/// <summary>
/// Create a new variable handle, optionally copying in `extra_handle_data`
/// </summary>
/// <param name="shape"></param>
/// <param name="dtype"></param>
/// <param name="shared_name"></param>
/// <param name="name"></param>
/// <param name="graph_mode"></param>
/// <param name="initial_value"></param>
/// <returns></returns>
public static Tensor variable_handle_from_shape_and_dtype(TensorShape shape, TF_DataType dtype,
string shared_name, string name, bool graph_mode, Tensor initial_value = null)
{
var container = ops.get_default_graph().Container;
var handle = gen_resource_variable_ops.var_handle_op(shape: shape,
dtype: dtype,
shared_name: shared_name,
name: name,
container: container);
if (initial_value == null)
{
initial_value = handle;
}
if (graph_mode)
{
var full_handle_data = _combine_handle_data(handle, initial_value);
_set_handle_shapes_and_types(handle, full_handle_data, graph_mode);
return(handle);
}
else
{
// We do not want two distinct ResourceVariable objects for the same
// underlying resource in the runtime.
// When in eager mode, explicitly ensure so here. When in graph mode, it's
// ensured by always generating different variable names.
var exists = gen_resource_variable_ops.var_is_initialized_op(handle);
// We create an assert Op instead of checking right away in order to be
// compatible with ASYNC execution mode. Further, since not all devices
// support string tensors, we encode the assertion string in the Op name
/*gen_logging_ops.assert(gen_math_ops.logical_not(exists),
* new[] { exists },
* name: "EagerVariableNameReuse");*/
var handle_data = new HandleData();
handle_data.IsSet = true;
handle_data.ShapeAndType.Add(new HandleShapeAndType
{
Dtype = dtype.as_datatype_enum(),
Shape = shape.as_proto()
});
_set_handle_shapes_and_types(handle, handle_data, graph_mode);
return(handle);
}
}
public TensorShapeProto _MakeShape(TensorShape shape, AttrDef attr_def)
{
return(shape.as_proto());
}