuralmonkey has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
neuralmonkey code analysis shows 0 unresolved vulnerabilities.
There are 12 security hotspots that need review.
License
neuralmonkey is licensed under the BSD-3-Clause License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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neuralmonkey releases are available to install and integrate.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
neuralmonkey saves you 19396 person hours of effort in developing the same functionality from scratch.
It has 38269 lines of code, 2834 functions and 486 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed neuralmonkey and discovered the below as its top functions. This is intended to give you an instant insight into neuralmonkey implemented functionality, and help decide if they suit your requirements.
- Create a function that parses an image file
- Crop the image
- Pad an image
- Resize an image
- Returns the next state of the given loop
- Dropout operator
- Append tensor to given axis
- Returns a GRU cell
- Initialize the experiment
- Loads the model
- Parse a config file
- Create a preprocessor for speech features
- Create a reader for t2t tokens
- Attention layer
- The image processing layers
- Build a configuration dictionary
- Score a batch of hypotheses
- Creates a function that returns a generator function that returns a function that reads the image files from the specified prefix
- Load the model
- Updates the statistics for a given pair
- Given a series config and a series config return a dictionary of key - value pairs
- Perform attention
- Performs attention
- Construct a vocabulary from a wordlist file
- Returns the result of the training loop
- Patch config builder
- Performs attention on query
Get all kandi verified functions for this library.
neuralmonkey Key Features
No Key Features are available at this moment for neuralmonkey.
neuralmonkey Examples and Code Snippets
No Code Snippets are available at this moment for neuralmonkey.
Community Discussions
Trending Discussions on neuralmonkey
QUESTION
InvalidArgumentError (see above for traceback): slice index 15 of dimension 0 out of bounds
Asked 2017-Sep-04 at 23:49
In a task to implement the minimum risk training for a neural machine translation system I need to sample sentences and gather the respective logits for the sampled word IDs. The step of gathering looks like this:
...ANSWER
Answered 2017-Sep-04 at 23:49According to the stack trace, the error comes from this expression in your code:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install neuralmonkey
You need Python 3.6 (or higher) to run Neural Monkey.
You need Python 3.6 (or higher) to run Neural Monkey.
When using virtual environment, execute these commands to install the Python dependencies: $ source path/to/virtualenv/bin/activate # For GPU-enabled version (virtualenv)$ pip install --upgrade -r requirements-gpu.txt # For CPU-only version (virtualenv)$ pip install --upgrade -r requirements.txt
If you are using the GPU version, make sure that the LD_LIBRARY_PATH environment variable points to lib and lib64 directories of your CUDA and CuDNN installations. Similarly, your PATH variable should point to the bin subdirectory of the CUDA installation directory.
If the training crashes on an unknown dependency, just install it with pip. Remember to keep your virtual environment up-to-date with the package requirements file, which may be changed over time. To update the dependencies, re-run the pip install command from above (pay attention to the distinction between GPU and non-GPU versions).
There is a tutorial that you can follow, which gives you the overwiev of how to design your experiments with Neural Monkey.
You need Python 3.6 (or higher) to run Neural Monkey.
When using virtual environment, execute these commands to install the Python dependencies: $ source path/to/virtualenv/bin/activate # For GPU-enabled version (virtualenv)$ pip install --upgrade -r requirements-gpu.txt # For CPU-only version (virtualenv)$ pip install --upgrade -r requirements.txt
If you are using the GPU version, make sure that the LD_LIBRARY_PATH environment variable points to lib and lib64 directories of your CUDA and CuDNN installations. Similarly, your PATH variable should point to the bin subdirectory of the CUDA installation directory.
If the training crashes on an unknown dependency, just install it with pip. Remember to keep your virtual environment up-to-date with the package requirements file, which may be changed over time. To update the dependencies, re-run the pip install command from above (pay attention to the distinction between GPU and non-GPU versions).
There is a tutorial that you can follow, which gives you the overwiev of how to design your experiments with Neural Monkey.
Support
You can find the API documentation of this package here. The documentation files are generated from docstrings using autodoc and Napoleon extensions to the Python documentation package Sphinx. The docstrings should follow the recommendations in the Google Python Style Guide. Additional details on the docstring formatting can be found in the Napoleon documentation as well.
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