rnn | Recurrent Neural Network in Go | Machine Learning library

by armhold Go Version: Current License: No License

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kandi X-RAY | rnn Summary

rnn is a Go library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. rnn has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

This is more or less a straight translation of Andrej Karpathy's Recurrent Neural Network code from Python to Go. See for more information. I have attempted to translate it faithfully, even down to the level of preserving variable names (many of which are somewhat... terse) and his comment text. The one major change I did introduce is code for checkpointing the model; this is primarily implemented in persistence.go. Any errors here are my own, and not Karpathy's. Corrections welcome.

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rnn has a low active ecosystem.

It has 10 star(s) with 4 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

rnn has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of rnn is current.

Quality

rnn has no bugs reported.

Security

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

License

rnn 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.

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

Top functions reviewed by kandi - BETA

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

GobEncode encodes RNN
NewRNN creates a new RNN .
Main entry point for testing .
LoadFrom loads a RNN from a file
ravel returns a slice of float64
mapInput returns a map of unique characters for the input string
expDivSumExp computes the sum of m .
NewSimpleRNN returns a new SimpleRNN .
clipTo clips the input matrix to the right range .
randomize randomizes the matrix .

Get all kandi verified functions for this library.

rnn Key Features

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rnn Examples and Code Snippets

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Community Discussions

Trending Discussions on rnn

Evaluate simple RNN in Julia Flux

React Native Navigation: Navigate to a screen from outside a React component

RNN LSTM valueError while training

LSTM outputs flat line

Last layer in a RNN - Dense, LSTM, GRU...?

Flatten 3D tensor

Interpreting recurrent neural networks features (RNN/LSTM)

Tensorflow TextVectorization brings None shape in model.summary()

Training Will Be Stop After a While in GRU Layer Pytorch

AttributeError: 'tuple' object has no attribute 'size'

QUESTION

Evaluate simple RNN in Julia Flux

Asked 2021-Jun-11 at 12:27

I'm trying to learn Recurrent Neural Networks (RNN) with Flux.jl in Julia by following along some tutorials, like Char RNN from the FluxML/model-zoo.

I managed to build and train a model containing some RNN cells, but am failing to evaluate the model after training.

Can someone point out what I'm missing for this code to evaluate a simple (untrained) RNN?

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ANSWER

Answered 2021-Jun-11 at 12:27

Turns out it's just a problem with the input type.

Doing something like this will work:

Source https://stackoverflow.com/questions/67934386

QUESTION

React Native Navigation: Navigate to a screen from outside a React component

Asked 2021-Jun-01 at 16:14

https://wix.github.io/react-native-navigation/docs/basic-navigation#navigating-in-a-stack indicates that pushing a new screen to the stack requires the current screen's componentId. My use case is to allow navigating based on certain events emitted by a native module. As such, componentId won't be available to me, since the event listener would reside outside any React component screen. Is there any way to navigate to a screen from outside in RNN? Or even get the current componentId.

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ANSWER

Answered 2021-Jun-01 at 16:14

I ended up adding a command event listener to store the current componentId in closure.

Source https://stackoverflow.com/questions/67787783

QUESTION

RNN LSTM valueError while training

Asked 2021-May-29 at 14:17

Hi there recently I've been working on a RNN LSTM project and I have e 2D data set like

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ANSWER

Answered 2021-May-29 at 14:17

Change:

Source https://stackoverflow.com/questions/67727890

QUESTION

LSTM outputs flat line

Asked 2021-May-23 at 20:46

I've been trying to make a simple LSTM network to predict S&P500 next 5 values % change. My NN however outputs almost a completely flat line.

5% future change and the red is the "prediction":

I know I should never check my model in the train set, but this is just a sanity check to find out if it works at all.

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ANSWER

Answered 2021-May-23 at 20:46

The model you show in your question at the moment is a linear regression of the inputs.

i.e.

