gru | Orchestration made easy with Go and Lua

by dnaeon Go Version: HEAD License: Non-SPDX

X-Ray Key Features Code Snippets(3)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | gru Summary

gru is a Go library typically used in Programming Style applications. gru has no bugs, it has no vulnerabilities and it has low support. However gru has a Non-SPDX License. You can download it from GitHub.

Gru is a fast and concurrent orchestration framework powered by Go and Lua, which allows you to manage your UNIX/Linux systems with ease.

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gru has a low active ecosystem.

It has 417 star(s) with 27 fork(s). There are 16 watchers for this library.

It had no major release in the last 12 months.

There are 9 open issues and 36 have been closed. On average issues are closed in 60 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of gru is HEAD

Quality

gru has no bugs reported.

Security

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

License

gru has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

gru releases are available to install and integrate.

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gru Key Features

No Key Features are available at this moment for gru.

gru Examples and Code Snippets

Convert an RNN layer to a function .

python

Lines of Code : 165

License : Non-SPDX (Apache License 2.0)

Copy

def _convert_rnn_weights(layer, weights):
  """Converts weights for RNN layers between native and CuDNN format.

  Input kernels for each gate are transposed and converted between Fortran
  and C layout, recurrent kernels are transposed. For LSTM bia

Wrapper for the GPU_gru_with_fallback method .

python

Lines of Code : 127

License : Non-SPDX (Apache License 2.0)

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def gru_with_backend_selection(inputs, init_h, kernel, recurrent_kernel, bias,
                               mask, time_major, go_backwards, sequence_lengths,
                               zero_output_for_mask):
  """Call the GRU with optimized bac

Uses CUDNN .

python

Lines of Code : 84

License : Non-SPDX (Apache License 2.0)

Copy

def gpu_gru(inputs, init_h, kernel, recurrent_kernel, bias, mask, time_major,
            go_backwards, sequence_lengths):
  """GRU with CuDNN implementation which is only available for GPU."""
  if not time_major and mask is None:
    inputs = array

Community Discussions

Trending Discussions on gru

Nginx redirection different with IP behavior on 2 different machine

Powershell regex to replace a specific character between two identical characters

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

How to monitor accuracy with CTC loss function and Datasets? (runnable code included)

Get second last value in each row of dataframe, R

Training Will Be Stop After a While in GRU Layer Pytorch

Keras autoencoder model for detect anomaly in text

RNN/GRU Increasing validation loss but decreasing mean absolute error

tf.train.Checkpoint is restoring or not?

Keras model having multiple inputs causes strange errors when fitting

QUESTION

Nginx redirection different with IP behavior on 2 different machine

Asked 2021-Jun-01 at 06:23

context

I have 2 Linux Red Hat machines hosting nginx server. Nginx server is working with below config:

nginx config server 1

...

ANSWER

Answered 2021-Jun-01 at 06:23

Thanks to @ThanhNguyenVan's comment, I found the solution.

I was only focusing on /etc/nginx/conf.d and as I am not familiar with nginx I was not aware that there was a config above i.e /etc/nginx/nginx.conf. I then compare the server1 vs server2 config and saw the difference.

After commenting that part below it worked like charm:

Source https://stackoverflow.com/questions/67718011

QUESTION

Powershell regex to replace a specific character between two identical characters

Asked 2021-May-25 at 20:23

I am trying to use Powershell to replace a semicolon ; with a pipe | that is in a file that is semicolon separated, so it's a specific set of semicolons that occurs between double-quotes ". Here's a sample of the file with the specific portion in bold:

Camp;Brazil;AI;BCS GRU;;MIL-32011257;172-43333640;;"1975995;1972871;1975";FAC0088/21;3;20.000;24.8;25.000;.149;GLASSES SPARE PARTS,;EXW;C;.00;EUR;

I've tried using -replace, as follows:

...

ANSWER

Answered 2021-May-25 at 20:23

You can use a Regex.Replace method with a callback as the replacement argument:

Source https://stackoverflow.com/questions/67691535

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):

...

