Initial commit

Add project files to remote repository.

README.md

@@ -0,0 +1,34 @@
+# Udacity project Face Generator using CelebA
+This project was part of the [Udacity Deep Learning Nano Degree Program](https://www.udacity.com/course/deep-learning-nanodegree--nd101) 
+that was teaching basics on building Generative Adversarial Networks (GANs).
+
+In this project, we did build and train a custom GAN architecture on the CelebA dataset, 
+leveraging the different skills learned during the course mentioned above.
+
+## Model Architecture
+### Deep Convolutional GANs
+In this notebook, I've chosen to apply the DCGAN architecture to build a GAN using convolutional layers in the generator and discriminator. 
+This is called a Deep Convolutional GAN, or DCGAN for short. 
+
+The DCGAN architecture was first explored in 2016 and has seen impressive results in generating new images. 
+You can read the original paper, [here](https://arxiv.org/pdf/1511.06434.pdf).
+
+Next to using Convolutional layers, I used label smoothing technique for the discriminator and the
+'Two Times Update Rule' for the genrator part of the model.  
+
+## Data Set
+You’ll be training DCGAN on the [Large-scale CelebFaces Attributes (CelebA) Dataset](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset. 
+
+These are color images of celebrities. In this course, Udacity provides a smaller subset of this data set with in total 32,600 pre-processed images.
+More details are provided by Udacity in the Jupyter notebook `face_generator.ipynb`.
+
+# Getting Started
+So, our goal is to create a DCGAN that can generate new, realistic-looking images. 
+We’ll go through the following steps:
+1. Extract the prepared subset of the celeb data set
+2. Define discriminator and generator networks
+3. Train the DCGAN network
+4. Visualize the loss over time and some sample, generated images
+
+## Requirements
+It is recommended to train the model on GPU.

requirements.txt

@@ -0,0 +1,5 @@
+matplotlib==3.4.3
+numpy==1.21.4
+Pillow==9.0.0
+torch==1.10.0
+torchvision==0.11.1

tests.py

@@ -0,0 +1,28 @@
+import numpy as np
+import torch
+from torch.utils.data import DataLoader, Dataset
+
+
+def check_dataset_outputs(dataset: Dataset):
+    assert len(dataset) == 32600, 'The dataset should contain 32,600 images.'
+    index = np.random.randint(len(dataset))
+    image = dataset[index]
+    assert  image.shape == torch.Size([3, 64, 64]), 'You must reshape the images to be 64x64'
+    assert image.min() >= -1 and image.max() <= 1, 'The images should range between -1 and 1.'
+    print('Congrats, your dataset implementation passed all the tests')
+
+
+def check_discriminator(discriminator: torch.nn.Module):
+    images = torch.randn(1, 3, 64, 64)
+    score = discriminator(images)
+    print(score.shape)
+    assert score.shape == torch.Size([1, 1, 1, 1]), 'The discriminator output should be a single score.'
+    print('Congrats, your discriminator implementation passed all the tests')
+
+
+def check_generator(generator: torch.nn.Module, latent_dim: int):
+    latent_vector = torch.randn(1, latent_dim, 1, 1)
+    image = generator(latent_vector)
+    assert image.shape == torch.Size([1, 3, 64, 64]), 'The generator should output a 64x64x3 images.'
+    print('Congrats, your generator implementation passed all the tests')
+