TensorFlow 2.2.0 phát hành chính thức: Vĩnh biệt Python 2

• tf.data:
  • Removed autotune_algorithm from experimental optimization options.
• TF Core:
  • tf.constant always creates CPU tensors irrespective of the current device context.
  • Eager TensorHandles maintain a list of mirrors for any copies to local or remote devices. This avoids any redundant copies due to op execution.
  • For tf.Tensor & tf.Variable, .experimental_ref() is no longer experimental and is available as simply .ref().
  • pfor/vectorized_map: Added support for vectorizing 56 more ops. Vectorizing tf.cond is also supported now.
  • Set as much partial shape as we can infer statically within the gradient impl of the gather op.
  • Gradient of tf.while_loop emits StatelessWhile op if cond and body functions are stateless. This allows multiple gradients while ops to run in parallel under distribution strategy.
  • Speed up GradientTape in eager mode by auto-generating list of op inputs/outputs which are unused and hence not cached for gradient functions.
  • Support back_prop=False in while_v2 but mark it as deprecated.
  • Improve error message when attempting to use None in data-dependent control flow.
  • Add RaggedTensor.numpy().
  • Update RaggedTensor.__getitem__ to preserve uniform dimensions & allow indexing into uniform dimensions.
  • Update tf.expand_dims to always insert the new dimension as a non-ragged dimension.
  • Update tf.embedding_lookup to use partition_strategy and max_norm when ids is ragged.
  • Allow batch_dims==rank(indices) in tf.gather.
  • Add support for bfloat16 in tf.print.
• tf.distribute:
  • Support embedding_column with variable-length input features for MultiWorkerMirroredStrategy.
• tf.keras:
  • Added experimental_aggregate_gradients argument to tf.keras.optimizer.Optimizer.apply_gradients. This allows custom gradient aggregation and processing aggregated gradients in custom training loop.
  • Allow pathlib.Path paths for loading models via Keras API.
• tf.function/AutoGraph:
  • AutoGraph is now available in ReplicaContext.merge_call, Strategy.extended.update and Strategy.extended.update_non_slot.
  • Experimental support for shape invariants has been enabled in tf.function. See the API docs for tf.autograph.experimental.set_loop_options for additonal info.
  • AutoGraph error messages now exclude frames corresponding to APIs internal to AutoGraph.
  • Improve shape inference for tf.function input arguments to unlock more Grappler optimizations in TensorFlow 2.x.
  • Improve automatic control dependency management of resources by allowing resource reads to occur in parallel and synchronizing only on writes.
  • Fix execution order of multiple stateful calls to experimental_run_v2 in tf.function.
  • You can now iterate over RaggedTensors using a for loop inside tf.function.
• tf.lite:
  • Migrated the tf.lite C inference API out of experimental into lite/c.
  • Add an option to disallow NNAPI CPU / partial acceleration on Android 10
  • TFLite Android AARs now include the C headers and APIs are required to use TFLite from native code.
  • Refactors the delegate and delegate kernel sources to allow usage in the linter.
  • Limit delegated ops to actually supported ones if a device name is specified or NNAPI CPU Fallback is disabled.
  • TFLite now supports tf.math.reciprocal1 op by lowering to tf.div op.
  • TFLite's unpack op now supports boolean tensor inputs.
  • Microcontroller and embedded code moved from experimental to main TensorFlow Lite folder
  • Check for large TFLite tensors.
  • Fix GPU delegate crash with C++17.
  • Add 5D support to TFLite strided_slice.
  • Fix error in delegation of DEPTH_TO_SPACE to NNAPI causing op not to be accelerated.
  • Fix segmentation fault when running a model with LSTM nodes using NNAPI Delegate
  • Fix NNAPI delegate failure when an operand for Maximum/Minimum operation is a scalar.
  • Fix NNAPI delegate failure when Axis input for reduce operation is a scalar.
  • Expose option to limit the number of partitions that will be delegated to NNAPI.
  • If a target accelerator is specified, use its feature level to determine operations to delegate instead of SDK version.
• tf.random:
  • Various random number generation improvements:
    • Add a fast path for default random_uniform
    • random_seed documentation improvement.
    • RandomBinomial broadcasts and appends the sample shape to the left rather than the right.
  • Added tf.random.stateless_binomial, tf.random.stateless_gamma, tf.random.stateless_poisson
  • tf.random.stateless_uniform now supports unbounded sampling of int types.
• Math and Linear Algebra:
  • Add tf.linalg.LinearOperatorTridiag.
  • Add LinearOperatorBlockLowerTriangular
  • Add broadcasting support to tf.linalg.triangular_solve#26204, tf.math.invert_permutation.
  • Add tf.math.sobol_sample op.
  • Add tf.math.xlog1py.
  • Add tf.math.special.{dawsn,expi,fresnel_cos,fresnel_sin,spence}.
  • Add a Modified Discrete Cosine Transform (MDCT) and its inverse to tf.signal.
• TPU Enhancements:
  • Refactor TpuClusterResolver to move shared logic to a separate pip package.
  • Support configuring TPU software version from cloud tpu client.
  • Allowed TPU embedding weight decay factor to be multiplied by learning rate.
• XLA Support:
  • Add standalone XLA AOT runtime target + relevant .cc sources to pip package.
  • Add check for memory alignment to MemoryAllocation::MemoryAllocation() on 32-bit ARM. This ensures a deterministic early exit instead of a hard to debug bus error later.
  • saved_model_cli aot_compile_cpu allows you to compile saved models to XLA header+object files and include them in your C++ programs.
  • Enable Igamma, Igammac for XLA.
• Deterministic Op Functionality:
  • XLA reduction emitter is deterministic when the environment variable TF_DETERMINISTIC_OPS is set to "true" or "1". This extends deterministic tf.nn.bias_add back-prop functionality (and therefore also deterministic back-prop of bias-addition in Keras layers) to include when XLA JIT complilation is enabled.
  • Fix problem, when running on a CUDA GPU and when either environment variable TF_DETERMINSTIC_OPS or environment variable TF_CUDNN_DETERMINISTIC is set to "true" or "1", in which some layer configurations led to an exception with the message "No algorithm worked!"
• Tracing and Debugging:
  • Add source, destination name to _send traceme to allow easier debugging.
  • Add traceme event to fastpathexecute.
• Other:
  • Fix an issue with AUC.reset_states for multi-label AUC #35852
  • Fix the TF upgrade script to not delete files when there is a parsing error and the output mode is in-place.
  • Move tensorflow/core:framework/*_pyclif rules to tensorflow/core/framework:*_pyclif.

Wiki
TensorFlow là một thư viện phần mềm mã nguồn mở dành cho máy học trong nhiều loại hình tác vụ nhận thức và hiểu ngôn ngữ. TensorFlow nguyên thủy được phát triển bởi đội Google Brain dành cho mục đích nghiên cứu và sản xuất của Google và sau đó được phát hành theo giấy phép mã nguồn mở Apache 2.0 vào ngày 9/11/2015.

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