https://github.com/wittyicon29/text-generation-using-tensorflow-rnn-lstm-
The model uses LSTMs to generate text and also is a good example for getting started into RNNs and LSTMs. The model is trained on the publicly available dataset called Shakespeare.
https://github.com/wittyicon29/text-generation-using-tensorflow-rnn-lstm-
deep-learning lstm machine-learning rnn tensorflow
Last synced: 2 months ago
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The model uses LSTMs to generate text and also is a good example for getting started into RNNs and LSTMs. The model is trained on the publicly available dataset called Shakespeare.
- Host: GitHub
- URL: https://github.com/wittyicon29/text-generation-using-tensorflow-rnn-lstm-
- Owner: wittyicon29
- Created: 2023-01-28T12:11:54.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-01-28T12:22:13.000Z (over 3 years ago)
- Last Synced: 2025-06-01T00:04:36.321Z (about 1 year ago)
- Topics: deep-learning, lstm, machine-learning, rnn, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 77.1 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Text-Generation-using-TensorFlow-
TensorFlow is an open-source machine learning library that can be used for text generation.
One way to generate text using TensorFlow is to train a language model on a large dataset of text and then use the trained model to generate new text. This can be done using a technique called "text generation" or "language modeling."
The process typically involves training a recurrent neural network (RNN) on the dataset and then using the trained RNN to generate new text. The quality of the generated text will depend on the quality of the training dataset and the architecture of the RNN.
Text generation models in TensorFlow using Long Short-Term Memory (LSTM) work by training a recurrent neural network (RNN) on a large dataset of text. An LSTM is a type of RNN that is able to remember previous inputs for a longer period of time, which makes it well-suited for tasks such as text generation where context is important.
The training process involves feeding the LSTM sequential chunks of text, called input sequences, one at a time and training it to predict the next word in the sequence. Once the LSTM is trained, it can be used to generate new text by feeding it an initial sequence and repeatedly predicting the next word in the sequence until the desired length of text is generated.
The LSTM network is composed of several layers of LSTM cells. Each LSTM cell has a set of learnable weights, which are updated during training. The input sequence is passed through the LSTM layers, and the output of the final layer is used to predict the next word in the sequence. The prediction is compared with the actual next word in the sequence, and the weights are updated to reduce the difference between the prediction and the actual next word. This process is repeated for each word in the input sequence, and the weights are updated after each sequence.
In summary, LSTM text generation models in TensorFlow work by training a recurrent neural network on a large dataset of text, using the LSTM cell structure to remember previous inputs for a longer period of time and then generate new text by predicting the next word in the sequence based on the previous inputs and learned weights.
The dataset for the following model is taken from the TensorFlow website . The link for the same is https://www.tensorflow.org/text/tutorials/text_generation