---
metrics:
- mse
library_name: tf-keras
tags:
- finance
---
Creating a model card on Hugging Face is an excellent way to document your model and provide users with instructions and important information about it. A model card typically includes sections such as model description, usage examples, training details, and any relevant information or limitations.

Here's a sample model card for your Keras Sequential model:

---

## Model Card for Sequential Regression Model

### Model Description

This model is a simple regression model built using Keras' Sequential API. It consists of five dense layers, each using ReLU activation functions except for the output layer. The model is designed for predicting a single continuous target value from an input vector of features.

**Model Architecture:**
- Dense Layer with 16 units and ReLU activation
- Dense Layer with 8 units and ReLU activation
- Dense Layer with 4 units and ReLU activation
- Dense Layer with 2 units and ReLU activation
- Dense Layer with 1 unit (output)

**Optimizer:** Adam  
**Loss Function:** Mean Squared Error (MSE)

### Usage

To use this model for inference, load the model and weights, and prepare your input data accordingly. The input data should be a 2D array with 10 features per sample.

#### Example

Here's a Python example of how to load the model and make predictions:

```python
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Initialize and define the model architecture
model = Sequential([
    Dense(16, activation='relu', input_shape=(10,)),  # Adjust input_shape as needed
    Dense(8, activation='relu'),
    Dense(4, activation='relu'),
    Dense(2, activation='relu'),
    Dense(1)
])

# Load the model weights
model.load_weights('model_weights.h5')

# Example input data
input_data = np.array([[0.5] * 10])  # Replace with actual data

# Make predictions
predictions = model.predict(input_data)
print(predictions)
```

### Training Details

- **Dataset**: retail-price-optimization kaggle.
- **Epochs**: 2000.
- 
- 

### Limitations and Considerations

- **Input Requirements**: The model expects input data with 10 features.
- 



---

### Adding the Model Card to Your Hugging Face Repository

1. **Create a `README.md` File**: Create a file named `README.md` in the root of your Hugging Face model repository.
2. **Add Content**: Copy the above content into the `README.md` file.
3. **Push Changes**: Commit the `README.md` file to your repository. You can do this through the Hugging Face web interface or by using Git from your local machine.

This will create a detailed model card that appears on your Hugging Face model page, providing users with all necessary information about your model. If you need further customization or help with any specific sections, feel free to ask!