You’re right. You want a personal roadmap for finishing the entire Micrograd++ project, not a document added to the repository.

Here is the plan I recommend following.

Micrograd++: complete implementation plan

The finish line

Micrograd++ is complete when you can:

Explain every line of the scalar autograd engine.
Build and train Neuron → Layer → MLP.
Implement reusable losses and optimizers.
Verify gradients against PyTorch.
Train an MLP on XOR.
Train a small classifier on 2D data.
Visualize a computation graph.
Explain the project clearly in your README.

GPT-2 is not part of the core Micrograd++ project. It should become the next project after this one.

Your long-term path is:

micrograd++ → makemore-style language model → transformer → GPT-2-style model

Current state

Already completed:

repository setup;
README and project image;
scalar Value class;
forward computation;
addition;
multiplication;
negation;
subtraction;
reverse addition and multiplication;
powers;
division;
tanh;
topological sorting;
automatic backpropagation;
first manual gradient test;
first GitHub commit.

Currently incomplete:

robust test suite;
ReLU and other activations;
neural-network classes;
loss classes;
optimizers;
weight initialization;
XOR example;
moons/circles example;
graph visualization;
packaging and final documentation.

The files nn.py, losses.py, optimizers.py, init.py, and activations.py are currently placeholders. That is fine. Do not implement all of them at once.

Phase 1 — Stabilize the engine

File:

micrograd/engine.py

Your current engine works, but first make the foundation complete and predictable.

Add or verify:

Value
├── data
├── grad
├── _prev
├── _op
├── _backward
├── __repr__
├── __add__
├── __mul__
├── __neg__
├── __sub__
├── __radd__
├── __rmul__
├── __rsub__
├── __pow__
├── __truediv__
├── tanh
└── backward

Add __rsub__ because this should work:

3 - a

The expression should become:

Value(3) + (-a)

Also decide whether you want to support:

a + 3
3 + a
a * 3
3 * a
a - 3
3 - a
a / 3
3 / a

Tests for this phase

Test forward values:

addition
multiplication
subtraction
division
power
tanh

Test gradients:

a + b
a * b
a*b + a
a**2
a / b
tanh(a)

Phase complete when

You can calculate these without looking at the implementation:

d = a*b + a

∂d/∂a = b + 1
∂d/∂b = a

Commit:

git commit -m "test: cover scalar engine operations and gradients"

Phase 2 — Build a real test suite

Your current test_engine.py is a manual script. Keep it for learning, but convert it into separate test functions.

Install pytest if needed:

python3 -m pip install pytest

Change the structure to something like:

def test_add_backward():
    ...


def test_mul_backward():
    ...


def test_tanh_backward():
    ...


def test_chained_graph():
    ...

Run:

python3 -m pytest -q

Add PyTorch comparisons

For each operation:

Build the graph with your Value.
Build the same graph with PyTorch.
Call backward on both.
Compare forward values.
Compare gradients.

Use float64 in PyTorch:

torch.tensor(2.0, dtype=torch.float64, requires_grad=True)

This makes the comparison more precise.

Phase complete when

Every core operator has:

one forward test
one gradient test
one edge-case test where relevant

Commit:

git commit -m "test: compare micrograd gradients with PyTorch"

Phase 3 — Add activations

Start with ReLU.

relu(x) = max(0, x)

Derivative:

1 if x > 0
0 if x <= 0

Test at:

x = -2
x = 0
x = 2

Then add:

sigmoid
leaky_relu

Add exp() and log() to Value because later losses need them.

Recommended order:

ReLU
exp
log
sigmoid
LeakyReLU

Do not start with GELU. GELU is useful later because GPT-style models use it, but it introduces more derivative and numerical details.

Phase complete when

You understand that every activation follows the same pattern:

1. calculate forward output
2. create output Value
3. record parent
4. define local derivative
5. multiply by out.grad
6. attach _backward

Commit each meaningful feature separately:

git commit -m "feat: add ReLU activation"
git commit -m "feat: add exp and log operations"
git commit -m "feat: add sigmoid and leaky ReLU"

Phase 4 — Build neural-network classes

File:

micrograd/nn.py

Build in this order:

Module
→ Neuron
→ Layer
→ MLP

`Module`

This mirrors the PyTorch idea.

It should eventually provide:

model.parameters()
model.zero_grad()

parameters() must return every trainable Value.

