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tensorflow/c/checkpoint_reader.h
// partitioned tensor are combined into a single entry. const TensorSliceReader::VarToDataTypeMap& GetVariableToDataTypeMap() const; // Attempts to look up the tensor named "name" and stores the found result in // "out_tensor". void GetTensor(const string& name, std::unique_ptr<tensorflow::Tensor>* out_tensor, TF_Status* out_status) const; private:
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tensorflow/c/eager/immediate_execution_tensor_handle.h
// // If false, a caller might implement pretty-printing by resolving and // iterating over the resulting tensor. This may still be viable if resolving // the handle loses information, but `SummarizeValue` would be more precise. virtual bool PreferCustomSummarizer() const { return false; } // Returns a string which summarizes the value of this TensorHandle, for
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tensorflow/c/env.h
TF_CAPI_EXPORT extern void TF_NewWritableFile(const char* filename, TF_WritableFileHandle** handle, TF_Status* status); // Closes the given handle and frees its memory. If there was a problem closing // the file, it is indicated by status. Memory is freed in any case. TF_CAPI_EXPORT extern void TF_CloseWritableFile(TF_WritableFileHandle* handle,
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tensorflow/c/eager/tape.h
// function and deleted (as the backprop code creates lots of gradients the user // is not interested in). // // BackwardFunction needs to be a closure which stores intermediate activations // from the forward computation and calls a vector-jacobian product function // (also known as adjoint function) to compute, given downstream gradients, // upstream gradients. //
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tensorflow/c/eager/c_api_unified_experimental.h
// ----------------------------------------------------------------------------- // Core APIs // ----------------------------------------------------------------------------- // A TF_ExecutionContext stores knowledge about how to execute an operation. // E.g. it could know whether we're in eager mode or graph mode, keeps track // of gradient tapes, etc. typedef struct TF_ExecutionContext TF_ExecutionContext;
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tensorflow/c/eager/gradients.h
// `GradientTape::Watch`.) The op_id is simply a unique index assigned to each // op executed under the tape. A separate map (`tensorflow::eager::OpTape`) // maintains the map from `op_id` to a `OpTapeEntry` which stores the `op_type`, // inputs and outputs and the gradient function These data structures combined // allow us to trace the data dependencies between operations and hence compute // gradients. //
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RELEASE.md
* Normalization can now accept mean and variance values as init args. * In `Attention` and `AdditiveAttention` layers, the `call()` method now accepts a `return_attention_scores` argument. When set to True, the layer returns the attention scores as an additional output argument. * Adds `tf.metrics.log_cosh` and `tf.metrics.logcosh` API entrypoints with the same implementation as their `tf.losses` equivalent.
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tensorflow/c/experimental/filesystem/filesystem_interface.h
/// been flushed and persisted. /// /// DEFAULT IMPLEMENTATION: No op. void (*sync)(const TF_WritableFile* file, TF_Status* status); /// Closes `*file`. /// /// Flushes all buffers and deallocates all resources. /// /// Calling `close` must not result in calling `cleanup`. /// /// Core TensorFlow will never call `close` twice.
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