Neural Network Framework

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 Project 6 

From First Principles to Your First Real Neural Network Framework

In Section 1, you built every piece of a neural network by hand:  dot products, activations, gradients, multi‑feature regression, logistic regression, and even multi‑layer backprop for XOR. You’ve seen how neuron networks  stack layers, and how gradients flow backward through the entire system.

Now we’re ready for the next step.


In the real world, nobody trains neural networks by manually writing out gradients or wiring layers together. Instead, we use frameworks like PyTorch and TensorFlow, tools that package all the math you’ve learned into clean, modular components.


Before we jump into those frameworks, we’re going to build our own.

Why?


Because once you understand how a framework works on the inside, learning PyTorch becomes effortless. You’ll recognize every concept, layers, modules, activations, loss functions, training loops; because you built them yourself.


Project 6 is where everything from Section 1 comes together.


 We’ll turn the math you’ve mastered into a real, modular neural‑network framework:


  • DenseLayer : the matrix math from Project 2

  • SequentialModel : the stacked architecture from Project 4

  • Activation functions : the nonlinearities from Project 3

  • Loss functions : the objectives from Projects 1–3

  • Trainer : the learning loop you’ve been building since Project 1

  • Saving + loading : the workflow used in real ML systems

  • Inference : making predictions with a trained model


By the end of this project, you won’t just understand neural networks, you’ll have built a framework that looks and feels like a tiny version of PyTorch or TensorFlow.


This is the bridge between:


  • Section 1 - math‑first learning

  • Section 2 - real neural network engineering.


Let’s build a machine that uses everything you’ve learned.


Before we write any code, let’s map out the architecture of the framework.
Just like any good code, each file has a single responsibility.

File Structure Overview

├── activations.py

├── losses.py

├── layers.py

├── model.py

├── trainer.py

├── saved_models.py

├── inference.py

└── data.py


Each file plays a specific role in the system:

layers.py

  • Base Layer class

  • DenseLayer (fully connected layer)

  • Weight initialization

  • Forward pass

  • Backward pass

  • Gradient computation

This is the mathematical heart of the network.

activations.py

  • ReLU

  • Sigmoid

  • Tanh

  • Derivatives for each

These functions introduce nonlinearity — the key to solving problems like XOR.

model.py

  • SequentialModel container

  • Runs forward pass through all layers

  • Runs backward pass in reverse

  • Provides .predict() and .summary()

This is your PyTorch‑style nn.Sequential.

trainer.py

  • Mini‑batch training

  • Forward pass

  • Loss computation

  • Backpropagation

  • Parameter updates

  • Evaluation

This is your training engine — the equivalent of PyTorch’s training loop + optimizer.

losses.py

  • MSE

  • Binary cross‑entropy

  • Softmax cross‑entropy

These functions tell the network how wrong it is.

saved_models.py

  • Save model weights

  • Load model weights

This mirrors PyTorch’s state_dict() workflow.

inference.py

  • Rebuild a model

  • Load saved weights

  • Run predictions on new data

This separates training from deployment — just like real ML systems.

Putting It All Together

By the end of this project, you’ll be able to write:

model = SequentialModel([

    DenseLayer(2, 6, relu, relu_deriv),

    DenseLayer(6, 3, relu, relu_deriv),

    DenseLayer(3, 1, sigmoid, sigmoid_deriv)

])


Then train it:

trainer = Trainer(model, binary_cross_entropy, binary_cross_entropy_deriv, lr=0.01)

trainer.train(X_train, y_train, epochs=2000, batch_size=4)


Evaluate it:

loss, acc = trainer.evaluate(X_test, y_test, classification=True)


Save it:

save_model(model, "model.pkl")


And run inference:

prediction = predict([5, 7])


This is the exact workflow used in real deep‑learning frameworks — and you’re about to build it from scratch.

What’s Next (Part 2 Preview)

In Part 2, we’ll start implementing the framework piece by piece:

  • Build DenseLayer

  • Build SequentialModel

  • Implement activations and losses

  • Write the training loop

  • Connect everything into a working system

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