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

How to monitor accuracy with CTC loss function and Datasets? (runnable code included)

Asked 2021-May-19 at 20:37

I've been trying to speed up training of my CRNN network for optical character recognition, but I can't get the accuracy metric working when using TFRecords and tf.data.Dataset pipelines. I previously used a Keras Sequence and had it working. Here is a complete runnable toy example showing my problem (tested with Tensorflow 2.4.1):

...

ANSWER

Answered 2021-May-17 at 09:45

There probably some issue with [accuracy] with tf.data, but I'm not super sure if this is the main cause in your case or if the issue still exits. If I try as follows, it runs anyway without Sequence (with tf.data).

Source https://stackoverflow.com/questions/67506106

QUESTION

Get second last value in each row of dataframe, R

Asked 2021-May-14 at 14:45

I am trying to get the second last value in each row of a data frame, meaning the first job a person has had. (Job1_latest is the most recent job and people had a different number of jobs in the past and I want to get the first one). I managed to get the last value per row with the code below:

first_job <- function(x) tail(x[!is.na(x)], 1)

first_job <- apply(data, 1, first_job)

...

ANSWER

Answered 2021-May-11 at 13:56

You can get the value which is next to last non-NA value.

Source https://stackoverflow.com/questions/67486393

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:

...

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

Keras autoencoder model for detect anomaly in text

Asked 2021-Apr-27 at 10:50

I am trying to create an autoencoder that is capable of finding anomalies in text sequences:

...

ANSWER

Answered 2021-Apr-24 at 11:54

I've seen your code snippet and it seems that your model output need to match your target shape which is (None, 999), but your output shape is (None, 200, 999).

You need to make your output model shape match the target shape.

Try using tf.reduce_mean with axis=1 (averages all the sequence):

Source https://stackoverflow.com/questions/67241372

QUESTION

RNN/GRU Increasing validation loss but decreasing mean absolute error

Asked 2021-Apr-19 at 20:35

I am new to deep learning and I try to implement an RNN (with 2 GRU layers). At first, the network seems to do it's job quite fine. However, I am currently trying to understand the loss and accuracy curve. I attached the pictures below. The dark-blue line is the training set and the cyan line is the validation set. After 50 epochs the validation loss increases. My assumption is that this indicates overfitting. However, I am unsure why the validation mean absolute error still decreases. Do you maybe got an idea?

One idea I had in mind was that this could be caused by some big outliers in my dataset. Thus I already tried to clean it up. I also tried to scale it properly. I also added a few dropout layers for further regularization (rate=0.2). However these are just normal dropout layers because cudnn does not seem to support recurrent_dropout from tensorflow.

Remark: I am using the negative log-likelihood as loss function and a tensorflow probability distribution as the output dense layer.

Any hints what I should investigate? Thanks in advance

Edit: I also attached the non-probabilistic plot as recommended in the comment. Seems like here the mean-absolute-error behaves normal (does not improve all the time).

...

ANSWER

Answered 2021-Apr-19 at 17:55

What are the outputs of your model? It sounds pretty strange that you're using the negative log-likelihood (which basically "works" with distributions) as the loss function but the MAE as a metric, which is suited for deterministic continuous values.

I don't know what is your task and perhaps this is meaningful in your specific case, but perhaps the strange behavior comes out from there.

Source https://stackoverflow.com/questions/67165410

QUESTION

tf.train.Checkpoint is restoring or not?

Asked 2021-Apr-07 at 13:45

I am running tensorflow 2.4 on colab. I tried to save the model using tf.train.Checkpoint() since it includes model subclassing, but after restoration I saw It didn't restored any weights of my model.

Here are few snippets:

...

ANSWER

Answered 2021-Apr-07 at 13:45

You are defining a keras model, so why do not use keras model chekpoints?

From Keras documentation:

Source https://stackoverflow.com/questions/66942378

QUESTION

Keras model having multiple inputs causes strange errors when fitting

Asked 2021-Mar-16 at 12:09

I am currently working on a encoder-decoder model using GRUs. It takes 2 inputs, encoder input and decoder input. There is only one output from the decoder. The model is:

...

ANSWER

Answered 2021-Mar-15 at 23:55

Decoder_data and decoder_truth should be the same length as GRUs give one output for each input. Also, the number of time steps per batch should remain constant.

Source https://stackoverflow.com/questions/66638181

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