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RELEASE.md
Hoffmann, Tim Zaman, tomguluson92, Tongxuan Liu, Trent Lo, Trevor Morris, TungJerry, Tyorden, Uday Bondhugula, v1incent, Vagif, Vasileios Lioutas, vbvg2008, vcarpani, Vijay Ravichandran, Vikram Tiwari,Viktor Gal, Vishwak Srinivasan, Vincent, Vishnuvardhan Janapati, Vitor-Alves, Vivek Suryamurthy, wangsiyu, wateryzephyr, WeberXie, Wei Wang, WeijieSun, Wen-Heng (Jack) Chung, wenxizhu, Will Battel, William D. Irons, winstonq, wyzhao, Xiaoming (Jason) Cui,
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docs/en/docs/img/logo-teal-vector.svg
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okhttp/src/test/resources/okhttp3/internal/publicsuffix/public_suffix_list.dat
// diy : 2015-11-05 Lifestyle Domain Holdings, Inc. diy // dnp : 2013-12-13 Dai Nippon Printing Co., Ltd. dnp // docs : 2014-10-16 Charleston Road Registry Inc. docs // doctor : 2016-06-02 Binky Moon, LLC doctor // dog : 2014-12-04 Binky Moon, LLC dog // domains : 2013-10-17 Binky Moon, LLC domains // dot : 2015-05-21 Dish DBS Corporation dot
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docs/en/docs/img/logo-margin/logo-teal-vector.svg
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tensorflow/c/eager/tape.h
: public std::function<Status(const std::vector<Gradient*>&, std::vector<Gradient*>*, bool)> { public: template <typename lambda_type> explicit ForwardFunction(lambda_type lambda) : std::function<Status(const std::vector<Gradient*>&, std::vector<Gradient*>*, bool)>(lambda) {} }; // Computes Jacobian-vector products using forward-mode automatic
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tensorflow/c/eager/c_api_experimental.cc
double growth_factor, int bucket_count) { return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() { return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor, bucket_count); }); }
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tensorflow/c/eager/parallel_device/parallel_device_lib.h
Status Shape(const std::vector<int64_t>** shape) const; TF_DataType dtype() const { return dtype_; } // Sets its output argument to a summary of the values of this tensor on every // component device. Status SummarizeValue(std::string& summary); std::vector<TensorHandlePtr> release_tensors() { return std::move(tensors_); } std::vector<TFE_TensorHandle*> tensors() const {
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tensorflow/c/eager/parallel_device/parallel_device_lib.cc
} } absl::optional<std::vector<std::unique_ptr<ParallelTensor>>> ParallelDevice::Join( const std::vector<PartialTensorShape>& expected_output_shapes, TF_Status* status) const { absl::optional<std::vector<std::unique_ptr<ParallelTensor>>> result; // Compute per-device per-output tensors std::vector<std::vector<TensorHandlePtr>> per_device_output_tensors;
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tensorflow/c/eager/parallel_device/parallel_device.cc
device->delete_device = &DeleteParallelDevice; device->execute = &ParallelDeviceExecute; std::vector<std::string> underlying_devices_vector; underlying_devices_vector.reserve(num_underlying_devices); for (int device_index = 0; device_index < num_underlying_devices; ++device_index) { underlying_devices_vector.push_back(underlying_devices[device_index]); } std::unique_ptr<ParallelDevice> parallel_device(
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tensorflow/c/eager/gradient_checker.cc
int num_elems = TF_TensorElementCount(theta_tensor); vector<float> theta_data(num_elems); memcpy(theta_data.data(), TF_TensorData(theta_tensor), TF_TensorByteSize(theta_tensor)); // Initialize space for the numerical gradient. vector<float> dtheta_approx(num_elems); // Get theta shape and store in theta_dims. int num_dims = TF_NumDims(theta_tensor); vector<int64_t> theta_dims(num_dims);
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