# detect_anomaly

Source: https://www.tensorplay.cn/docs/generated/tensorplay.autograd.anomaly_mode.detect_anomaly.html

```python
class tensorplay.autograd.anomaly_mode.detect_anomaly(check_nan=True)
```

Context-manager that enables anomaly detection for the autograd engine.

This does two things:

- Running the forward pass with detection enabled will allow the backward pass to print the traceback of the forward operation that created the failing backward function.

- If check_nan is True, any backward computation that generates “nan” value will raise an error. Default True.

> **Warning**
>
> This mode should be enabled only for debugging as the different tests will slow down your program execution.

Example

```
>>> import tensorplay
>>> from tensorplay import autograd
>>> class MyFunc(autograd.Function):
...     @staticmethod
...     def forward(ctx, inp):
...         return inp.clone()
...
...     @staticmethod
...     def backward(ctx, gO):
...         # Error during the backward pass
...         raise RuntimeError("Some error in backward")
...         return gO.clone()
>>> def run_fn(a):
...     out = MyFunc.apply(a)
...     return out.sum()
>>> inp = tensorplay.rand(10, 10, requires_grad=True)
>>> out = run_fn(inp)
>>> out.backward()
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
        out.backward()
    RuntimeError: Some error in backward
>>> with autograd.detect_anomaly():
...     inp = tensorplay.rand(10, 10, requires_grad=True)
...     out = run_fn(inp)
...     out.backward()
    Traceback of forward call that caused the error:
      File "tmp.py", line 53, in <module>
        out = run_fn(inp)
      File "tmp.py", line 44, in run_fn
        out = MyFunc.apply(a)
    Traceback (most recent call last):
      File "<stdin>", line 4, in <module>
    RuntimeError: Some error in backward
```