In this project, I generated my own Seinfeld TV scripts using RNNs. I used part of the Seinfled dataset of scripts from 9 seasons. The Neural Network I generated a new, "fake" TV script, based on patterns it reconginzes in this training data.
- Clone the repository and navigate to the downloaded folder.
git clone https://github.com/PradeepVenna292/Udacity-Deep-Learning-Nanodegree-Project3-Generating-TV-Scripts.git
cd TV-Script-Generator
- Open the
dlnd_tv_script_generation.ipynb file. Of course, you can find HTML version of the file.
jupyter notebook dlnd_tv_script_generation.ipynb
- Read and follow the instructions! This repository already includes the dataset in a form of txt flie in
data folder.
- Get the Data
- Explore the Data
- Implement Pre-processing Functions
- Lookup Table
- Tokenize Punctuation
- Pre-process all the data and save it
- Check Access to GPU
- Input
- Batching
- Test your dataloader
- Sizes
- Values
- Build the Neural Network
- Define forward and backpropagation
- Neural Network Training
- Train Loop
- Hyperparameters
- Train
- Generate TV Script
- Generate text
- Generate a new script
| Layer |
Input Dimension |
Output Dimension |
| Embedding |
Vocab Size |
200 |
| LSTM |
200 |
256 |
| FC |
256 |
Vocab Size |
| Data Parameter |
Value |
| sequence_length |
10 |
| batch_size |
128 |
| Training Parameter |
Value |
| num_epochs |
20 |
| learning_rate |
0.001 |
| embedding_dim |
200 |
| hidden_dim |
256 |
| n_layers(Number of RNN Layers) |
2 |
The list below represents main libraries and its objects for the project.