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SECURITY.md
TensorFlow [**models**](https://developers.google.com/machine-learning/glossary/#model) (to use a term commonly used by machine learning practitioners) are expressed as programs that TensorFlow executes. TensorFlow programs are encoded as computation [**graphs**](https://developers.google.com/machine-learning/glossary/#graph). Since models are practically programs that TensorFlow executes, using untrusted
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RELEASE.md
* For Keras model, the individual call of `Model.evaluate` uses no cached data for evaluation, while `Model.fit` uses cached data when `validation_data` arg is provided for better performance. * Adds a `save_traces` argument to `model.save`/ `tf.keras.models.save_model` which determines whether the SavedModel format stores the Keras model/layer
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README.md
## Resources * [TensorFlow.org](https://www.tensorflow.org) * [TensorFlow Tutorials](https://www.tensorflow.org/tutorials/) * [TensorFlow Official Models](https://github.com/tensorflow/models/tree/master/official) * [TensorFlow Examples](https://github.com/tensorflow/examples) * [TensorFlow Codelabs](https://codelabs.developers.google.com/?cat=TensorFlow)
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tensorflow/c/eager/unified_api_testutil.h
// outputs = tf.function(model)(inputs) // else: // outputs = model(inputs) Status RunModel(Model model, AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs, absl::Span<AbstractTensorHandle*> outputs, bool use_function); Status BuildImmediateExecutionContext(bool use_tfrt, AbstractContext** ctx); // Return a tensor handle with given type, values and dimensions.
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.github/ISSUE_TEMPLATE/tflite-other.md
false - type: input id: Cuda attributes: label: CUDA/cuDNN version description: placeholder: validations: required: false - type: input id: Gpu attributes: label: GPU model and memory description: if compiling from source placeholder: validations: required: false - type: textarea id: what-happened attributes: label: Current Behaviour?
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tensorflow/c/experimental/gradients/nn_grad_test.cc
TF_RETURN_IF_ERROR(ops::SparseSoftmaxCrossEntropyWithLogits( ctx, inputs[0], inputs[1], &loss, &backprop, "SparseSoftmaxCrossEntropyWithLogits")); // `gradient_checker` only works with model that returns only 1 tensor. // Although, `ops::SparseSoftmaxCrossEntropyWithLogits` returns 2 tensors, the // second tensor isn't needed for computing gradient so we could safely drop // it. outputs[0] = loss;
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tensorflow/c/c_api_test.cc
SetViaProto(desc_, {}); FinishAndVerify(desc_, {}); } TEST(CAPI, SavedModel) { // Load the saved model. const string saved_model_dir = tensorflow::GetDataDependencyFilepath( tensorflow::io::JoinPath("tensorflow", "cc", "saved_model", "testdata", "half_plus_two", "00000123")); TF_SessionOptions* opt = TF_NewSessionOptions();
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tensorflow/BUILD
"//tensorflow/c:logging", "//tensorflow/c:ops", "//tensorflow/cc/saved_model:fingerprinting_impl", "//tensorflow/cc/saved_model:loader_lite_impl", "//tensorflow/cc/saved_model:metrics_impl", "//tensorflow/compiler/tf2tensorrt:op_converter_registry_impl", "//tensorflow/core/common_runtime:core_cpu_impl",
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ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats
cat > $BATS_TEST_TMPDIR/ignore_these_deps <<EOF benchmark _test$ _test.py$ _test_cpu$ _test_cpu.py$ _test_gpu$ _test_gpu.py$ _test_lib$ //tensorflow/cc/saved_model:saved_model_test_files //tensorflow/cc/saved_model:saved_model_half_plus_two //tensorflow:no_tensorflow_py_deps //tensorflow/tools/pip_package:win_pip_package_marker //tensorflow/core:image_testdata //tensorflow/core/lib/lmdb:lmdb_testdata
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.github/ISSUE_TEMPLATE/tflite-converter-issue.md
1) Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.
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