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CONTRIBUTING.md
feature. * Full new features (e.g., a new op implementing a cutting-edge algorithm) typically will live in [tensorflow/addons](https://github.com/tensorflow/addons) to get some airtime before a decision is made regarding whether they are to be migrated to the core. * As every PR requires several CPU/GPU hours of CI testing, we discourage
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tensorflow/api_template_v1.__init__.py
The TensorFlow contrib module will not be included in TensorFlow 2.0. For more information, please see: * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md * https://github.com/tensorflow/addons * https://github.com/tensorflow/io (for I/O related ops) If you depend on functionality not listed there, please file an issue. """ contrib = _LazyLoader("contrib", globals(), "tensorflow.contrib",
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tensorflow/c/c_api.cc
TF_Status* status, TF_Output* dy) { #if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) status->status = tensorflow::errors::Unimplemented( "Adding gradients is not supported on mobile. File a bug at " "https://github.com/tensorflow/tensorflow/issues if this feature is " "important to you"); #else
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RELEASE.md
* `tf.contrib`: * `tf.contrib` has been deprecated, and functionality has been either migrated to the core TensorFlow API, to an ecosystem project such as [tensorflow/addons](https://www.github.com/tensorflow/addons) or [tensorflow/io](https://www.github.com/tensorflow/io), or removed entirely. * Remove `tf.contrib.timeseries` dependency on TF distributions.
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tensorflow/c/eager/c_api_unified_experimental_graph.cc
const char* op_name_ = nullptr; // TODO(srbs): Use this. string device_name_; }; // GraphContext wraps a TF_Graph modeling a single function and manages the // "execution" of operation, i.e. adding them to the function. class GraphContext : public TracingContext { public: explicit GraphContext(const char* name) : TracingContext(kGraph), graph_(new TF_Graph(), TF_DeleteGraph),
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tensorflow/c/eager/c_api_test_util.cc
TFE_DeleteOp(op); if (TF_GetCode(status) != TF_OK) return nullptr; CHECK_EQ(0, num_retvals); TF_DeleteStatus(status); return var_handle; } TFE_Op* AddOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b) { TF_Status* status = TF_NewStatus(); TFE_Op* op = TFE_NewOp(ctx, "AddV2", status); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
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tensorflow/c/eager/c_api.cc
if (!status->status.ok()) return; // TODO(nareshmodi): TFE_OpSetAttrFunction and TFE_OpSetAttrFunctionList // require TFE_Op* and just convert it internally a NameAttrValue, so // consider adding an overload to the C API to make this case easier. TFE_OpSetAttrFunction(op, attr_name, func_op); TFE_DeleteOp(func_op); } break; case tensorflow::AttrValue::kList: { // String
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tensorflow/c/eager/tape.h
// Operations the tape needs to perform on tensors to do backpropagation. Named // "vspace" because a subset of these are related to a vector space, such as // adding gradients, getting zeroes, etc. Currently cannot be implemented // without using tensorflow python code, hence left unspecified here. // // Gradient is the type returned by gradient functions. In Python TF it's either
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tensorflow/c/c_api_experimental.cc
// Make a copy so that we can mutate it. FunctionDef fdef_to_load = fdef; if (mutate_proto_func) { (*mutate_proto_func)(&fdef_to_load); } VLOG(1) << "Adding func to graph: " << fdef_to_load.DebugString(); std::vector<char> binary_proto_buf(fdef_to_load.ByteSizeLong()); fdef_to_load.SerializeToArray(binary_proto_buf.data(),
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configure.py
question: optional string for how to ask for user input. yes_reply: optional string for reply when feature is enabled. no_reply: optional string for reply when feature is disabled. bazel_config_name: adding config to .bazelrc instead of action_env. """ var = int( get_var(environ_cp, var_name, query_item, enabled_by_default, question, yes_reply, no_reply)) if not bazel_config_name:
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