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tensorflow/c/eager/unified_api_testutil.h
using Model = std::function<Status(AbstractContext*, absl::Span<AbstractTensorHandle* const>, absl::Span<AbstractTensorHandle*>)>; // Runs `model` maybe wrapped in a function call op. This can be thought as // being equivalent to the following python code. // // if use_function: // outputs = tf.function(model)(inputs) // else: // outputs = model(inputs)
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tensorflow/c/eager/c_api.cc
} // Set server_def on the context, possibly updating it. // TODO(b/291142876) Simplify TFE_ContextSetServerDefWithTimeoutAndRetries and // TFE_ContextUpdateServerDefWithTimeout to be simple wrappers around the same // C++ function. // Retries are used for CreateContext calls, which is used in // ParameterServerStrategy initialization to be robust to worker preemption.
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tensorflow/c/eager/graph_function.h
using tensorflow::AbstractFunction; // Thin wrapper around a FunctionDef. class GraphFunction : public AbstractFunction { public: explicit GraphFunction(FunctionDef fdef); ~GraphFunction() override; // GraphFunction maybe stay alive for the duration of the returned // FunctionDef. Status GetFunctionDef(const FunctionDef** fdef) override; // Returns a shared reference to the wrapped function.
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tensorflow/api_template.__init__.py
# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import. _os.environ["TF2_BEHAVIOR"] = "1" from tensorflow.python import tf2 as _tf2 _tf2.enable() # API IMPORTS PLACEHOLDER # WRAPPER_PLACEHOLDER # Make sure directory containing top level submodules is in # the __path__ so that "from tensorflow.foo import bar" works. # We're using bitwise, but there's nothing special about that.
Python - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Mar 05 06:27:59 GMT 2024 - 6.7K bytes - Viewed (3) -
RELEASE.md
## Keras Keras is a framework built on top of the TensorFlow. See more details on the [Keras website](https://keras.io/). ### Breaking Changes * Removed the Keras scikit-learn API wrappers (`KerasClassifier` and `KerasRegressor`), which had been deprecated in August 2021. We recommend using [SciKeras](https://github.com/adriangb/scikeras) instead.
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tensorflow/api_template_v1.__init__.py
from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader # API IMPORTS PLACEHOLDER # WRAPPER_PLACEHOLDER if "dev" in __version__: # pylint: disable=undefined-variable _logging.warning(""" TensorFlow's `tf-nightly` package will soon be updated to TensorFlow 2.0.
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tensorflow/c/eager/c_api_experimental.h
// which is passed to the functions referenced in the TFE_CustomDevice struct // `device` (execute, delete_device, etc.). It can for example contain the // names of wrapped devices. // // There are currently no graph semantics implemented for registered custom // devices, so executing tf.functions which contain operations placed on the // custom devices will fail. //
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tensorflow/c/eager/unified_api_testutil.cc
TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingContext>(ctx)->AddParameter( input->DataType(), shape, &handle)); params->emplace_back(handle); } return absl::OkStatus(); } // Runs `model` maybe wrapped in a function. Status RunModel(Model model, AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs, absl::Span<AbstractTensorHandle*> outputs, bool use_function) {
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tensorflow/c/eager/abstract_function.h
AbstractFunctionKind getKind() const { return kind_; } // Returns the AbstractFunction as a FunctionDef. virtual Status GetFunctionDef(const FunctionDef**) = 0; // Returns a shared reference to the wrapped function. virtual absl::StatusOr<core::RefCountPtr<FunctionRecord>> GetFunctionRecord() = 0; private: const AbstractFunctionKind kind_; }; using AbstractFunctionPtr =
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