Udacity-TV-Script-Project

The Jupyter-Notebook-File dlnd_tv_script_generation.ipynb includes my solution to the Generate-TV-Scripts-Project in the Deeplearning-Nanodegree.

General Task

The task is to use a neural network to generate an artificial Seinfeld TV script.

Approach

The idea is to attack this task by training a recurrent neural network feeding it a dataset of Seinfeld TV scripts from nine seasons. After training, the functionality of the model will be as follows: After inputting a word to the model, it will generate a whole script of a specified length word by word.

All of the code is written in python and the pytorch library is used to implement the recurrent neural network.

Architecture of the Model and Hyperparameters

The network is a recurrent neural network consisting of two hidden layers with LSTM cells, an embedding layer at the beginning and a fully connected layer at the end. The optimizer for the backpropagation process is an Adam optimizer.

Here are the chosen hyperparameters:

• Sequence Length: 10
• Batch Size: 128
• Embedding Dimension: 400
• Dimension of hidden States: 256
• Epochs: 15
• Learning Rate: 0.001