README.md

metadata

license: mit
datasets:
  - scikit-learn/iris
language:
  - en
base_model:
  - NeuralNine999/INET
pipeline_tag: tabular-classification
tags:
  - biology

INet - PyTorch Iris Classifier

Overview

INet is a simple fully-connected neural network trained on the Iris dataset using PyTorch. It classifies iris flowers into 4 categories based on 4 features: sepal length, sepal width, petal length, and petal width.

Model Architecture

• Input: 4 features
• Hidden layers: 64 → 32 → 16 → 8 neurons (ReLU activations)
• Output: 4 classes

Architecture flow: Input(4) → Linear(64) → ReLU → Linear(32) → ReLU → Linear(16) → ReLU → Linear(8) → ReLU → Linear(4)

• Loss: CrossEntropyLoss
• Optimizer: Adam, lr=0.01
• Epochs: 30

Files

• inet.pth → Trained model weights
• model.py → Contains INet class and architecture
• README.md → This file

How to Load

import torch
from model import INet  # make sure INet class is in model.py

model = INet()
model.load_state_dict(torch.load("inet.pth"))
model.eval()

# Example usage:
sample_input = torch.tensor([[5.1, 3.5, 1.4, 0.2]])
pred = model(sample_input)
pred_class = pred.argmax(dim=1).item()
print(pred_class)

Notes

• Make sure PyTorch is installed correctly

pip install torch

• The model expects input as a tensor of shape [batch_size, 4] with float32 values.