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---
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()}")
```
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