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README.md

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 ---
-license: apache-2.0
+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()}")
+```

config.json

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+{
+    "input_size": 10,
+    "hidden_size": 32,
+    "output_size": 2,
+    "num_samples": 1000,
+    "batch_size": 32,
+    "epochs": 20,
+    "learning_rate": 0.001,
+    "train_split": 0.8
+}

model.pth

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