gru | centric inventory management system | DevOps library
kandi X-RAY | gru Summary
kandi X-RAY | gru Summary
GRU is a host-centric inventory management system. It is used to visualize and provide context on individual servers and more importantly, on groups of servers. It was designed in order to help operations teams, as well as developers to better understand their infrastructure and to provide a unified view of available compute resources.
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Top functions reviewed by kandi - BETA
- Generate a Flask application
- Return the relative path to a file
- Setup logging
- Get value from config
- List hosts
- Returns the range of the results
- Returns the next page of results
- Serialize the results to a dictionary
- Returns a list of HostCategory objects for a given category
- Return a list of HostCategory objects for the given category
- Saves a session
- Return a list of hosts
- Default handler for requests
- Pretty print a nested object
- Get a host by id
- Render the plugin
- Displays a group breakdown
- Updates facts in elasticsearch
- Return a session object
- View for a host search
- Perform a host search
- Show host info
- Authenticate user with given credentials
- Returns a list of hosts
- Initialize AWS accounts
- Get facter facts
gru Key Features
gru Examples and Code Snippets
Lines of Code : 165License : Non-SPDX (Apache License 2.0)
Copydef _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 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 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
QUESTION
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 06:23Thanks 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:
QUESTION
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 20:23You can use a Regex.Replace method with a callback as the replacement argument:
QUESTION
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 11:48TL;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.
QUESTION
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 09:45There 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).
QUESTION
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 13:56You can get the value which is next to last non-NA value.
QUESTION
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 02:58Errors 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:
QUESTION
I am trying to create an autoencoder that is capable of finding anomalies in text sequences:
...ANSWER
Answered 2021-Apr-24 at 11:54I'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):
QUESTION
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 17:55What 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.
QUESTION
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 13:45You are defining a keras model, so why do not use keras model chekpoints?
From Keras documentation:
QUESTION
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 23:55Decoder_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.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install gru
You can use gru 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.
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