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System Requirements Document

  1. Hyperparameter Tuning The system should provide the ability to fine-tune the hyperparameters of the neural network, enabling researchers and developers to optimize performance. Specific requirements include: 1.1 Adjustable Hyperparameters:  Number of Hidden Layers: Users must be able to increase or decrease the number of hidden layers to experiment with model depth.  Number of Hidden Units: Each hidden layer should allow for customization of the number of units (neurons).  Activation Functions: The system should support multiple activation functions such as ReLU, sigmoid, and tanh, selectable per layer.  Learning Rate: Users must be able to specify a learning rate for the optimizer, with support for both constant and adaptive learning rates.  Number of Epochs: The training process should allow the specification of the number of epochs, with visual feedback on progress. 1.2 Configuration Management:  A dedicated configuration file or graphical interface should be available to make changes to these parameters easily.  The system should maintain a history of configurations for reproducibility and comparison of experiments.

  2. Validation Mechanism To ensure the robustness of the model, the system must include a thorough validation mechanism with the following capabilities: 2.1 Misclassification Detection:  The system should automatically identify misclassified examples during the validation phase.

 For each class, at least one misclassified instance must be displayed, accompanied by the predicted label and true label. 2.2 Visualization:  The misclassified examples should be presented in a clear and concise format, such as images with overlaid labels or tabular summaries for non-visual data.  Users should be able to export this information for further analysis. 2.3 Metrics:  The system should calculate and display common evaluation metrics, including accuracy, precision, recall, and F1-score, for all classes.

  1. Workflow Improvements In addition to hyperparameter tuning, the system should support advanced workflow features to improve the overall accuracy of the neural network. These features include: 3.1 Data Preprocessing:  Support for advanced preprocessing techniques, such as normalization, standardization, and handling of missing values.  Options for data augmentation, including rotation, scaling, cropping, and flipping of images, to increase training data diversity. 3.2 Regularization:  Implementation of regularization techniques such as dropout to prevent overfitting.  Support for L1 and L2 regularization to control model complexity. 3.3 Alternative Architectures:  The system should provide templates or guidance for testing alternative architectures, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), depending on the dataset. 3.4 Automated Suggestions:  Incorporation of automated tools to recommend potential improvements, such as underutilized features or patterns in validation results. 3.5 Logging and Monitoring:

 Continuous logging of training and validation metrics for detailed performance tracking.  Real-time monitoring of resource utilization, including GPU/CPU usage, memory, and training time.

Additional Notes The system must be user-friendly and include comprehensive documentation for all features. It should also be designed to accommodate future enhancements and be compatible with industry-standard frameworks such as TensorFlow or PyTorch.