`Neuron`

A neuron computes:

w₁x₁ + w₂x₂ + ... + b

Then applies an activation:

activation(weighted_sum)

Start with:

tanh

Add activation selection only after the basic neuron works.

`Layer`

A layer is several neurons receiving the same input:

input → neuron 1
      → neuron 2
      → neuron 3

`MLP`

An MLP is a sequence of layers:

input → layer 1 → layer 2 → output

Example:

MLP(2, [4, 4, 1])

means:

2 inputs
→ 4 neurons
→ 4 neurons
→ 1 output

Phase complete when

You can run:

model = MLP(2, [4, 4, 1])
prediction = model([1.0, 0.0])
parameters = model.parameters()

and explain:

what __call__ does;
how inputs move through layers;
where every parameter comes from;
why each parameter is a Value.

Commit:

git commit -m "feat: add Neuron Layer and MLP modules"

Phase 5 — Add loss functions

File:

micrograd/losses.py

Start with MSE.

MSE = average((prediction - target)²)

Make it callable:

loss_fn = MSELoss()
loss = loss_fn(predictions, targets)

This is the reusable version of the loss you wrote in Karpathy’s training loop.

Then add binary cross-entropy later.

Before binary cross-entropy, make sure Value.log() works correctly.

Recommended order:

MSELoss
→ log operation
→ sigmoid output
→ BinaryCrossEntropy
→ BCEWithLogitsLoss

For this project, MSELoss is enough to train XOR initially. Cross-entropy is useful, but not required for the first successful model.

Phase complete when

You can test:

forward loss value
gradient with respect to prediction
gradient against PyTorch

Commit:

git commit -m "feat: add mean squared error loss"

Later:

git commit -m "feat: add binary cross entropy loss"

Phase 6 — Add optimizers

File:

micrograd/optimizers.py

Your current manual training update is:

for p in model.parameters():
    p.data += -learning_rate * p.grad

Turn that into an optimizer.

SGD

Implement:

optimizer.zero_grad()
optimizer.step()

This should be the first optimizer.

Momentum

Then add velocity:

velocity = momentum × old_velocity - learning_rate × gradient
parameter += velocity

Adam

Implement Adam only after SGD and momentum are understood.

Adam needs:

first moment
second moment
bias correction
epsilon
timestep

Do not copy the formula blindly. Write down what each state variable represents.

Phase complete when

You understand the difference between:

backward()

which calculates gradients, and:

optimizer.step()

which changes parameters.

Test optimizer updates against PyTorch for a single parameter.

Commit:

git commit -m "feat: add SGD optimizer"
git commit -m "feat: add SGD momentum"
git commit -m "feat: add Adam optimizer"

Phase 7 — Weight initialization

File:

micrograd/init.py

Start with simple random initialization so the network works.

Then add:

Xavier initialization
Kaiming/He initialization

Understand why initialization matters:

too-large weights can cause exploding activations;
too-small weights can cause vanishing signals;
ReLU usually pairs well with Kaiming;
tanh and sigmoid commonly pair with Xavier.

Do not worry about making this industrial-grade. This is a scalar educational engine.

Phase complete when

Neuron can choose an initialization method:

Neuron(nin=4, init="xavier")
Neuron(nin=4, init="kaiming")

Commit:

git commit -m "feat: add Xavier and Kaiming initialization"

Phase 8 — Train XOR

File:

examples/01_xor.py

Dataset:

[0, 0] → 0
[0, 1] → 1
[1, 0] → 1
[1, 1] → 0

Use:

MLP
MSELoss
SGD or Adam
tanh or ReLU

Training loop:

1. forward pass
2. calculate predictions
3. calculate loss
4. zero gradients
5. backward
6. optimizer step
7. print loss

The loss should decrease.

Do not worry if it does not reach exactly zero immediately. Investigate:

learning rate;
initialization;
activation;
model size;
whether gradients are reset;
whether parameters are actually updated.

Phase complete when

The model predicts approximately:

and the loss clearly decreases.

Save a loss plot:

assets/xor-loss.png

Commit:

git commit -m "feat: train an MLP on XOR"

This is the first major finish milestone.

Phase 9 — Train a 2D classifier

File:

examples/02_moons.py

Use a small dataset such as:

make_moons

or:

make_circles

The goal is to prove that your engine can learn a nonlinear boundary.

Steps:

create 2D points;
split training and test data;
build an MLP;
train with binary loss;
calculate accuracy;
plot the decision boundary.

