When back-propagation is failing to decrease loss properly in your own neural network implementation compared to TensorFlow, it's a common issue in custom deep learning implementations. There can be various reasons for this, and debugging the issue can be complex. Here are some common things to check and adjust:
1. **Check Network Architecture**: Ensure that the architecture of your custom neural network matches the one in TensorFlow, including the number of layers, neurons, activation functions, and connections. Mismatched architectures can lead to suboptimal convergence.
2. **Initialization of Weights**: Weight initialization is crucial. Improper initialization can result in gradients that are too small or too large, making training difficult. Use proper weight initialization techniques like Xavier/Glorot or He initialization.
3. **Learning Rate**: Adjust the learning rate. A learning rate that's too high can make the optimization algorithm diverge, while one that's too low can make it converge very slowly. Experiment with different learning rates.
4. **Gradient Clipping**: Implement gradient clipping to prevent exploding gradients. This can be especially important for deep networks. It involves scaling gradients if they exceed a certain threshold.
5. **Activation Functions**: Check that you're using the same activation functions in your custom implementation as in TensorFlow. Different activation functions can lead to different convergence behavior.
6. **Loss Function**: Ensure that you're using the same loss function in both implementations. Different loss functions can lead to different gradients, which can affect convergence.
7. **Batch Size**: Adjust the batch size used during training. Smaller batch sizes may introduce noise, while larger batch sizes may slow down convergence. Experiment with different batch sizes.
8. **Regularization**: Apply regularization techniques like L1 or L2 regularization to prevent overfitting. Overfitting can make it look like the loss isn't decreasing when it's just increasing on the validation set.
9. **Data Preprocessing**: Ensure that data preprocessing, such as normalization and scaling, is consistent between your custom implementation and TensorFlow.
10. **Check for Bugs**: Carefully review your custom implementation for bugs, typos, or logic errors in the forward and backward passes.
11. **Monitor Training**: Monitor the training process by tracking metrics like training and validation loss, and inspect the gradient updates for any unusual behavior.
12. **Early Stopping**: Implement early stopping based on validation loss. Training for too long can lead to overfitting.
13. **Differences in Optimizers**: If you're using a custom optimizer, ensure that it is functioning correctly and consistently with the optimizer in TensorFlow.
14. **Comparison to Standard Datasets**: If you're trying to match performance on a specific dataset, make sure that your custom implementation and TensorFlow are being tested on the exact same data and in the same conditions.
By carefully checking these aspects, you can improve your custom neural network implementation and make it perform closer to TensorFlow. Keep in mind that TensorFlow is a highly optimized library, so it's normal for custom implementations to require some fine-tuning to achieve similar performance.