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Oceania2018Oceania2018
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add VariableScope and _VariableStore
1 parent 7c420df commit bf45277

11 files changed

Lines changed: 265 additions & 12 deletions

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‎src/TensorFlowNET.Core/Operations/IInitializer.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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namespace Tensorflow
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{
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public interface IInitializer
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{
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Tensor call(TensorShape shape, TF_DataType dtype);
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object get_config();
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}
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}

‎src/TensorFlowNET.Core/Operations/tf.init_ops.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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namespace Tensorflow
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{
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public static partial class tf
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{
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public static IInitializer zeros_initializer => new Zeros();
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public class Zeros : IInitializer
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{
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private TF_DataType dtype;
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public Zeros(TF_DataType dtype = TF_DataType.TF_FLOAT)
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{
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this.dtype = dtype;
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}
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public Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid)
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{
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if (dtype == TF_DataType.DtInvalid)
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dtype = this.dtype;
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return array_ops.zeros(shape, dtype);
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}
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public object get_config()
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{
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return new { dtype = dtype.name() };
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}
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}
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}
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}

‎src/TensorFlowNET.Core/Tensors/dtypes.cs‎

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@@ -71,6 +71,11 @@ public static TF_DataType as_base_dtype(this TF_DataType type)
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type;
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}
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public static int name(this TF_DataType type)
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{
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return (int)type;
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}
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public static DataType as_base_dtype(this DataType type)
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{
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return (int)type > 100 ?

‎src/TensorFlowNET.Core/Variables/VariableAggregation.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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namespace Tensorflow
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{
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public enum VariableAggregation
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{
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NONE = 0,
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SUM = 1,
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MEAN = 2,
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ONLY_FIRST_REPLICA = 3 // ONLY_FIRST_TOWER
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}
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}

‎src/TensorFlowNET.Core/Variables/VariableScope.cs‎

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@@ -6,6 +6,35 @@ namespace Tensorflow
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{
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public class VariableScope
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{
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public bool? use_resource { get; set; }
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public bool use_resource { get; set; }
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private _ReuseMode _reuse { get; set; }
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private object _regularizer;
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private TF_DataType _dtype;
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public string name { get; set; }
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public VariableScope()
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{
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_reuse = _ReuseMode.AUTO_REUSE;
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}
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public RefVariable get_variable(_VariableStore var_store,
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string name,
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TensorShape shape = null,
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TF_DataType dtype = TF_DataType.DtInvalid,
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VariableSynchronization synchronization = VariableSynchronization.AUTO,
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VariableAggregation aggregation= VariableAggregation.NONE)
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{
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string full_name = !string.IsNullOrEmpty(this.name) ? this.name + "/" + name : name;
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return Python.with<ops.name_scope, Tensor>(new ops.name_scope(""), scope =>
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{
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if (dtype == TF_DataType.DtInvalid)
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dtype = _dtype;
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return var_store.get_variable(full_name);
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});
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}
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}
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}

‎src/TensorFlowNET.Core/Variables/_ReuseMode.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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namespace Tensorflow
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{
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/// <summary>
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/// Mode for variable access within a variable scope.
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/// </summary>
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public enum _ReuseMode
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{
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// Indicates that variables are to be fetched if they already exist or
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// otherwise created.
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AUTO_REUSE = 1
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}
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}

