image-creation-from-text-

Le but de ce projet est de former un réseau antagoniste génératif pour générer des images à partir d'une description textuelle.

• Construction de modèles génératifs en utilisant des réseaux antagonistes génératifs (DCGAN)
• Construction d'un modèle discriminateur (Discriminator model)
• Développement d'un algorithme de training, qui crée les images, calcule la perte et ajuste les gradients.

The notebook has 5 parts:

- Data Pre-processing:

- Converting the images into numpy arrays based on the pre-set pixel size and storing them.
- Converting the image description into embeddings and storing them.
- Sotring the captions in a csv file.

- Loading and Combining:

- Loading all the images' numpy arrays and appending it to form the image data.
- Loading the image description embedding numpy array.

- Data modeling:

- Creating the discrimintor and generator networks.
- Functions for calculating the discriminator and generator loss.
- Setting the optimizer and learning rate.

- Training:

- Function for the train step, that creates the images, calculates the loss and adjusts the gradients.
- Function for the train, it fetches the batch data and passes it to train_step and collects all the metrics.

- Results:

- Function for testing the output from the generator given an input noise and a caption