TensorFlow error message - "InvalidArgumentError Graph execution error"

This article explains the common TensorFlow error message, "InvalidArgumentError: Graph execution error," and provides a detailed explanation of its causes and potential solutions. With the help of an example scenario involving inconsistent tensor shapes during batching, the article guides readers through understanding the error and demonstrates how to resolve it by ensuring tensors have consistent shapes, particularly in the context of image data processing. The article aims to help TensorFlow users overcome this error and continue their machine learning projects smoothly.

The error message is:

InvalidArgumentError: Cannot batch tensors with different shapes in component 0. First element had shape [408, 500, 3] and element 1 had shape [360, 343, 3].
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This error typically occurs when you try to batch tensors with inconsistent shapes, especially when using frameworks like TensorFlow or Keras. It specifically mentions that the tensors in component 0 (usually the input data) have different shapes: the first element has a shape of [408, 500, 3], while the second element has a shape of [360, 343, 3]. This inconsistency prevents the tensors from being batched together.

To understand this further, let's consider an example. Suppose you are training a convolutional neural network (CNN) for image classification. Each image in your dataset has a different shape, and you want to batch them together for efficient training. However, CNNs typically require inputs of the same shape to process them in parallel.

Here's an example of how this error can occur:

import tensorflow as tf
 
# Creating two example tensors with different shapes
tensor_1 = tf.random.normal([408, 500, 3])
tensor_2 = tf.random.normal([360, 343, 3])
 
# Creating a dataset from the tensors
dataset = tf.data.Dataset.from_tensor_slices([tensor_1, tensor_2])
dataset = dataset.batch(2)  # Trying to batch the tensors together
 
# Performing an operation that triggers the error
for batch in dataset:
    print(batch)

When you execute the above code, you will encounter the InvalidArgumentError with the same error message as before.

To resolve this issue, you need to ensure that all tensors in the dataset have the same shape. In the context of image data, you can achieve this by resizing or cropping the images to a consistent size before batching them.

Here's an updated example that preprocesses the images to a common size before batching:

import tensorflow as tf
 
# Creating two example tensors with different shapes
tensor_1 = tf.random.normal([408, 500, 3])
tensor_2 = tf.random.normal([360, 343, 3])
 
# Preprocessing function to resize images
def preprocess(image):
    resized_image = tf.image.resize(image, [400, 400])  # Resizing to a common size
    return resized_image
 
# Preprocessing the tensors
preprocessed_tensor_1 = preprocess(tensor_1)
preprocessed_tensor_2 = preprocess(tensor_2)
 
# Creating a dataset from the preprocessed tensors
dataset = tf.data.Dataset.from_tensor_slices([preprocessed_tensor_1, preprocessed_tensor_2])
dataset = dataset.batch(2)  # Batching the tensors
 
# Performing an operation with the updated dataset
for batch in dataset:
    print(batch)

In the updated example, the preprocess() function is used to resize each image tensor to a common size of [400, 400, 3]. The preprocessed tensors are then used to create the dataset, which is successfully batched without triggering the error.

By ensuring that all tensors in your training dataset have the same shape, you can avoid the InvalidArgumentError related to inconsistent shapes when batching. Adapt this approach to your specific use case and adjust the preprocessing steps accordingly.