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Effective TensorFlow 2.0 There are multiple changes in TensorFlow 2.0 to make TensorFlow users more productive. TensorFlow 2.0 removes redundant APIs, makes APIs more consistent (Unified RNNs, Unified Optimizers), and better integrates with the Python runtime with Eager execution. Many RFCs have explained the changes that have gone into making TensorFlow 2.0. This guide presents a vision for what
Quantization-aware training Quantization-aware model training ensures that the forward pass matches precision for both training and inference. There are two aspects to this: Operator fusion at inference time are accurately modeled at training time. Quantization effects at inference are modeled at training time. For efficient inference, TensorFlow combines batch normalization with the preceding con
Note: XLA is still under development. Some use cases will not see improvements in speed or decreased memory usage. XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that optimizes TensorFlow computations. The results are improvements in speed, memory usage, and portability on server and mobile platforms. Initially, most users will not see large benefits from XLA, bu
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