While training a deep neural network, you notice that the gradients are becoming extremely small, making the weights of the initial layers change very slowly. What might be the primary cause of this issue?
- Overfitting
- Vanishing gradients due to the use of deep activation functions
- Underfitting due to a small learning rate
- Excessive learning rate causing divergence
The primary cause of extremely small gradients in deep neural networks is vanishing gradients, often caused by the use of deep activation functions like sigmoid or tanh. As gradients propagate backward through many layers, they tend to approach zero, which can slow down training. Proper initialization techniques and activation functions like ReLU can help mitigate this issue.
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