'Nan loss values' in TensorFlow: Causes and How to Fix

Check Data Preprocessing

• Ensure that your input data is properly normalized. Normalize features to have a mean of 0 and a standard deviation of 1.

 

• Verify that labels in classification tasks are one-hot encoded if your network expects categorical rather than sparse labels.

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
scaled_data = scaler.fit_transform(raw_data)

Adjust Learning Rate

• Try decreasing the learning rate, as a too-high learning rate can cause numerical instability leading to NaN loss.

 

• Utilize a learning rate schedule or adaptive learning rate optimizers like Adam to dynamically control the learning rate.

from tensorflow.keras.optimizers import Adam

optimizer = Adam(learning_rate=0.0001)

Use Gradient Clipping

• Implement gradient clipping to prevent large gradients from causing overflow during training. This will limit the maximum gradient.

 

• Set a clipping threshold in your optimizer to control the gradient clipping behavior.

from tensorflow.keras.optimizers import Adam

optimizer = Adam(learning_rate=0.001, clipnorm=1.0)

Handle Initialization

• Ensure that the model weights are properly initialized. Use appropriate initializers instead of relying on default settings.

 

• Consider initializers like HeNormal for ReLU activations or GlorotUniform for sigmoid/tanh activations.

from tensorflow.keras.layers import Dense
from tensorflow.keras.initializers import HeNormal

layer = Dense(units=64, activation='relu', kernel_initializer=HeNormal())

Monitor for Infinities and NaNs

• Investigate where in your network NaN values start to appear by adding debugging callbacks or printing intermediate values.

 

• Utilize TensorFlow's debugging utilities to log and watch for events that produce NaNs.

import tensorflow as tf

@tf.function
def train_step():
    # Example training step function
    pass    # Replace with actual training loop logic

tf.debugging.enable_check_numerics()  # Watch for NaNs or Infs

Check for Exploding Gradient

• Review layers and parameters to see if they are causing excessively large gradients. This frequently occurs in recurrent networks.

 

• Use regularization techniques such as L1, L2, or Dropout to help stabilize the gradients.

from tensorflow.keras.layers import LSTM, Dropout

dropout_layer = Dropout(rate=0.2)
lstm_layer = LSTM(units=128, activity_regularizer='l2')

Audit Objective Function

• Ensure the loss function is appropriate for your task. For instance, using a regression loss for a classification task might provide NaN values.

 

• Review the loss to make sure there are no divisions by zero or log of zero within the function.

```python
from tensorflow.keras.losses import SparseCategoricalCrossentropy

loss = SparseCategoricalCrossentropy()
```