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CONTRIBUTING.md
For example: ```bash python tf_doctest.py --file=<file_path> ``` This will run it using your installed version of TensorFlow. To be sure you're running the same code that you're testing: * Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/) `pip install -U tf-nightly` * Rebase your pull request onto a recent pull from
Plain Text - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Thu Mar 21 11:45:51 GMT 2024 - 15.6K bytes - Viewed (0) -
tensorflow/api_template.__init__.py
_tf2.enable() # API IMPORTS PLACEHOLDER # WRAPPER_PLACEHOLDER # Make sure directory containing top level submodules is in # the __path__ so that "from tensorflow.foo import bar" works. # We're using bitwise, but there's nothing special about that. _API_MODULE = _sys.modules[__name__].bitwise _tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__)) _current_module = _sys.modules[__name__]
Python - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Mar 05 06:27:59 GMT 2024 - 6.7K bytes - Viewed (3) -
ci/official/utilities/code_check_full.bats
# only on affected targets, usually. There are targets in //tensorflow/lite that # don't pass --nobuild, so they're on their own. # # Although buildifier checks for formatting as well, "bazel build nobuild" # checks for cross-file issues like bad includes or missing BUILD definitions. # # We can't test on the windows toolchains because they're using a legacy # toolchain format (or something) that specifies the toolchain directly instead
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configure.py
""" # First check to see if we're using a blessed version of the NDK. properties_path = '%s/source.properties' % android_ndk_home_path if is_windows() or is_cygwin(): properties_path = cygpath(properties_path) with open(properties_path, 'r') as f: filedata = f.read() revision = re.search(r'Pkg.Revision = (\d+)', filedata) if revision:
Python - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 18:25:36 GMT 2024 - 53.8K bytes - Viewed (1) -
tensorflow/c/eager/tape.h
tensors.reserve(output_tensors.size()); for (const TapeTensor& o : output_tensors) { // Note: the tensor can have already been watched and hence be in the tape, // so we cannot check that we're inserting it here. tensor_tape_[o.GetID()] = op_id; tensor_usage_[o.GetID()] = 1; tensors.push_back(o); } op_tape_[op_id] = OpTapeEntry<BackwardFunction, TapeTensor>{
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tensorflow/c/eager/c_api_experimental.h
TFE_Executor*, TF_Status* status); // When an error happens, any pending operations are discarded, and newly issued // ops return an error. This call clears the error state and re-enables // execution of newly issued ops. // // Note that outputs of discarded ops remain in a corrupt state and should not // be used for future calls.
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tensorflow/c/eager/c_api_experimental.cc
tensorflow::ImmediateExecutionTensorHandle* unwrapped_handle = tensorflow::unwrap(handles[i]); if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) { // One of the inputs we're trying to pack is on a custom device. We'll let // the first custom device we see handle all of the packing. auto* custom_device_handle = tensorflow::down_cast<tensorflow::CustomDeviceTensorHandle*>(
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RELEASE.md
* Added a new `rerandomize_each_iteration` argument for the `tf.data.Dataset.random()` operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If `seed` is set and `rerandomize_each_iteration=True`, the `random()` operation will produce a different (deterministic) sequence of numbers every epoch.
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