Optmizer | Comparison of different optimizers used in machine learning | Machine Learning library

by Coder-Yu Python Version: Current License: No License

X-Ray Key Features Code Snippets(1)Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | Optmizer Summary

Optmizer is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. Optmizer has no bugs, it has no vulnerabilities and it has low support. However Optmizer build file is not available. You can download it from GitHub.

Comparison of different optimizers used in machine learning

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Support

Optmizer has a low active ecosystem.

It has 6 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of Optmizer is current.

Quality

Optmizer has no bugs reported.

Security

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

License

Optmizer 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.

Reuse

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

Optmizer has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Update the model .
read data from a file
Parse options .
Runs the optimizer .
read configuration file
Read parameters from file .
Return the option with the given key .
returns the option value

Get all kandi verified functions for this library.

Optmizer Key Features

No Key Features are available at this moment for Optmizer.

Optmizer Examples and Code Snippets

Build an optimizer .

python

Lines of Code : 16

License : Permissive (MIT License)

Copy

def build_optimizer(loss, learning_rate, grad_clip):
    ''' Build optmizer for training, using gradient clipping.
    
        Arguments:
        loss: Network loss
        learning_rate: Learning rate for optimizer
        grad_clip: threshold for

Community Discussions

Trending Discussions on Optmizer

How to fix TensorFlow Linear Regression no change in MSE?

Understanding scikit-learn GridSearchCV - param tuning and averaging performance metrics

Are WordPress admin plugins loaded on the front end?

Function approximation with Neural Network - Loss 0

tensorflow resize nearest neighbor approach don't optmize weights

QUESTION

How to fix TensorFlow Linear Regression no change in MSE?

Asked 2019-Jan-09 at 19:12

I'm working on a simple linear regression model to predict the next step in a series. I'm giving it x/y coordinate data and I want the regressor to predict where the next point on the plot will lie.

I'm using dense layers with AdamOptmizer and have my loss function set to:

tf.reduce_mean(tf.square(layer_out - y))

I'm trying to create linear regression models from scratch (I don't want to utilize the TF estimator package here).

I've seen ways to do it by manually specifying weights and biases, but nothing goes into deep regression.

...

ANSWER

Answered 2019-Jan-09 at 19:12

The issue is that I'm applying an activation to the output layer. This is causing that output to go to whatever it activates to.

By specifying in the last layer that activation=None the deep regression works as intended.

Here is the updated architecture:

Source https://stackoverflow.com/questions/54103624

QUESTION

Understanding scikit-learn GridSearchCV - param tuning and averaging performance metrics

Asked 2018-Feb-18 at 17:49

I am trying to understand how exactly the GridSearchCV in scikit-learn implements the train-validation-test principle in machine learning. As you see in the following code, I understand what it does is as follows:

split the 'dataset' into 75% and 25%, where 75% is used for param tuning, and 25% is the held out test set (line 1)
init some parameters to search (lines 3 to 6)
fit the model on the 75% of dataset, but split this dataset into 5 folds, i.e., each time train on 60% of the data, test on the other 15%, and do this 5 times (lines 8 - 10). I have my first and second questions here, see below.
take the best performing model and parameters, test on the holdout data (lines 11-13)

Question 1: what is exactly going on in step 3 with respect to the parameter space? Is GridSearchCV trying every parameter combination on every one of the five runs (5-fold) so giving a total of 10 runs? (i.e., the single param from 'optmizers', 'init', and 'batches' is paired with the 2 from 'epoches']

Question 2: what scores does line 'cross_val_score' print? Is this the average of the 10 above runs on the single fold of the data in each of the 5 runs? (i.e., the average of five 15% of the entire dataset)?

Question 3: suppose line 5 now has only 1 parameter value, this time GridSearchCV is really not searching any parameters because each parameter has only 1 value, is this correcct?

Question 4: in case explained in question 3, if we take a weighted average of the scores computed on the 5-folds of GridSearchCV runs and the heldout run, that gives us an average peformance score on the entire dataset - this is very similar to a 6-fold cross-validation experiment (i.e., without gridsearch), except the 6 fold are not entirely equal size. Or is this not?

Many thanks in advance for any replies!

...

ANSWER

Answered 2018-Feb-18 at 17:49

Question 1: As you said, you dataset will be splitted in 5 pieces. Every parameters will be tried (in your case 2). For each parameters, model will be trained on 4 of the 5 folds. The remaining one will be used as test. So you are right, in your example, you are going to train 10 times a model.

Question 2: 'cross_val_score' is the average (accuracy, loss or something) on the 5 test folds. This is done to avoid having for example a good result juste because the test set was really easy.

Question 3: Yes. It makes no sense if you have only one set of parameter to try to do a grid search

Question 4: I didn't exactly understand your question. Usually, you use a grid search on your train set. This allows you to keep your test set as a validation set. Without cross validation, you could find a perfect setting to maximise results on your test set and you would be overfitting your test set. With a cross validation, you can play as much as you want with fine-tunning parameter as you don't use your validation set to set it up.

In your code, there is no big need of CV as you don't have a lot of parameters to play with, but if you start adding regularization, you may try 10+ and in such case, CV is required.

I hope it helps,

Source https://stackoverflow.com/questions/48852524

QUESTION

Are WordPress admin plugins loaded on the front end?

Asked 2017-Aug-16 at 21:00

My question basically is: are the "back-end" plugins, which in nothing affects the blog front-end, loaded when an anonymous user, for example, browse my blog?

Let's say the EWWW image optmizer, for instance: it just optmize the images, in the back-end. It has a admin interface to optimize the images, but the end-user doesn't use it at all. Still it gets loaded in each page visit?

I'm not sure if I'm making myself clear. Hope so.

...

ANSWER

Answered 2017-Aug-16 at 21:00

As far as I know there is no way to specify a plugin as admin only in the WordPress API. The only plugin types I know about are 'must use', 'network activitate' (for multi user sites) and 'active' so I think an admin plugin will also load in non admin mode. The plugins are loaded in wp-settings.php. I read the code and it seems to me that WordPress doesn't distinguish between admin mode and non-admin mode as far as plugin loading is concerned. The relevant code is:

Source https://stackoverflow.com/questions/45722023

QUESTION

Function approximation with Neural Network - Loss 0

Asked 2017-Jul-19 at 17:02

I'm trying to create a NN to approximate functions (sine, cos, custom...) but I'm struggling with the format, I don't want to use input-label, but rather, input-output. How do I change it?

I'm following this tutorial

...

ANSWER

Answered 2017-Jul-19 at 16:59

Have not tried it myself, so there might be more things you need to change to get the model to run, but you will definitely want to change this line:

Source https://stackoverflow.com/questions/45196416

QUESTION

tensorflow resize nearest neighbor approach don't optmize weights

Asked 2017-Apr-03 at 16:43

I'm beginner in tensorflow and i'm working on a Model which Colorize Greyscale images and in the last part of the model the paper say :

Once the features are fused, they are processed by a set of convolutions and upsampling layers, the latter which consist of simply upsampling the input by using the nearest neighbour technique so that the output is twice as wide and twice as tall.

when i tried to implement it in tensorflow i used tf.image.resize_nearest_neighbor for upsampling but when i used it i found the cost didn't change in all the epochs except of the 2nd epoch, and without it the cost is optmized and changed

This part of code

...

ANSWER

Answered 2017-Apr-03 at 16:43

Ok, i solved it, i noticed that tf.random normal was the problem and when i replaced it with tf.truncated normal it is works well

Source https://stackoverflow.com/questions/43184376

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Optmizer

You can download it from GitHub.
You can use Optmizer 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.

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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