InvalidArgumentError Unsupported 'dtype' Value bfloat16

InvalidArgumentError: Value for attr 'dtype' of bfloat16 is not in the list of allowed values: uint8, int32, int64, half, float, double

In TensorFlow, when creating tensors or specifying the data type for operations, you need to choose from a list of supported data types. The 'dtype' attribute specifies the data type of a tensor or operation result.

The error message "InvalidArgumentError: Value for attr 'dtype' of bfloat16 is not in the list of allowed values: uint8, int32, int64, half, float, double" indicates that 'bfloat16' is not a valid data type in TensorFlow. It is not included in the list of allowed values.

To resolve the issue, you should select one of the supported data types and use it instead. Let's explore each of these data types and provide examples:

  1. uint8: Unsigned 8-bit integer. Example usage: Image data with pixel values ranging from 0 to 255.

  2. int32: 32-bit integer. Example usage: Labels or indices in a classification task.

  3. int64: 64-bit integer. Example usage: Large integer values or indices.

  4. half: 16-bit floating-point. Example usage: Memory-constrained environments that benefit from reduced precision.

  5. float: 32-bit floating-point. Example usage: General-purpose floating-point operations.

  6. double: 64-bit floating-point. Example usage: High-precision calculations requiring increased accuracy.

When creating a tensor or specifying the 'dtype' attribute in TensorFlow operations, ensure that you select one of the valid data types mentioned above. Here's an example that illustrates the correct usage:

import tensorflow as tf
 
# Creating a tensor with dtype=float32
tensor = tf.constant([1.0, 2.0, 3.0], dtype=tf.float32)
 
# Performing a TensorFlow operation with the correct dtype
result = tf.math.sqrt(tensor)
 
# Printing the result
print(result)

In this example, we create a tensor with a valid 'dtype' of 'float32' and then perform the square root operation. This code will execute without any errors.

However, if you were to replace tf.float32 with tf.bfloat16 in the above code snippet, you would encounter the "InvalidArgumentError" due to the unsupported 'dtype' value.

Remember to choose an appropriate data type from the list of supported types when working with TensorFlow operations to avoid encountering this error.

See all posts