‎src/TensorFlowNET.Core/Variables/_VariableStore.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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namespace Tensorflow
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{
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/// <summary>
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/// Variable store that carries a number of named Variables.
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/// </summary>
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public class _VariableStore
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{
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private Dictionary<string, object> _vars;
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private Dictionary<string, object> _partitioned_vars;
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private bool _store_eager_variables;
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public _VariableStore()
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{
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_vars = new Dictionary<string, object>();
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_partitioned_vars = new Dictionary<string, object>();
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_store_eager_variables = false;
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}
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public RefVariable get_variable(string name,
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TensorShape shape = null,
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TF_DataType dtype = TF_DataType.TF_FLOAT,
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IInitializer initializer = null,
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bool trainable = false,
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bool validate_shape = true,
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VariableSynchronization synchronization = VariableSynchronization.AUTO,
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VariableAggregation aggregation = VariableAggregation.NONE)
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{
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dtype = dtype.as_base_dtype();
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trainable = variable_scope._get_trainable_value(synchronization, trainable);
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return _true_getter(name,
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shape: shape,
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dtype: dtype,
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initializer: initializer,
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trainable: trainable,
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validate_shape: validate_shape,
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synchronization: synchronization,
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aggregation: aggregation);
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}
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private RefVariable _true_getter(string name,
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TensorShape shape = null,
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TF_DataType dtype = TF_DataType.DtInvalid,
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IInitializer initializer = null,
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bool trainable = false,
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bool validate_shape = true,
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VariableSynchronization synchronization = VariableSynchronization.AUTO,
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VariableAggregation aggregation = VariableAggregation.NONE)
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{
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return _get_single_variable(name: name);
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}
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private RefVariable _get_single_variable(string name,
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TensorShape shape = null,
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TF_DataType dtype = TF_DataType.DtInvalid,
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IInitializer initializer = null,
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bool reuse = false,
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bool trainable = false,
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bool validate_shape = false,
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VariableSynchronization synchronization = VariableSynchronization.AUTO,
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VariableAggregation aggregation = VariableAggregation.NONE)
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{
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if (_vars.ContainsKey(name))
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{
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if (!reuse)
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{
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var var = _vars[name];
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}
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throw new NotImplementedException("_get_single_variable");
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}
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throw new NotImplementedException("_get_single_variable");
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}
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}
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}

‎src/TensorFlowNET.Core/Variables/tf.variable.cs‎

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@@ -11,5 +11,15 @@ public static Operation global_variables_initializer()
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var g = variables.global_variables();
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return variables.variables_initializer(g.ToArray());
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}
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public static RefVariable get_variable(string name,
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TensorShape shape = null,
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IInitializer initializer = null,
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VariableSynchronization synchronization = VariableSynchronization.AUTO,
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VariableAggregation aggregation = VariableAggregation.NONE)
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{
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var store = variable_scope._get_default_variable_store();
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return variable_scope.get_variable_scope().get_variable(store, name, shape: shape);
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}
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}
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}

‎src/TensorFlowNET.Core/Variables/variable_scope.py.cs‎

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@@ -6,6 +6,7 @@ namespace Tensorflow
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{
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public class variable_scope
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{
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public static string _VARSTORE_KEY = "__variable_store";
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public static string _VARSCOPESTORE_KEY = "__varscope";
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public static bool _DEFAULT_USE_RESOURCE = false;
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}
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}
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public static _VariableStore _get_default_variable_store()
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{
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var store = ops.get_collection(_VARSTORE_KEY);
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if (store != null)
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return (store as List<_VariableStore>)[0];
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var store1 = new _VariableStore();
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ops.add_to_collection(_VARSTORE_KEY, store1);
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return store1;
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}
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public static VariableScope get_variable_scope()
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{
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return get_variable_scope_store().current_scope;
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return ret;
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}
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public static bool _get_trainable_value(VariableSynchronization synchronization, bool? trainable = null)
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public static bool _get_trainable_value(VariableSynchronization synchronization, bool trainable = true)
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{
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if(synchronization == VariableSynchronization.ON_READ)
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if (synchronization == VariableSynchronization.ON_READ)
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{
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if (trainable.Value)
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if (trainable)
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throw new ValueError("Synchronization value can be set to " +
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"VariableSynchronization.ON_READ only for non-trainable variables. " +
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"You have specified trainable=True and " +
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"synchronization=VariableSynchronization.ON_READ.");
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else
78-
trainable = false;
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}
80-
else if (!trainable.HasValue)
81-
{
82-
trainable = true;
83-
}
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85-
return trainable.Value;
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return trainable;
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}
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}
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}

‎test/TensorFlowNET.UnitTest/TrainSaverTest.cs‎

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using Microsoft.VisualStudio.TestTools.UnitTesting;
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using System;
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using System.Collections.Generic;
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using System.Text;
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using Tensorflow;
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namespace TensorFlowNET.UnitTest
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{
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[TestClass]
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public class TrainSaverTest
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{
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[TestMethod]
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public void Save()
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{
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var v1 = tf.get_variable("v1", shape: new TensorShape(3), initializer: tf.zeros_initializer);
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var v2 = tf.get_variable("v2", shape: new TensorShape(5), initializer: tf.zeros_initializer);
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}
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}
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}

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