SimpleLSTM | recurrent neural network heavily inspired by Long Short | Machine Learning library
kandi X-RAY | SimpleLSTM Summary
kandi X-RAY | SimpleLSTM Summary
SimpleLSTM is a Java library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Neural Network applications. SimpleLSTM has no vulnerabilities, it has a Permissive License and it has low support. However SimpleLSTM has 1 bugs and it build file is not available. You can download it from GitHub.
This is a recurrent neural network architecture inspired by Long Short Term Memory, but with a much simpler architecture. I will be adding more documentation to describe this architecture shortly. For now, a brief summary is that it has only a single gate (similar to the Forget Gate in LSTM) and an input squashing function. Rather than acting to reset the state of the cell, the gate modulates a weighted average of the current input with the state of the cell at the previous time step. So if the gate is fully active, the cell will ignore its current input and fully retain its state. If the gate is inactive then the cell will lose all traces of its previous state and shift to match the current input. So far on a few tests I've done, it appears to perform fairly well, although it tends to need more cell blocks than LSTM to do the same thing.
This is a recurrent neural network architecture inspired by Long Short Term Memory, but with a much simpler architecture. I will be adding more documentation to describe this architecture shortly. For now, a brief summary is that it has only a single gate (similar to the Forget Gate in LSTM) and an input squashing function. Rather than acting to reset the state of the cell, the gate modulates a weighted average of the current input with the state of the cell at the previous time step. So if the gate is fully active, the cell will ignore its current input and fully retain its state. If the gate is inactive then the cell will lose all traces of its previous state and shift to match the current input. So far on a few tests I've done, it appears to perform fairly well, although it tends to need more cell blocks than LSTM to do the same thing.
Support
Quality
Security
License
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Support
SimpleLSTM has a low active ecosystem.
It has 20 star(s) with 11 fork(s). There are 4 watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 1 have been closed. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of SimpleLSTM is current.
Quality
SimpleLSTM has 1 bugs (0 blocker, 1 critical, 0 major, 0 minor) and 65 code smells.
Security
SimpleLSTM has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
SimpleLSTM code analysis shows 0 unresolved vulnerabilities.
There are 1 security hotspots that need review.
License
SimpleLSTM 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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SimpleLSTM releases are not available. You will need to build from source code and install.
SimpleLSTM has no build file. You will be need to create the build yourself to build the component from source.
It has 353 lines of code, 25 functions and 10 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed SimpleLSTM and discovered the below as its top functions. This is intended to give you an instant insight into SimpleLSTM implemented functionality, and help decide if they suit your requirements.
- Evaluate the fitness of the agent
- Generate a list of interactions from the test set
- Find the maximum value in a vector
- Computes the derivative of the activation function
- Activation function
- Derive derivative of x
- Function activation function
Get all kandi verified functions for this library.
SimpleLSTM Key Features
No Key Features are available at this moment for SimpleLSTM.
SimpleLSTM Examples and Code Snippets
No Code Snippets are available at this moment for SimpleLSTM.
Community Discussions
Trending Discussions on SimpleLSTM
QUESTION
TypeError: forward() missing 1 required positional argument with Tensorboard PyTorch
Asked 2020-Jun-18 at 20:48
I am trying to write my model to tensorboard
with the following code:
ANSWER
Answered 2020-Jun-18 at 20:48Your model takes two arguments input
and batch_size
, but you only provide one argument for add_graph
to call your model with.
The inputs (second argument to add_graph
) should be a tuple with the input
and the batch_size
:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install SimpleLSTM
You can download it from GitHub.
You can use SimpleLSTM like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the SimpleLSTM component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
You can use SimpleLSTM like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the SimpleLSTM component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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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