Lina  Biyinzika

Lina Biyinzika

1658468940

Lattice: Monotonic Calibrated interpolated Look-Up Tables - TensorFlow

TensorFlow Lattice

TensorFlow Lattice is a library that implements constrained and interpretable lattice based models. It is an implementation of Monotonic Calibrated Interpolated Look-Up Tables in TensorFlow.

The library enables you to inject domain knowledge into the learning process through common-sense or policy-driven shape constraints. This is done using a collection of Keras layers that can satisfy constraints such as monotonicity, convexity and pairwise trust:

  • PWLCalibration: piecewise linear calibration of signals.
  • CategoricalCalibration: mapping of categorical inputs into real values.
  • Lattice: interpolated look-up table implementation.
  • Linear: linear function with monotonicity and norm constraints.

The library also provides easy to setup canned estimators for common use cases:

  • Calibrated Linear
  • Calibrated Lattice
  • Random Tiny Lattices (RTL)
  • Crystals

With TF Lattice you can use domain knowledge to better extrapolate to the parts of the input space not covered by the training dataset. This helps avoid unexpected model behaviour when the serving distribution is different from the training distribution.

You can install our prebuilt pip package using

pip install tensorflow-lattice

Author: tensorflow
Source code: https://github.com/tensorflow/lattice
License: Apache-2.0 license

#tensorflow #python 

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Lattice: Monotonic Calibrated interpolated Look-Up Tables - TensorFlow
Lina  Biyinzika

Lina Biyinzika

1658468940

Lattice: Monotonic Calibrated interpolated Look-Up Tables - TensorFlow

TensorFlow Lattice

TensorFlow Lattice is a library that implements constrained and interpretable lattice based models. It is an implementation of Monotonic Calibrated Interpolated Look-Up Tables in TensorFlow.

The library enables you to inject domain knowledge into the learning process through common-sense or policy-driven shape constraints. This is done using a collection of Keras layers that can satisfy constraints such as monotonicity, convexity and pairwise trust:

  • PWLCalibration: piecewise linear calibration of signals.
  • CategoricalCalibration: mapping of categorical inputs into real values.
  • Lattice: interpolated look-up table implementation.
  • Linear: linear function with monotonicity and norm constraints.

The library also provides easy to setup canned estimators for common use cases:

  • Calibrated Linear
  • Calibrated Lattice
  • Random Tiny Lattices (RTL)
  • Crystals

With TF Lattice you can use domain knowledge to better extrapolate to the parts of the input space not covered by the training dataset. This helps avoid unexpected model behaviour when the serving distribution is different from the training distribution.

You can install our prebuilt pip package using

pip install tensorflow-lattice

Author: tensorflow
Source code: https://github.com/tensorflow/lattice
License: Apache-2.0 license

#tensorflow #python 

Fredy  Larson

Fredy Larson

1595209620

How to alter tables in production when records are in millions

As a developer, I have experienced changes in app when it is in production and the records have grown up to millions. In this specific case if you want to alter a column using simple migrations that will not work because of the following reasons:

It is not so easy if your production servers are under heavy load and the database tables have 100 million rows. Because such a migration will run for some seconds or even minutes and the database table can be locked for this time period – a no-go on a zero-downtime environment.

In this specific case you can use MySQL’s algorithms: Online DDL operations. That’s how you can do it in Laravel.

First of all create migration. For example I want to modify a column’s name the traditional migration will be:

Schema::table('users', function (Blueprint $table) {
            $table->renameColumn('name', 'first_name');
        });

Run the following command php artisan migrate –pretend this command will not run the migration rather it will print out it’s raw sql:

ALTER TABLE users CHANGE name first_name VARCHAR(191) NOT NULL

Copy that raw sql, remove following code:

Schema::table('users', function (Blueprint $table) {
            $table->renameColumn('name', 'first_name');
        });

Replace it with following in migrations up method:

\DB::statement('ALTER TABLE users CHANGE name first_name VARCHAR(191) NOT NULL');

Add desired algorithm, in my case query will look like this:

\DB::statement('ALTER TABLE users CHANGE name first_name VARCHAR(191) NOT NULL, ALGORITHM=INPLACE, LOCK=NONE;');

#laravel #mysql #php #alter heavy tables in production laravel #alter table in production laravel #alter tables with million of records in laravel #how to alter heavy table in production laravel #how to alter table in production larave #mysql online ddl operations

5 Steps to Passing the TensorFlow Developer Certificate

Deep Learning is one of the most in demand skills on the market and TensorFlow is the most popular DL Framework. One of the best ways in my opinion to show that you are comfortable with DL fundaments is taking this TensorFlow Developer Certificate. I completed mine last week and now I am giving tips to those who want to validate your DL skills and I hope you love Memes!

  1. Do the DeepLearning.AI TensorFlow Developer Professional Certificate Course on Coursera Laurence Moroney and by Andrew Ng.

2. Do the course questions in parallel in PyCharm.

#tensorflow #steps to passing the tensorflow developer certificate #tensorflow developer certificate #certificate #5 steps to passing the tensorflow developer certificate #passing

Mckenzie  Osiki

Mckenzie Osiki

1623139838

Transfer Learning on Images with Tensorflow 2 – Predictive Hacks

In this tutorial, we will provide you an example of how you can build a powerful neural network model to classify images of **cats **and dogs using transfer learning by considering as base model a pre-trained model trained on ImageNet and then we will train additional new layers for our cats and dogs classification model.

The Data

We will work with a sample of 600 images from the Dogs vs Cats dataset, which was used for a 2013 Kaggle competition.

#python #transfer learning #tensorflow #images #transfer learning on images with tensorflow #tensorflow 2

TensorFlow Lite Object Detection using Raspberry Pi and Pi Camera

I have not created the Object Detection model, I have just merely cloned Google’s Tensor Flow Lite model and followed their Raspberry Pi Tutorial which they talked about in the Readme! You don’t need to use this article if you understand everything from the Readme. I merely talk about what I did!

Prerequisites:

  • I have used a Raspberry Pi 3 Model B and PI Camera Board (3D printed a case for camera board). **I had this connected before starting and did not include this in the 90 minutes **(plenty of YouTube videos showing how to do this depending on what Pi model you have. I used a video like this a while ago!)

  • I have used my Apple Macbook which is Linux at heart and so is the Raspberry Pi. By using Apple you don’t need to install any applications to interact with the Raspberry Pi, but on Windows you do (I will explain where to go in the article if you use windows)

#raspberry-pi #object-detection #raspberry-pi-camera #tensorflow-lite #tensorflow #tensorflow lite object detection using raspberry pi and pi camera