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SECURITY.md
inspected and debugged and it is intended to be used during the development phase. As part of the differences that make Eager mode easier to debug, the [shape inference functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface) are skipped, and any checks implemented inside the shape inference code are not executed. The security impact of skipping those checks should be low, since the attack
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ci/official/utilities/rename_and_verify_wheels.sh
"$python" -m pip install *.whl $TFCI_PYTHON_VERIFY_PIP_INSTALL_ARGS if [[ "$TFCI_WHL_IMPORT_TEST_ENABLE" == "1" ]]; then "$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)' "$python" -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)' fi # VERY basic check to ensure the [and-cuda] package variant is installable.
Shell Script - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Mar 27 21:16:27 GMT 2024 - 3.4K bytes - Viewed (0) -
tensorflow/c/c_api_function_test.cc
TF_DeleteStatus); TF_Tensor* tensor_shape = Int32Tensor({37, 1}); TF_Operation* shape = Const(tensor_shape, func_graph.get(), s.get(), "shape"); TF_Operation* random = RandomUniform(shape, TF_FLOAT, func_graph.get(), s.get()); TF_Output outputs[] = {{random, 0}}; *func = TF_GraphToFunction(func_graph.get(), name,
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tensorflow/c/eager/c_api_test_util.cc
// Create the variable handle. TFE_Op* op = TFE_NewOp(ctx, "VarHandleOp", status); if (TF_GetCode(status) != TF_OK) return nullptr; TFE_OpSetAttrType(op, "dtype", TF_FLOAT); TFE_OpSetAttrShape(op, "shape", {}, 0, status); TFE_OpSetAttrString(op, "container", "localhost", 0); TFE_OpSetAttrString(op, "shared_name", "", 0); if (!device_name.empty()) { TFE_OpSetDevice(op, device_name.c_str(), status); }
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tensorflow/c/c_test_util.h
TF_Operation* Neg(TF_Operation* n, TF_Graph* graph, TF_Status* s, const char* name = "neg"); TF_Operation* LessThan(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s); TF_Operation* RandomUniform(TF_Operation* shape, TF_DataType dtype, TF_Graph* graph, TF_Status* s); // Split `input` along the first dimension into 3 tensors TF_Operation* Split3(TF_Operation* input, TF_Graph* graph, TF_Status* s,
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tensorflow/c/eager/c_api_experimental.cc
} TFE_MonitoringBuckets* TFE_MonitoringNewExponentialBuckets(double scale, double growth_factor, int bucket_count) { return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() { return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,
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tensorflow/c/experimental/gradients/math_grad.cc
} Status Compute(AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> grad_outputs, absl::Span<AbstractTensorHandle*> grad_inputs) override { // TODO(vnvo2409): Add shape broadcasting /* Given upstream grad U and a Div op: Z = X/Y, the gradients are: * * dX = U / Y * dY = -U*X / Y^2 = (X/Y) * -U / Y = -U*Z / Y * */
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tensorflow/c/c_api_experimental.cc
ShapeHandle shape_handle = c.output(i); TF_ShapeAndType& shape = output_shapes_result->items[i]; shape.num_dims = c.Rank(shape_handle); if (shape.num_dims == InferenceContext::kUnknownRank) { shape.dims = nullptr; continue; } shape.dims = new int64_t[shape.num_dims]; for (size_t j = 0; j < shape.num_dims; ++j) { shape.dims[j] = c.Value(c.Dim(shape_handle, j));
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tensorflow/c/eager/parallel_device/parallel_device_lib_test.cc
ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); const std::vector<std::unique_ptr<ParallelTensor>>& handles = *outputs; const std::vector<int64_t>* shape; Status s = handles[0]->Shape(&shape); ASSERT_TRUE(s.ok()); EXPECT_EQ(0, shape->size()); } TEST(PARALLEL_DEVICE_LIB, TestCancelOnError) { std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status( TF_NewStatus(), TF_DeleteStatus);
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tensorflow/c/c_api_experimental.h
TF_CAPI_EXPORT extern void TF_DeleteShapeAndTypeListArray( TF_ShapeAndTypeList** shape_list_array, int num_items); // Infer shapes for the given `op`. The arguments mimic the arguments of the // `shape_inference::InferenceContext` constructor. Note the following: // - The inputs of the `op` are not used for shape inference. So, it is // OK to not have the inputs properly set in `op`. See `input_tensors`
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