README.md

GAN Generative Adversial Network :

its a type of neural network that used in machine learning to generate new data that looks similar to a given data.

it consists of two main components:

1 Generator: its a neural network takes random noise(vector of random numbers) as input and tries to generate data that looks realistic enough.

2 Discriminator: its also a neural network , it takes both real data from the dataset and fake data from the generator as input and tries to do a binary classification, if it as real or fake.

These two networks are compeeting to win if we could say, at some point, the generator gets so good at creating realistic data that the discriminator wont descriminate .

applications of GAN :

                  -Image to Image translation

                  -Image to Text / Text to Image translation
                  
                  -Deep Fake

GANs Don’t Work Well for Text ?

GANs perform well with continuous data, like images, because the generator can create smooth, flowing values ( for example : pixel intensities), but when it comes to text and words; it cant perform good results beacus the input is discrete valuse , and this causes two main issues:

1.Hard to Learn with Words:

GANs learn by making smooth and continious changes, but with text, it has to pick exact and precise words, which prevents smooth transitions

2.Sentences Have Order and Meaning:

GANs aren’t designed to understand grammar or how sentences should flow so its struggle with text, becuz the order of words is important: sentences need to follow a structure to make sense.

THATS WHY >>> Researchers suggest various architectures to handle text generation with GANs, including models that combine RNNs or LSTMs with adversarial training, enabling to improv coherence and structure in the faked text.

one such intersting architecture is SeqGAN

what's a SeqGAN ?

SeqGAN (Sequence Generative Adversarial Network) is a model designed for generating high-quality sequences, such as text, by combining the adversarial framework of GANs with sequence modeling. It consists of two components: a generator that creates sequences and a discriminator that evaluates their quality. The generator is trained using reinforcement learning, where it receives rewards from the discriminator based on how realistic the generated sequences are, These rewards help guide the generator to improve over time, enabling it to produce more coherent and contextually accurate sequences.

I will be implementing here, a SeqGAN for text generation as part of my M2 advanced NLP project.

step 01: preprocesing and data preparing :

iam using data from Project Gutenberg :

1. the preprocessing :

• i put everything in lower case.
• replace spaces with only one space .
• i will keep stopwords and some punctuations because they are useful here.

2. tokenization
3. create vocabulary and to give each word of the vocabulary a unique ID (token to ID this nu,bers dont have any semantic meaning) , why? , because the generator take numirical values as inputs (>>>its needed for the embeding layer.)
4. convert sentences into sequences of integers based on vocabulary, (all sequences must have the same lenght)

5.split data for training and testing

step 02: define the Generator :