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TensorFlow 1 version View source on GitHub

Convert image to dtype, scaling its values if needed.



Used in the guide:

Used in the tutorials:

Images that are represented using floating point values are expected to have values in the range [0,1). Image data stored in integer data types are expected to have values in the range [0,MAX], where MAX is the largest positive representable number for the data type.

This op converts between data types, scaling the values appropriately before casting.

Note that converting from floating point inputs to integer types may lead to over/underflow problems. Set saturate to True to avoid such problem in problematic conversions. If enabled, saturation will clip the output into the allowed range before performing a potentially dangerous cast (and only before performing such a cast, i.e., when casting from a floating point to an integer type, and when casting from a signed to an unsigned type; saturate has no effect on casts between floats, or on casts that increase the type's range).


  • image: An image.
  • dtype: A DType to convert image to.
  • saturate: If True, clip the input before casting (if necessary).
  • name: A name for this operation (optional).


image, converted to dtype.

Usage Example:

>> import tensorflow as tf
>> x = tf.random.normal(shape=(256, 256, 3), dtype=tf.float32)
>> tf.image.convert_image_dtype(x, dtype=tf.float16, saturate=False)


  • AttributeError: Raises an attribute error when dtype is neither float nor integer