In the world of deep learning and machine learning, TensorFlow has emerged as one of the leading frameworks for building complex models with ease. However, even with its robust capabilities, developers often encounter bugs that can be difficult to diagnose, especially when dealing with numerical computations. NaNs (Not a Number) and Infinities in your computation graph can lead to unexpected behaviors and results, making debugging an essential skill. This article will guide you through the process of identifying and dealing with NaNs and Infinities in TensorFlow.
Understanding NaNs and Infinities
Before diving into debugging, it's crucial to understand what NaNs and Infinities signify. NaNs typically arise from operations that do not yield a well-defined numerical result, such as dividing zero by zero or taking the square root of a negative number. Infinities result from operations like dividing a positive number by zero. Detecting these anomalies early in your TensorFlow graphs ensures that they don't propagate and affect the model training and results.
Checking for NaNs and Infinities in TensorFlow
TensorFlow provides several methods for inspecting your tensors during graph execution. This helps identify and handle these numerical issues effectively.
Basic Tensor Checking with TensorFlow
The simplest way to check tensors for NaNs is to use TensorFlow’s tf.debugging.check_numerics function. This method ensures that your tensors do not contain any NaN or Inf values.
import tensorflow as tf
# Assuming tensor is the tensor you want to check
with tf.Session() as sess:
# Define tensor operation
tensor = tf.constant([1.0, 2.0, float('nan'), float('inf')])
# Check for NaNs and Infinities
checked_tensor = tf.debugging.check_numerics(tensor, 'Checking for NaN and Inf')
try:
result = sess.run(checked_tensor)
print("Tensor is clean: ", result)
except tf.errors.InvalidArgumentError as e:
print("Encountered NaN or Inf in tensor:", e)In the example above, the tf.debugging.check_numerics function throws an error if NaNs or Infs are detected. You can then use try-catch blocks to catch exceptions and pinpoint the operation generating them, making debugging much simpler.
Visualizing NaNs and Infinities Using TensorFlow Debugger
When working with intricate models, logging individual tensor values may become impractical. TensorFlow Debugger (tfdbg) offers facilities for inspection of tensors during runtime, which helps investigate the state of tensor values and operations.
from tensorflow.python import debug as tf_debug
# Create a session and wrap with tfdbg
with tf.Session() as sess:
sess = tf_debug.LocalCLIDebugWrapperSession(sess)
# Set up the guiding tensors like loss, accuracy
loss = some_model() # Assume some_model defines your model
# During session.run(), anomalies will pop up for inspection in CLI
sess.run(loss, feed_dict={input: data, labels: label})The example above wraps the TensorFlow session with tfdbg, which launches an interactive command-line debugger. It provides inspection utilities right where suspicious operations occur.
Handling NaNs and Infinities
Once detected, handling NaNs and infinities can involve methods such as adding small values like epsilon to denominator, normalization, or clamping values to stay within reasonable ranges. Addressing these proactively in your model design helps avoid future issues.
Example: Safe Division
def safe_div(x, y, eps=1e-12):
return x / (y + eps)In the example above, the safe_div function uses epsilon to prevent division by very small numbers, hence avoiding infinite values.
Conclusion
By implementing these debugging techniques, and through early detection and handling of numerical issues in TensorFlow, you can vastly improve the robustness of your models. Remember, keeping track of computations, understanding source operations of anomalies, and implementing safe coding practices help build more reliable and efficient machine learning applications.
