evaluate(model)
```
Another approach is to compare statistics of the FP32 and INT8 layers; commonly used metrics for these are SQNR (Signal to Quantized Noise Ratio) and Mean-Squre-Error. Such a comparative analysis may also help in guiding further optimizations. 



  Fig 8. Comparing model weights and activations

PyTorch provides tools to help with this analysis under the Numeric Suite. Learn more about using Numeric Suite from the full tutorial.
```python
extract from https://pytorch.org/tutorials/prototype/numeric_suite_tutorial.html
import torch.quantization._numeric_suite as ns
def SQNR(x, y):
    # Higher is better
    Ps = torch.norm(x)
    Pn = torch.norm(x-y)
    return 20*torch.log10(Ps/Pn)