tflite::Interpreter::output_tensor_by_signature.tflite::Interpreter::input_tensor_by_signature.tflite::Interpreter::GetSignatureRunner.Likewise for the following signature-related methods of tflite::Interpreter: The tflite::SignatureRunner class, which provides support for named parameters and for multiple named computations within a single TF Lite model, is no longer considered experimental. Sub_op and mul_op support broadcasting up to 6 dimensions. Moved option warm_start from tf. to tf.data.Options.See tf. documentation for how to call and use it.Can be accessed through inference_fn property of ConcreteFunctions. Introducing tf. as the fastest way to perform TF computations in Python. See the tf. documentation for more details. It can be accessed through the function_type property of tf.functions and ConcreteFunctions. Introducing tf. as the comprehensive representation of the signature of tf.function callables.tf. now allows custom tf.function inputs to declare Tensor decomposition and type casting support.Making the tf.function type system fully available: If the exact phrase is not there, it means they are off. To verify if oneDNN optimizations are on, look for a message with "oneDNN custom operations are on" in the log.oneDNN optimizations can yield slightly different numerical results compared to when oneDNN optimizations are disabled due to floating-point round-off errors fromĭifferent computation approaches and orders.To fall back to default settings, unset the environment variable. To explicitly enable or disable oneDNN optimizations, set the environment variable TF_ENABLE_ONEDNN_OPTS to 1 (enable) or 0 (disable) before running TensorFlow.oneDNN optimizations are enabled by default on X86 CPUs.OneDNN CPU performance optimizations Windows 圆4 & x86. Release 2.15.0 TensorFlow Breaking Changes
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