Source https://stackoverflow.com/questions/67659532

QUESTION

Last layer in a RNN - Dense, LSTM, GRU...?

Asked 2021-May-20 at 11:48

I know you can use different types of layers in an RNN architecture in Keras, depending on the type of problem you have. What I'm referring to is for example layers.SimpleRNN, layers.LSTM or layers.GRU.

So let's say we have (with the functional API in Keras):

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ANSWER

Answered 2021-May-20 at 11:48

TL;DR Both are valid choices.

Overall it depends of the kind of output you want or, more precisely, where do you want your output to come from. You can use the outputs of the LSTM layer directly, or you can use a Dense layer, with or without a TimeDistributed layer. One reason for adding another Dense layer after the final LSTM is allowing your model to be more expressive (and also more prone to overfitting). So, using a final dense layer or not is up to experimentation.

Source https://stackoverflow.com/questions/67610760

QUESTION

Flatten 3D tensor

Asked 2021-May-19 at 15:13

I have a tensor of the shape T x B x N (training data for a RNN, T is max seq length, B is number of batches, and N number of features) and I'd like to flatten all the features across timesteps, such that I get a tensor of the shape B x TN. Haven't been able to figure out how to do this..

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ANSWER

Answered 2021-May-19 at 15:13

You need to permute your axes before flattening, like so:

Source https://stackoverflow.com/questions/67595244

QUESTION

Interpreting recurrent neural networks features (RNN/LSTM)

Asked 2021-May-14 at 21:22

I tried to use shap in order to do a feature importance analysis. I am using keras and I want to get a bar chart and violin charts. With my DNN, I got something like that: Violin chart bar chart

However, when I tried it with my SimpleRNN, I had problem with the shape. The input shape is (samples,time,features), whereas my output shape is (samples,features). So it is a many-to-one RNN. KernelExplainer, the one that I used in my static models, does not work because of the dimension. DeepExplainer does not work either. It show me this error:

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ANSWER

Answered 2021-May-14 at 21:22

I managed to fix it by adding at the beginning of my code

Source https://stackoverflow.com/questions/67533326

QUESTION

Tensorflow TextVectorization brings None shape in model.summary()

Asked 2021-May-13 at 18:10

I am using an encoder from using the TextVectorization object from preprocessing class. I then adapt my train data like so:

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ANSWER

Answered 2021-May-13 at 17:53

This is because you haven't specified the argument that indicates what the output shape of encoder will be, i.e output_sequence_length.

output_sequence_length: If set, the output will have its time dimension padded or truncated to exactly output_sequence_length values, resulting in a tensor of shape [batch_size, output_sequence_length] regardless of how many tokens resulted from the splitting step. Defaults to None.

If you set it to a number, you will see that the output shape of the layer will be defined:

Source https://stackoverflow.com/questions/67521034

QUESTION

Training Will Be Stop After a While in GRU Layer Pytorch

Asked 2021-May-11 at 02:58

I use my custom dataset class to convert audio files to mel-Spectrogram images. the shape will be padded to (128,1024). I have 10 classes. after a while of training in the first epoch, my network will be crashed inside the hidden layer in GRU shapes due to this error:

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ANSWER

Answered 2021-May-11 at 02:58

Errors like this are usually due to your data changing in some unexpected way, as the model is fixed and (as you said) working until a point. I think your error comes from this line in your model.forward() call:

Source https://stackoverflow.com/questions/67476115

QUESTION

AttributeError: 'tuple' object has no attribute 'size'

Asked 2021-May-10 at 23:55

UPDATE: after looking back on this question, most of the code was unnecessary. To make a long story short, the hidden layer of a Pytorch RNN needs to be a torch tensor. When I posted the question, the hidden layer was a tuple.

Below are my data loader and model.

...

ANSWER

Answered 2021-Jan-02 at 20:05

There should be [] brackets instead of () around 0.

Source https://stackoverflow.com/questions/65543423

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