public FilterDataset(IDatasetV2 input_dataset,
Func <Tensor, bool> predicate_func) : base(input_dataset)
{
Func <Tensors, Tensors> predicate_func_update = x =>
{
var result = predicate_func(x);
return(constant_op.constant(result));
};
var func = new ConcreteFunction($"{predicate_func.Method.Name}_{Tensorflow.ops.uid_function()}");
func.Enter();
var inputs = new Tensors();
foreach (var input in input_dataset.element_spec)
{
inputs.Add(tf.placeholder(input.dtype, shape: input.shape, name: "arg"));
}
var outputs = predicate_func_update(inputs);
func.ToGraph(inputs, outputs);
func.Exit();
structure = func.OutputStructure;
variant_tensor = ops.filter_dataset(input_dataset.variant_tensor,
func,
output_types,
output_shapes);
}
public MapDataset(IDatasetV2 input_dataset,
Func <Tensors, Tensors> map_func,
bool use_inter_op_parallelism = true,
bool preserve_cardinality = false,
bool use_legacy_function = false) : base(input_dataset)
{
var func = new ConcreteFunction($"{map_func.Method.Name}_{Tensorflow.ops.uid_function()}");
func.Enter();
var inputs = new Tensors();
foreach (var input in input_dataset.element_spec)
{
inputs.Add(tf.placeholder(input.dtype, shape: input.shape, name: "arg"));
}
var outputs = map_func(inputs);
func.ToGraph(inputs, outputs);
func.Exit();
structure = func.OutputStructure;
variant_tensor = ops.map_dataset(input_dataset.variant_tensor,
func,
output_types,
output_shapes,
use_inter_op_parallelism: use_inter_op_parallelism,
preserve_cardinality: preserve_cardinality);
}
public static (Tensors, Tensor) clip_by_global_norm(Tensor[] t_list, float clip_norm, Tensor use_norm = null, string name = null)
{
use_norm = global_norm(t_list, name);
return(tf_with(ops.name_scope(name, "clip_by_global_norm", t_list), delegate
{
// Calculate L2-norm, clip elements by ratio of clip_norm to L2-norm
var scale_for_finite = clip_norm * math_ops.minimum(
1.0f / use_norm,
constant_op.constant(1.0, dtype: use_norm.dtype) / clip_norm);
// If use_norm is any finite number, this is a no-op. For inf/-inf/NaN,
// this will make scale NaN.
var scale = scale_for_finite + (use_norm - use_norm);
Tensors values_clipped = new Tensors();
foreach (var(i, v) in enumerate(t_list))
{
values_clipped.Add(array_ops.identity(v * scale, name: $"{name}_{i}"));
}
return (values_clipped, use_norm);
}));
