/***************************************************************************** Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ******************************************************************************/ using static Tensorflow.Python; namespace Tensorflow { /// /// Variable scope object to carry defaults to provide to `get_variable` /// public class VariableScope { public bool use_resource { get; set; } private _ReuseMode _reuse; public bool resue; private TF_DataType _dtype; string _name; public string name => _name; public string _name_scope { get; set; } public string original_name_scope => _name_scope; public VariableScope(bool reuse, string name = "", string name_scope = "", TF_DataType dtype = TF_DataType.TF_FLOAT) { _name = name; _name_scope = name_scope; _reuse = _ReuseMode.AUTO_REUSE; _dtype = dtype; } public RefVariable get_variable(_VariableStore var_store, string name, TensorShape shape = null, TF_DataType dtype = TF_DataType.DtInvalid, object initializer = null, // IInitializer or Tensor bool? trainable = null, bool? use_resource = null, bool validate_shape = true, VariableSynchronization synchronization = VariableSynchronization.Auto, VariableAggregation aggregation= VariableAggregation.None) { string full_name = !string.IsNullOrEmpty(this.name) ? this.name + "/" + name : name; return tf_with(ops.name_scope(null), scope => { if (dtype == TF_DataType.DtInvalid) dtype = _dtype; return var_store.get_variable(full_name, shape: shape, dtype: dtype, initializer: initializer, reuse: resue, trainable: trainable, synchronization: synchronization, aggregation: aggregation); }); } } }