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 *.zip filter=lfs diff=lfs merge=lfs -text
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LICENSE

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+MIT License
+
+Copyright (c) 2023 Hussam Alafandi
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+tags:
+  - dcgan
+  - generative-adversarial-network
+  - celeba
+  - image-generation
+  - deep-learning
+datasets:
+  - CelebA
+license: mit
+---
+
+# DCGAN Model Card
+
+## Model Description
+This is a Deep Convolutional Generative Adversarial Network (DCGAN) trained on the CelebA dataset to generate realistic 64x64 RGB images of human faces. The model was developed as part of the Generative AI course.
+
+## Training Details
+- **Dataset**: CelebA
+- **Subset Size**: 50,000 images
+- **Image Size**: 64x64
+- **Number of Channels**: 3 (RGB)
+- **Latent Dimension**: 100
+- **Generator Feature Map Size**: 64
+- **Discriminator Feature Map Size**: 64
+- **Batch Size**: 128
+- **Epochs**: 50
+- **Learning Rate**: 0.0002
+- **Beta1**: 0.5
+- **Weight Decay**: 0
+- **Optimizer**: Adam
+- **Hardware**: CUDA-enabled GPU
+- **Logging**: Weights and Biases (wandb)
+
+### Weights and Biases Run
+
+The training process was tracked using [Weights and Biases](https://wandb.ai). You can view the full training logs and metrics [here](https://wandb.ai/hussam-alafandi/DCGAN_CelebA/runs/32qhixp1?nw=nwuserhussamalafandi).
+
+## Usage
+
+### Loading the Model
+
+To load the trained model, use the following code snippet:
+```python
+import torch
+from dcgan import Generator
+
+# Load the configuration
+config = {
+    "latent_dim": 100,
+    "ngf": 64,
+    "nc": 3
+}
+
+# Initialize the generator
+generator = Generator(config)
+
+# Load the trained weights
+model_path = "./dcgan_celeba.pth"
+
+generator.load_state_dict(torch.load(checkpoint_path, map_location=torch.device('cuda' if torch.cuda.is_available() else 'cpu')))
+
+# Set the model to evaluation mode
+generator.eval()
+
+# Example: Generate an image
+latent_vector = torch.randn(1, config["latent_dim"], 1, 1)  # Batch size of 1
+
+if torch.cuda.is_available():
+    latent_vector = latent_vector.cuda()
+    generator = generator.cuda()
+
+generated_image = generator(latent_vector)
+```
+
+## Example Results
+
+![generate image](./gan_celeba.png)
+
+## Resources
+- **Course Repository**: [Generative AI Course](https://github.com/hussamalafandi/Generative_AI)
+- **WandB Run**: [DCGAN_CelebA Run](https://wandb.ai/hussam-alafandi/DCGAN_CelebA/runs/32qhixp1?nw=nwuserhussamalafandi)

dcgan.py

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+from torch import nn
+
+class Generator(nn.Module):
+    def __init__(self, config):
+        super(Generator, self).__init__()
+        
+        self.latent_dim = config["latent_dim"]
+        self.ngf = config["ngf"]
+        self.nc = config["nc"]
+
+        # DCGAN generator architecture
+        self.main = nn.Sequential(
+
+            # Input: latent vector Z (batch_size, latent_dim, 1, 1)
+            nn.ConvTranspose2d(self.latent_dim, self.ngf * 8, 4, 1, 0, bias=False),
+            nn.BatchNorm2d(self.ngf * 8),
+            nn.ReLU(True),
+
+            # State: (ngf*8) x 4 x 4
+            nn.ConvTranspose2d(self.ngf * 8, self.ngf * 4, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(self.ngf * 4),
+            nn.ReLU(True),
+
+            # State: (ngf*4) x 8 x 8
+            nn.ConvTranspose2d(self.ngf * 4, self.ngf * 2, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(self.ngf * 2),
+            nn.ReLU(True),
+
+            # State: (ngf*2) x 16 x 16
+            nn.ConvTranspose2d(self.ngf * 2, self.ngf, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(self.ngf),
+            nn.ReLU(True),
+
+            # State: (ngf) x 32 x 32
+            nn.ConvTranspose2d(self.ngf, self.nc, 4, 2, 1, bias=False),
+            nn.Tanh()
+            # Output: (nc) x 64 x 64
+        )
+    
+    def forward(self, input):
+        return self.main(input)
+
+class Discriminator(nn.Module):
+    def __init__(self, config):
+        super(Discriminator, self).__init__()
+
+        self.ndf = config["ndf"]
+        self.nc = config["nc"]
+
+        # DCGAN discriminator architecture
+        self.main = nn.Sequential(
+
+            # Input: (nc) x 64 x 64
+            nn.Conv2d(self.nc, self.ndf, 4, 2, 1, bias=False),
+            nn.LeakyReLU(0.2, inplace=True),
+
+            # State: (ndf) x 32 x 32
+            nn.Conv2d(self.ndf, self.ndf * 2, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(self.ndf * 2),
+            nn.LeakyReLU(0.2, inplace=True),
+
+            # State: (ndf*2) x 16 x 16
+            nn.Conv2d(self.ndf * 2, self.ndf * 4, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(self.ndf * 4),
+            nn.LeakyReLU(0.2, inplace=True),
+
+            # State: (ndf*4) x 8 x 8
+            nn.Conv2d(self.ndf * 4, self.ndf * 8, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(self.ndf * 8),
+            nn.LeakyReLU(0.2, inplace=True),
+            
+            # State: (ndf*8) x 4 x 4
+            nn.Conv2d(self.ndf * 8, 1, 4, 1, 0, bias=False),
+            nn.Sigmoid()
+        )
+    
+    def forward(self, input):
+        return self.main(input).view(-1, 1).squeeze(1)

dcgan_celeba.pth

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+size 25405907