TensorFlow.NET/src/TensorFlowNET.Core/Variables/RefVariable.cs

using System;
using System.Collections.Generic;
using System.Text;

namespace Tensorflow
{
    public class RefVariable : VariableV1
    {
        public bool _in_graph_mode = true;
        public Tensor _initial_value;
        public string _graph_key;
        public bool _trainable;
        public Tensor _variable;
        public Tensor _snapshot;

        private Operation _initializer_op;
        public Operation initializer => _initializer_op;
        public Operation op => _initializer_op;

        public RefVariable(object initial_value,
            bool trainable = true,
            List<string> collections = null,
            bool validate_shape = true,
            string caching_device = "",
            string name = "",
            TF_DataType dtype = TF_DataType.DtInvalid) : 
            base(initial_value, trainable, collections, validate_shape, caching_device, name, dtype)
        {
            _init_from_args(initial_value, trainable, collections, validate_shape, caching_device, name, dtype);
        }

        private void _init_from_args(object initial_value,
            bool trainable = true,
            List<string> collections = null,
            bool validate_shape = true,
            string caching_device = "",
            string name = "",
            TF_DataType dtype = TF_DataType.DtInvalid)
        {
            if (initial_value is null)
                throw new ValueError("initial_value must be specified.");

            var init_from_fn = false;

            if(collections == null)
            {
                collections = new List<string> { ops.GraphKeys.GLOBAL_VARIABLES };
            }

            // Store the graph key so optimizers know how to only retrieve variables from
            // this graph.
            _graph_key = ops.get_default_graph()._graph_key;

            _trainable = trainable;
            if (!collections.Contains(ops.GraphKeys.TRAINABLE_VARIABLES))
                collections.Add(ops.GraphKeys.TRAINABLE_VARIABLES);

            ops.init_scope();
            var values = init_from_fn ? new List<object>() : new List<object> { initial_value };
            using (var namescope = new ops.name_scope<object>(name, "Variable", values))
            {
                name = namescope;

                if (init_from_fn)
                {

                }
                else
                {
                    _initial_value = ops.convert_to_tensor(initial_value, name: "initial_value");
                }

                var shape = _initial_value.shape;
                dtype = _initial_value.dtype;
                _variable = gen_state_ops.variable_v2(shape, dtype, name);

                // Manually overrides the variable's shape with the initial value's.
                if (validate_shape)
                {
                    var initial_value_shape = _initial_value.shape;
                }

                // If 'initial_value' makes use of other variables, make sure we don't
                // have an issue if these other variables aren't initialized first by
                // using their initialized_value() method.

                _initializer_op = gen_state_ops.assign(_variable, _initial_value, validate_shape).op;

                if (!String.IsNullOrEmpty(caching_device))
                {

                }
                else
                {
                    _snapshot = gen_array_ops.identity(_variable, name = "read");
                }

                ops.add_to_collections(collections, this);
            }
        }

        public Tensor _ref()
        {
            return _variable;
        }

        public static implicit operator _VariableScopeStore(RefVariable variable)
        {
            return null;
        }

        public static implicit operator RefVariable(_VariableScopeStore store)
        {
            return null;
        }
    }
}