/my_first_gan

"Generative Adversarial Network (GAN) for digit '0' generation from MNIST."

Primary LanguagePython

The provided code implements a Generative Adversarial Network (GAN) to generate images of the digit '0' from the MNIST dataset. Let's break down the code and understand each part:

  1. Importing Libraries:

    • The code imports necessary libraries such as tqdm for progress bars, numpy for numerical operations, TensorFlow and Keras for building and training deep learning models.

  2. Loading Data:

    • The MNIST dataset is loaded using mnist.load_data(), which returns training and testing data along with their respective labels.

  3. Preprocessing Data:

    • Only images of the digit '0' are extracted from the training set and stored in X_train.

  4. Creating Discriminator:

    • A sequential model representing the discriminator is created. It consists of three dense layers with ReLU activation functions and a final sigmoid layer to output the probability of the input being real.
    • The discriminator is compiled with binary cross-entropy loss and the Adam optimizer.

  5. Creating Generator:

    • A sequential model representing the generator is created. It takes random noise as input and generates images of the digit '0'.
    • The generator consists of two dense layers with ReLU activation functions and a reshape layer to transform the output into the shape of an image.

  6. Combining Generator and Discriminator to Form GAN:

    • The generator and discriminator are combined sequentially to form the GAN model.
    • The discriminator is set to non-trainable since it will only be trained in conjunction with the generator.
    • The GAN is compiled with binary cross-entropy loss and the Adam optimizer.

  7. Training:

    • The dataset is converted into a TensorFlow dataset for efficient processing.
    • The training loop runs for a specified number of epochs.
    • In each epoch, for each batch in the dataset, the discriminator and generator are alternatively trained.
    • For the discriminator training, it is first trained on real images with labels indicating they are real, then on fake images generated by the generator with labels indicating they are fake.
    • For the generator training, it generates fake images and tries to fool the discriminator by labeling them as real.

  8. Saving the Generator Model:

    • After training, the generator model is saved to a file named "generator.h5".

Overall, this code implements a basic GAN architecture for generating images of the digit '0' from the MNIST dataset.