'InvalidArgumentError: assertion failed' in TensorFlow: Causes and How to Fix

Identify the Assertion Point

• Look at the error message to find out which specific assertion failed. TensorFlow error messages usually provide insights about the tensor shapes, types, or values that triggered the failure.

 

• Trace the error back to the line in your code where the assertion failed, often indicated in the stack trace.

2023-10-08 17:48:44.627230: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
2023-10-08 17:48:44.641905: I tensorflow/compiler/xla/service/service.cc:169] XLA service 0x560ea83dc090 initialized for platform Host (this does not guarantee that XLA will be used. Set the  TF_XLA_FLAGS  environment variable to "FLAGS=-xla" to force)
2023-10-08 17:48:45.390032: I tensorflow/compiler/xla/service/service.cc:169] XLA service 0x560ea8b5b410 initialized for platform CUDA (this does not guarantee that XLA will be used. Set the  TF_XLA_FLAGS  environment variable to "FLAGS=-xla" to force)
InvalidArgumentError: assertion failed: [0] [0] [labels must have the same shape as logits] [Condition x == y did not hold.]

Check Tensor Operations

• Verify tensor shapes involved in the failed assertion. Make sure all relevant tensors are of the expected dimensions and have the correct data types before any operations are performed.

 

• Use TensorFlow's shape inspection methods to check tensor shapes at runtime, such as `tf.shape(tensor)`.

import tensorflow as tf

# Example tensors
logits = tf.constant([[0.5, 1.5], [1.0, -0.5]])
labels = tf.constant([[0.0, 1.0], [0.0, 1.0]])

# Check shapes of logits and labels
assert logits.shape == labels.shape, f"Shape mismatch: {logits.shape} vs {labels.shape}"

Adjust Model Inputs or Outputs

• Ensure that your model's expected inputs and outputs match the provided data, especially during the compilation and execution phases.

 

• Modify your model's architecture to correctly handle differing shapes, perhaps by adding appropriate layers to transform shapes as needed.

from tensorflow import keras
from tensorflow.keras.layers import Dense, Flatten

# Sample model architecture
model = keras.Sequential([
    Flatten(input_shape=(28, 28)),
    Dense(128, activation='relu'),
    Dense(10)    # Assume 10 classes (output shape must match the label's one-hot encoded shape)
])

Use TensorFlow Debugging Tools

• Enable TensorFlow's eager execution mode if not enabled already. This provides more immediate feedback and easier debugging.

 

• Utilize TensorFlow debugging tools like `tf.print()` to closely monitor tensor values during execution without disturbing the graph execution.

tf.print("Logits:", logits)
tf.print("Labels:", labels)
tf.debugging.assert_shapes([(logits, (None, 10)), (labels, (None, 10))], 
                           message="Shapes are not compatible")

Implement Error Handling

• Wrap code blocks prone to error in `try-except` constructs to gracefully handle exceptions and provide more meaningful error messages.

 

• Consider using custom error classes to capture specific tensor-related issues within your TensorFlow graph.

try:
    # Code that might trigger invalid argument errors
    logits = model.predict(sample_input)
    tf.assert_equal(tf.shape(logits), tf.shape(labels), message="Prediction and label shapes do not match")
except tf.errors.InvalidArgumentError as e:
    print(f"Handling Error: {e}")