Showing posts with label Keras Model Load. Show all posts
Showing posts with label Keras Model Load. Show all posts

Saturday, April 8, 2023

Part G: Text Classification with a Recurrent Layer

 

Part G: Text Classification with a Recurrent Layer


Author: Murat Karakaya
Date created….. 17 02 2023
Date published… 08 04 2023
Last modified…. 08 04 2023

Description: This is the Part G of the tutorial series “Multi-Topic Text Classification with Various Deep Learning Models which covers all the phases of multi-class  text classification:

  • Exploratory Data Analysis (EDA),

We will design various Deep Learning models by using

  • Keras Embedding layer,

We will cover all the topics related to solving Multi-Class Text Classification problems with sample implementations in Python / TensorFlow / Keras environment.

We will use a Kaggle Dataset in which there are 32 topics and more than 400K total reviews.

If you would like to learn more about Deep Learning with practical coding examples,

You can access all the codes, videos, and posts of this tutorial series from the links below.

Accessible on:


PARTS

In this tutorial series, there are several parts to cover Text Classification with various Deep Learning Models topics. You can access all the parts from this index page.

In this part, we will use the Keras Bidirectional LSTM layer in a Feed Forward Network (FFN).

If you are not familiar with the Keras LSTM layer or the Recurrent Networks concept, you can check in the following Murat Karakaya Akademi YouTube playlists:

English:

Turkish

If you are not familiar with the classification with Deep Learning topic, you can find the 5-part tutorials in the below Murat Karakaya Akademi YouTube playlists:

Friday, November 4, 2022

How to save and load a TensorFlow / Keras Model with Custom Objects?

 

How to save and load a TensorFlow / Keras Model with Custom Objects?

Author: Murat Karakaya
Date created: 30 May 2021
Last modified: 30 July 2021
Description: This tutorial will design and train a Keras model (miniature GPT3) with some custom objects (custom layers). We aim to learn how to save the trained model as a whole and load the saved model “TensorFlow SevedModel”.
Accessible on:



Photo by Markus Winkler on Unsplash