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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:
+- gan
+- mnist
+- pytorch
+- generative-model
+- deep-learning
+license: mit
+datasets:
+- mnist
+library_name: pytorch
+---
+
+# GAN for MNIST Digit Generation
+
+This repository contains a Generative Adversarial Network (GAN) trained on the MNIST dataset to generate realistic handwritten digits. The model was trained as part of the Generative AI course.
+
+## Model Details
+
+- **Model Type**: GAN
+- **Dataset**: MNIST (handwritten digits)
+- **Generator Input**: Latent vector of size 100
+- **Output**: 28x28 grayscale images
+- **Framework**: PyTorch
+
+## Training Details
+
+- **Optimizer**: Adam
+- **Learning Rate**: 0.0002
+- **Beta1**: 0.5
+- **Epochs**: 50
+- **Batch Size**: 64
+- **Weight Decay**: 0.0001
+- **Logging**: [Weights & Biases](https://wandb.ai/hussam-alafandi/GAN_MNIST/runs/6ehnzhm0?nw=nwuserhussamalafandi)
+
+## Usage
+
+### Loading the Model
+
+To load the trained model, use the following code snippet:
+   ```python
+   from gan import Generator
+   import torch
+
+   latent_dim = 100
+   generator = Generator(latent_dim)
+   generator.load_state_dict(torch.load("./gan_mnist.pth"))
+   generator.eval()
+
+   # Generate samples
+   z = torch.randn(16, latent_dim)
+   samples = generator(z)
+   ```
+## Example Results
+![generated images](./gan_mnist.png)
+
+## References
+
+- [Generative AI Course Repository](https://github.com/hussamalafandi/Generative_AI)
+- [Weights & Biases Training Logs](https://wandb.ai/hussam-alafandi/GAN_MNIST/runs/6ehnzhm0?nw=nwuserhussamalafandi)
+
+## License
+
+This project is licensed under the MIT License.

gan.py

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+from torch import nn
+
+class Generator(nn.Module):
+    def __init__(self, latent_dim):
+        super(Generator, self).__init__()
+        self.latent_dim = latent_dim
+        self.net = nn.Sequential(
+            nn.Linear(self.latent_dim, 128),
+            nn.ReLU(True),
+            nn.Linear(128, 256),
+            nn.BatchNorm1d(256),
+            nn.ReLU(True),
+            nn.Linear(256, 512),
+            nn.BatchNorm1d(512),
+            nn.ReLU(True),
+            nn.Linear(512, 28*28),
+            nn.Tanh()
+        )
+
+    def forward(self, z):
+        img = self.net(z)
+        img = img.view(z.size(0), 1, 28, 28)
+        return img
+
+
+class Discriminator(nn.Module):
+    def __init__(self):
+        super(Discriminator, self).__init__()
+        self.net = nn.Sequential(
+            nn.Linear(28*28, 512),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Linear(512, 256),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Linear(256, 1),
+            nn.Sigmoid()
+        )
+
+    def forward(self, img):
+        img_flat = img.view(img.size(0), -1)
+        validity = self.net(img_flat)
+        return validity

gan_mnist.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:21a826687c374ae29109ff7916b70ecfc530524dd0aa9a8c21621521880f8c21
+size 4477638