https://github.com/miozilla/kerastcvai

kerastcvai :books::abacus::moneybag: : # Keras Text Classification # DNN # RNN # CNN
https://github.com/miozilla/kerastcvai

conv1d convolution deep dense earlystopping embedding flatten gru lambda network neural one-hot recurrent token

Last synced: 7 months ago
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kerastcvai :books::abacus::moneybag: : # Keras Text Classification # DNN # RNN # CNN

• Host: GitHub
• URL: https://github.com/miozilla/kerastcvai
• Owner: miozilla
• Created: 2025-08-31T04:42:23.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-08-31T05:34:31.000Z (9 months ago)
• Last Synced: 2025-08-31T07:13:36.611Z (9 months ago)
• Topics: conv1d, convolution, deep, dense, earlystopping, embedding, flatten, gru, lambda, network, neural, one-hot, recurrent, token
• Language: Jupyter Notebook
• Homepage:
• Size: 1.83 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# kerastcvai 📚🧮💰

kerastcvai : Keras Text Classification # DNN # RNN # CNN

## Objective

- To create a text classification datasets using BigQuery.

- To tokenize and integerize a corpus of text for training in Keras.

- To do one-hot-encodings in Keras.

- To use embedding layers to represent words in Keras.

- To use bag-of-word representation for sentences.

- To use DNN/CNN/RNN model to classify text in keras.

- To create Keras Sequential model with three layers (build_dnn_model)

    - 1st layer : Embedding transforming the word integers into dense vectors

    - 2nd layer : Lambda to create a bag-of-words representation of the sentences by computing the mean

    - 3rd layer : Dense softmax layer that returns the probabilities for each class

- To build RNN model which predicts the article class

- To create CNN model for text classification

## Keras Text Classification

![kerastcvai001.png](./media/kerastcvai001.png)

![kerastcvai002.png](./media/kerastcvai002.png)

![kerastcvai003.png](./media/kerastcvai003.png)

![kerastcvai004.png](./media/kerastcvai004.png)

![kerastcvai005.png](./media/kerastcvai005.png)

![kerastcvai006.png](./media/kerastcvai006.png)

![kerastcvai007.png](./media/kerastcvai007.png)

![kerastcvai008.png](./media/kerastcvai008.png)

![kerastcvai009.png](./media/kerastcvai009.png)

![kerastcvai010.png](./media/kerastcvai010.png)

![kerastcvai011.png](./media/kerastcvai011.png)

![kerastcvai012.png](./media/kerastcvai012.png)

![kerastcvai013.png](./media/kerastcvai013.png)

![kerastcvai014.png](./media/kerastcvai014.png)

![kerastcvai015.png](./media/kerastcvai015.png)