---
tags:
- pytorch
- tabular-classification
- synthetic-data
library_name: pytorch
---

# Simple Feed-Forward Neural Network

This is a simple PyTorch feed-forward neural network trained on synthetic data.

## Model Details

- **Architecture**: Feed-forward Neural Network
- **Input Size**: 10 features
- **Hidden Layer**: 32 neurons with ReLU activation
- **Output Layer**: 2 classes (Binary Classification)
- **Framework**: PyTorch

## Training Data

The model was trained on 1000 samples of synthetic data generated using `torch.randn`.
- **Features**: 10 random float values per sample.
- **Labels**: Binary (0 or 1), randomly assigned.
- **Split**: 80% Training, 20% Testing.

## Training Procedure

- **Optimizer**: Adam
- **Loss Function**: CrossEntropyLoss
- **Batch Size**: 32
- **Epochs**: 20

## Usage

### Installation

```bash
pip install torch
```

### Inference Code

```python
import torch
import torch.nn as nn
import json

# Define Model Architecture
class SimpleNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

# Load Configuration
with open("config.json", "r") as f:
    config = json.load(f)

# Load Model
model = SimpleNN(config["input_size"], config["hidden_size"], config["output_size"])
model.load_state_dict(torch.load("model.pth"))
model.eval()

# Predict
dummy_input = torch.randn(1, 10)
output = model(dummy_input)
_, prediction = torch.max(output, 1)
print(f"Prediction: {prediction.item()}")
```