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| PDF Title : | Python Machine Learning |
|---|---|
| Total Page : | 454 Pages |
| Author: | Sebastian Raschka |
| PDF Size : | 32.2 MB |
| Language : | English |
| Source : | sebastianraschka.com |
| PDF Link : | Available |
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Python Machine Learning
we want the relative error between the numerical gradient and the analytical gradient to be as small as possible. Before we implement gradient checking, we need to discuss one more detail: what is the acceptable error threshold to pass the gradient check? The relative error threshold for passing the gradient check depends on the complexity of the network architecture.
As a rule of thumb, the more hidden layers we add, the larger the difference between the numerical and analytical gradient can become if backpropagation is implemented correctly. Since we have implemented a relatively simple neural network architecture in this chapter, we want to be rather strict about the threshold and define the following rules:
• Relative error <= 1e-7 means everything is okay! • Relative error <= 1e-4 means the condition is problematic, and we should look into it. • Relative error > 1e-4 means there is probably something wrong in our code.
Python Machine Learning PDF
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