| ---
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| tags:
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| - pytorch
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| - tabular-classification
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| - synthetic-data
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| library_name: pytorch
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| ---
|
|
|
| # Simple Feed-Forward Neural Network
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|
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| This is a simple PyTorch feed-forward neural network trained on synthetic data.
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|
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| ## Model Details
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|
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| - **Architecture**: Feed-forward Neural Network
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| - **Input Size**: 10 features
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| - **Hidden Layer**: 32 neurons with ReLU activation
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| - **Output Layer**: 2 classes (Binary Classification)
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| - **Framework**: PyTorch
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|
|
| ## Training Data
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|
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| The model was trained on 1000 samples of synthetic data generated using `torch.randn`.
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| - **Features**: 10 random float values per sample.
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| - **Labels**: Binary (0 or 1), randomly assigned.
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| - **Split**: 80% Training, 20% Testing.
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|
|
| ## Training Procedure
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|
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| - **Optimizer**: Adam
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| - **Loss Function**: CrossEntropyLoss
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| - **Batch Size**: 32
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| - **Epochs**: 20
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|
|
| ## Usage
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|
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| ### Installation
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|
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| ```bash
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| pip install torch
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| ```
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|
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| ### Inference Code
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|
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| ```python
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| import torch
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| import torch.nn as nn
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| import json
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|
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| # Define Model Architecture
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| class SimpleNN(nn.Module):
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| def __init__(self, input_size, hidden_size, output_size):
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| super(SimpleNN, self).__init__()
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| self.fc1 = nn.Linear(input_size, hidden_size)
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| self.relu = nn.ReLU()
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| self.fc2 = nn.Linear(hidden_size, output_size)
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|
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| def forward(self, x):
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| out = self.fc1(x)
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| out = self.relu(out)
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| out = self.fc2(out)
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| return out
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|
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| # Load Configuration
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| with open("config.json", "r") as f:
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| config = json.load(f)
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|
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| # Load Model
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| model = SimpleNN(config["input_size"], config["hidden_size"], config["output_size"])
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| model.load_state_dict(torch.load("model.pth"))
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| model.eval()
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|
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| # Predict
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| dummy_input = torch.randn(1, 10)
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| output = model(dummy_input)
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| _, prediction = torch.max(output, 1)
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| print(f"Prediction: {prediction.item()}")
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| ```
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|
|
