https://github.com/realtristan/micronet

Neural network library from scratch
https://github.com/realtristan/micronet

neural-networks numpy python pytorch

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Neural network library from scratch

• Host: GitHub
• URL: https://github.com/realtristan/micronet
• Owner: realTristan
• Created: 2024-06-11T15:55:54.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-15T13:33:43.000Z (5 months ago)
• Last Synced: 2024-06-16T14:44:43.342Z (5 months ago)
• Topics: neural-networks, numpy, python, pytorch
• Language: Python
• Homepage:
• Size: 25.4 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# micronet

Welcome to micronet!

### What is micronet?

Micronet is a `PyTorch` syntax inspired neural network python library! 

Andrej Karpathy's `micrograd` inspired the actual creation of this library. His video

`The spelled-out intro to neural networks and backpropagation: building micrograd` really

helped me grasp the concepts of neural networks, allowing me to build `micronet` on my own!

### Example

This is an example output from running the `main.py` script.

#### Imports

The imports used in the example!

```python3

from engine.nn.module import Module

from engine.nn.linear import Linear

from engine.nn.activation import ReLU

from engine.loss import MSELoss

```

#### Model Class

Defining our model!

```python3

class MLPModel(Module):

    def __init__(self) -> None:

        super(MLPModel, self).__init__([

            Linear(3, 128),

            ReLU(),

            Linear(128, 16),

            ReLU(),

            Linear(16, 1),

            ReLU(),

        ])

```

#### Model, Criterion, Hyperparameters, & Data

The model initialization, loss function, hyperparameters, and the data that was used!

```python3

## Model, Criterion, and Hyperparameters

model = MLPModel()

criterion = MSELoss()

lr = 0.1

epochs = 100

## Train data and corresponding labels

train_data = [

    [1.70, 70, 1],

    [1.60, 50, 0],

    [1.80, 80, 1],

    [1.85, 90, 1],

    [1.75, 75, 0],

    [1.65, 55, 0],

]

train_labels = [25, 20, 30, 35, 27, 22]

## Test data and corresponding labels

test_data = [

    [1.75, 80, 1],

    [1.65, 55, 0],

]

test_labels = [30, 22]

```

#### Training & Testing Loops

The actual loops used to train and then test the network!

```python3

##

## Train the network

##

for epoch in range(epochs):

    for x, y in zip(train_data, train_labels):

        model.zero_grad()

        y_pred = model(x)

        criterion(y, y_pred)

        criterion.backward()

    model.update(lr)

    print(f"Epoch {epoch}, Loss: {criterion}")

##

## Test the network

##

print("\nTest")

for i, x in enumerate(test_data):

    y = model(x)

    print(f"Prediction: {y.data}, Actual: {test_labels[i]}")

```

#### Terminal Output

The output when all of the above code is put together!

```bash

Test

Prediction: 31.31720183645183, Actual: 30

Prediction: 21.999999999402718, Actual: 22

```