public Tensor Activate(Tensor features, string name = null)
{
OpDefLibrary _op_def_lib = new OpDefLibrary();
var _op = _op_def_lib._apply_op_helper("Relu", name: name, args: new
{
features
});
return(_op.outputs[0]);
}
/// <summary>
/// Computes rectified linear: `max(features, 0)`.
/// </summary>
/// <param name="features">A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`, `qint8`.</param>
/// <param name="name">A name for the operation (optional).</param>
/// <returns>A `Tensor`. Has the same type as `features`.</returns>
public static Tensor relu(Tensor features, string name = null)
{
//_ctx = _context._context
//if _ctx is not None and _ctx._eager_context.is_eager:
// try:
// _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
// _ctx._context_handle, _ctx._eager_context.device_name, "Relu", name,
// _ctx._post_execution_callbacks, features)
// return _result
// except _core._FallbackException:
// try:
// return relu_eager_fallback(
// features, name=name, ctx=_ctx)
// except _core._SymbolicException:
// pass # Add nodes to the TensorFlow graph.
// except (TypeError, ValueError):
// result = _dispatch.dispatch(
// relu, features=features, name=name)
// if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
// return result
// raise
// except _core._NotOkStatusException as e:
// if name is not None:
// message = e.message + " name: " + name
// else:
// message = e.message
// _six.raise_from(_core._status_to_exception(e.code, message), None)
//# Add nodes to the TensorFlow graph.
//try:
OpDefLibrary _op_def_lib = new OpDefLibrary();
var _op = _op_def_lib._apply_op_helper("Relu", name: name, args: new { features });
return(_op.outputs[0]);
//except (TypeError, ValueError):
// result = _dispatch.dispatch(
// relu, features=features, name=name)
// if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
// return result
// raise
// var _result = _op.outputs.ToArray();
//_inputs_flat = _op.inputs
//_attrs = ("T", _op.get_attr("T"))
//_execute.record_gradient(
// "Relu", _inputs_flat, _attrs, _result, name)
//_result, = _result
// return _result;
}
