manupawar6388/Chessman_image-DCGAN

Chessman DCGAN - Chess Piece Generator

This is a Deep Convolutional Generative Adversarial Network (DCGAN) trained to generate images of chess pieces.

Model Description

• Architecture: DCGAN (Deep Convolutional GAN)
• Framework: TensorFlow/Keras
• Input: 100-dimensional random noise vector
• Output: 64x64 RGB images of chess pieces
• Training Data: 552 images from the Chessman Image Dataset (6 chess piece types)

Training Details

• Epochs: 50
• Batch Size: 128
• Optimizer: Adam (lr=1e-4, beta_1=0.5)
• Loss: Binary Cross-Entropy with label smoothing
• Data Augmentation: Random flips, rotations, and zoom

Model Architecture

Generator

• Input: 100-dim latent vector
• Dense layer → 8×8×256
• Conv2DTranspose layers: 256→128→64→3
• Output: 64×64×3 RGB image

Discriminator

• Input: 64×64×3 RGB image
• Conv2D layers: 64→128→256
• Output: Binary classification (real/fake)

Usage

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

# Load the model
generator = tf.keras.models.load_model('dcgan_generator.keras')

# Generate random noise
noise = tf.random.normal([1, 100])

# Generate image
generated_image = generator(noise, training=False)

# Display
img = ((generated_image[0, :, :, :] * 127.5) + 127.5).numpy().astype("uint8")
plt.imshow(img)
plt.axis('off')
plt.show()

Limitations

• Images are 64×64 resolution (relatively low)
• Model trained on only 552 images (small dataset)
• Generated pieces may not always be perfectly recognizable
• No control over which piece type is generated

Citation

Dataset: Chessman Image Dataset on Kaggle

License

MIT License