NAN_LOSS tensorflow runtime_error ai_generated partial

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

ID: tensorflow/optimizer-nan-loss

Also available as: JSON · Markdown · 中文
75%Fix Rate
90%Confidence
1Evidence
2023-03-12First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow 2.8.0 active — — —
tensorflow 2.9.0 active — — —
tensorflow 2.10.0 active — — —

Root Cause

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

generic

中文

损失函数产生了 NaN 值，通常是由于梯度爆炸、除以零或损失计算中对零取对数。

Official Documentation

https://www.tensorflow.org/guide/keras/train_and_evaluate

Workarounds

  1. 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))`. 
    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))`.

中文步骤

  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))`.

Dead Ends

Common approaches that don't work:

  1. 90% fail

    This may delay NaN but does not fix the root cause; the loss can still explode later.

  2. 85% fail

    SGD is also susceptible to exploding gradients without clipping.