# tensorflow.python.framework.errors_impl.InvalidArgumentError: Loss is inf or nan : Tensor had NaN values

- **ID:** `tensorflow/optimizer-nan-loss`
- **Domain:** tensorflow
- **Category:** runtime_error
- **Error Code:** `NAN_LOSS`
- **Verification:** ai_generated
- **Fix Rate:** 75%

## Root Cause

The loss function produced NaN values, often due to exploding gradients, division by zero, or log of zero in the loss computation.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| tensorflow 2.8.0 | active | — | — |
| tensorflow 2.9.0 | active | — | — |
| tensorflow 2.10.0 | active | — | — |

## Workarounds

1. **Add gradient clipping in the optimizer: `optimizer = tf.keras.optimizers.Adam(clipnorm=1.0)` or use `tf.clip_by_global_norm`. Also check for log(0) by adding a small epsilon: `loss = -tf.reduce_sum(y_true * tf.math.log(y_pred + 1e-10))`.** (85% success)
   ```
   Add gradient clipping in the optimizer: `optimizer = tf.keras.optimizers.Adam(clipnorm=1.0)` or use `tf.clip_by_global_norm`. Also check for log(0) by adding a small epsilon: `loss = -tf.reduce_sum(y_true * tf.math.log(y_pred + 1e-10))`.
   ```

## Dead Ends

- **** — This may delay NaN but does not fix the root cause; the loss can still explode later. (90% fail)
- **** — SGD is also susceptible to exploding gradients without clipping. (85% fail)