v0.1.0

📦 Release: v0.1.0 – Initial Working Version

This is the first official release of PyCNN — a lightweight and educational CNN built using only NumPy, SciPy, and Pillow, with zero reliance on machine learning frameworks.

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🚀 Key Features

• ✅ Fully functional CNN implementation from scratch
• 🧠 Manual convolution, max pooling, and ReLU activations
• 🔁 Forward and backward propagation with mini-batch gradient descent
• 🏷 Multi-class classification via softmax and cross-entropy loss
• 💾 Model save/load using pickle
• 🖼 RGB image preprocessing with customizable filters
• 🔍 Predict function to classify new unseen images

📌 Installation

📦 install directly from GitHub:

pip install git+https://github.com/77AXEL/PyCNN.git@v0.1.0

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🧪 How to Use

🏁 Train the model:

from cnnfs.model import CNN

model = CNN()
model.init(
    image_size=32,
    batch_size=32,
    h1=128,
    h2=64,
    learning_rate=0.01,
    epochs=10,
    dataset_path="data",
    max_image=200 
)
model.load_dataset()
model.train_model()
model.save_model()

🔍 Predict an image:

model.load_model("model.bin")
result = model.predict("path/to/image.png")
print("Prediction:", result)

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🗂 Included Filters

PyCNN uses a set of basic filters for feature extraction:

• Sharpen
• Vertical edges
• Laplacian (high-pass)

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🧾 Changelog

• Initial version with full training and prediction pipeline
• Manual convolution + pooling + ReLU
• Batch softmax + cross-entropy loss
• Two hidden layers and output layer
• Save/load model capability
• Example dataset folder included
• Beginner-friendly structure for learning how CNNs work

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💬 Feedback & Contributions

We welcome issues, suggestions, and contributions!
Join the conversation in the [Discussions tab](https://github.com/77AXEL/PyCNN/discussions) or check the [CONTRIBUTING.md](./CONTRIBUTING.md) guide.

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🛡 Security

Found a security issue? Please report it privately to:
📧 a.x.e.l777444000@gmail.com

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📜 License

Released under the MIT License