Fashion GAN is a Python code repository that implements a Generative Adversarial Network (GAN) for generating fashion images using TensorFlow. GANs are a type of deep learning model composed of two competing networks: a generator and a discriminator. The generator learns to create realistic images, while the discriminator learns to distinguish between real and generated images. This adversarial training process leads to the generation of high-quality images.
## Installation
Install the required dependencies:
```bash
pip install tensorflow tensorflow_datasets matplotlib- Import the required libraries:
import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np
from matplotlib import pyplot as plt- Set up GPU memory growth:
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)- Load the Fashion MNIST dataset:
ds = tfds.load('fashion_mnist', split='train')- Perform data transformations:
def scale_images(data):
image = data['image']
return image / 255
ds = ds.map(scale_images)- Build the generator model:
def build_generator():
# Model architecture here
generator = build_generator()- Build the discriminator model:
def build_discriminator():
# Model architecture here
discriminator = build_discriminator()- Train the FashionGAN model:
class FashionGAN(Model):
# Model implementation here
fashgan = FashionGAN(generator, discriminator)
fashgan.compile(g_opt, d_opt, g_loss, d_loss)
hist = fashgan.fit(ds, epochs=20, callbacks=[ModelMonitor()])- Generate images using the trained generator:
imgs = generator.predict(tf.random.normal((16, 128, 1)))