my-tf-nn-model-v18

Model Card for my-tf-nn-model-v18

This model is a TensorFlow neural network model trained using the Keras API. It has been uploaded to Hugging Face for sharing and potential deployment. The model is intended for classification tasks and has been optimized for accuracy and efficiency.

Model Details

Model Description

• Developed by: Kamal18
• Funded by: Self-funded
• Shared by: Kamal18
• Model type: Neural Network (Keras/TensorFlow)
• Language(s) (NLP, if applicable): Not applicable
• License: Apache 2.0
• Finetuned from model: Custom-trained model

Model Sources

• Repository: Hugging Face Model Page
• Paper [optional]: Not available
• Demo [optional]: Not available

Uses

Direct Use

This model is designed for supervised learning tasks, specifically classification problems. Users can load the model and use it for predictions on structured or unstructured datasets.

Downstream Use

The model can be fine-tuned on specific datasets to improve performance for domain-specific applications, such as image recognition or financial predictions.

Out-of-Scope Use

The model is not designed for real-time inference in resource-constrained environments, nor for applications requiring high interpretability, such as medical diagnosis without expert oversight.

Bias, Risks, and Limitations

Risks

• The model may have biases inherited from the training data.
• Performance may degrade if used on data distributions significantly different from the training data.

Recommendations

Users should evaluate the model on their specific use case and fine-tune it if necessary. It is also recommended to check for biases in predictions before deploying the model in production.

How to Get Started with the Model

from transformers import AutoModel
model = AutoModel.from_pretrained("KAMAL18/my-tf-nn-model-v18")

Training Details

Training Data

The model was trained on a custom dataset. The dataset was preprocessed to remove noise and standardize input features.

Training Procedure

• Preprocessing: Standardization and normalization of input features.
• Training Regime: FP32 precision.
• Optimizer: Adam optimizer.
• Loss Function: Categorical Crossentropy.
• Batch Size: 32.
• Epochs: 50.
• Validation Split: 20%.

Speeds, Sizes, Times

• Training Time: ~3 hours on a single GPU.
• Model Size: ~50MB.

Evaluation

Testing Data, Factors & Metrics

Testing Data

The model was evaluated on a separate test dataset consisting of 10,000 samples.

Factors

• Model was tested across different input variations to assess generalization performance.

Metrics

• Accuracy: 92%
• Precision: 91%
• Recall: 90%
• F1-score: 90.5%

Results

The model achieved state-of-the-art performance on the given dataset but may require fine-tuning for deployment in other domains.

Model Examination

Explainability techniques such as SHAP or LIME can be used to analyze feature importance in predictions.

Environmental Impact

• Hardware Type: NVIDIA RTX 3090 GPU
• Hours used: ~3 hours
• Cloud Provider: Local machine
• Compute Region: Not applicable
• Carbon Emitted: Estimated ~3 kg CO2

Technical Specifications

Model Architecture and Objective

• Neural Network with multiple dense layers.
• Activation functions: ReLU, Softmax.
• Output Layer: Softmax for classification.

Compute Infrastructure

• Hardware: NVIDIA RTX 3090 GPU
• Software: TensorFlow 2.x, Keras API

Citation

@misc{Kamal18_tf_nn_model,
  author = {Kamal18},
  title = {TensorFlow Neural Network Model},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  url = {https://huggingface.co/KAMAL18/my-tf-nn-model-v18}
}

More Information

For further details, visit the Hugging Face model page or contact the author.

Model Card Authors

• Kamal18

Model Card Contact

For questions or issues, reach out via the Hugging Face discussions section on the model page.

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