Save:

assets/moons-boundary.png

Phase complete when

You have:

training loss curve
test accuracy
decision-boundary plot

Commit:

git commit -m "feat: classify two-dimensional moons data"

Phase 10 — Computational graph visualization

File:

viz/graph.py

Port the graph drawing idea from Karpathy.

Given a final Value, show:

node data
node gradient
operation
edges

Test it with:

a = Value(2.0)
b = Value(3.0)
c = (a * b).tanh()
c.backward()

Then save a graph image for the README.

This phase is useful because it connects the code to the mental model you learned in the lecture.

Commit:

git commit -m "feat: visualize scalar computation graphs"

Phase 11 — Clean API and project structure

Now review the repository as a library.

Add or improve:

pyproject.toml
requirements.txt

Make sure:

from micrograd.engine import Value
from micrograd.nn import MLP
from micrograd.losses import MSELoss
from micrograd.optimizers import SGD

works from the project root.

Remove unused placeholder confusion or clearly mark future modules as planned.

Add:

__all__

only if it genuinely improves imports. Do not add abstractions just to look professional.

Add quality checks

Run:

python3 -m pytest
python3 -m py_compile micrograd/engine.py
git diff --check

Optional later:

ruff
mypy
GitHub Actions

These are polish, not the learning core.

Commit:

git commit -m "chore: polish package structure and development checks"

Phase 12 — Final README

Update the README only with features that actually work.

It should show:

why the project exists
what currently works
how to install it
how to run tests
how to run XOR
how gradients are verified
limitations
future work

Include screenshots of:

XOR loss curve
decision boundary
computation graph
pytest output

Be honest about the main limitation:

This is a scalar educational engine, so it is intentionally much slower and smaller than PyTorch.

That limitation is part of the point.

The final definition of done

Call Micrograd++ complete when all of these are true:

[ ] Value engine works
[ ] Automatic backward works
[ ] Core operators are tested
[ ] Activations are tested
[ ] Neuron works
[ ] Layer works
[ ] MLP works
[ ] MSE loss works
[ ] SGD works
[ ] Adam works or is clearly marked optional
[ ] Model parameters can be collected
[ ] Gradients can be reset
[ ] XOR trains successfully
[ ] A 2D classifier trains successfully
[ ] Computational graphs can be visualized
[ ] README explains the project
[ ] Tests pass from a clean checkout
[ ] Git history shows incremental work

You do not need MNIST to call the project complete. You do not need GELU, Adam, or CrossEntropy to prove that you understand the core. They are extensions.

Your working rhythm

For every feature, follow this loop:

1. Learn the math
2. Explain the idea in plain language
3. Write the smallest forward example
4. Add the local backward rule
5. Test the gradient by hand
6. Compare with PyTorch
7. Add the feature to the project
8. Run all tests
9. Commit
10. Write down what is clear and what is fuzzy

Use one commit per meaningful feature:

feat: add ReLU activation
test: compare tanh gradient with PyTorch
feat: add MLP modules
feat: add SGD optimizer
feat: train XOR example

At the beginning of each session:

git status
git log --oneline -5
python3 -m pytest -q

At the end:

git diff --check
python3 -m pytest -q
git status

What you should do next

Your immediate next phase is not nn.py.

First convert the current manual test into proper tests and add coverage for:

addition
multiplication
chained graph
tanh
power
division

Then add __rsub__.

After those tests pass, implement ReLU.

Only after ReLU and the test suite are stable should you begin Neuron → Layer → MLP.

This roadmap gives you a complete path without turning Micrograd++ into an unfinished imitation of PyTorch.

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Implementation.md

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File metadata and controls

Micrograd++: complete implementation plan

The finish line

Current state

Phase 1 — Stabilize the engine

Tests for this phase

Phase complete when

Phase 2 — Build a real test suite

Add PyTorch comparisons

Phase complete when

Phase 3 — Add activations

Phase complete when

Phase 4 — Build neural-network classes

Module

Neuron

Layer

MLP

Phase complete when

Phase 5 — Add loss functions

Phase complete when

Phase 6 — Add optimizers

SGD

Momentum

Adam

Phase complete when

Phase 7 — Weight initialization

Phase complete when

Phase 8 — Train XOR

Phase complete when

Phase 9 — Train a 2D classifier

Phase complete when

Phase 10 — Computational graph visualization

Phase 11 — Clean API and project structure

Add quality checks

Phase 12 — Final README

The final definition of done

Your working rhythm

What you should do next

`Module`

`Neuron`

`Layer`

`MLP`