fix tf.layers.conv2d

src/TensorFlowNET.Core/APIs/tf.init.cs

@@ -14,7 +14,7 @@ namespace Tensorflow
 
         public static variable_scope variable_scope(string name,
                string default_name = null,
-               object values = null,
+               Tensor[] values = null,
                bool auxiliary_name_scope = true) => new variable_scope(name, 
                    default_name, 
                    values,
@@ -22,7 +22,7 @@ namespace Tensorflow
 
         public static variable_scope variable_scope(VariableScope scope,
               string default_name = null,
-              object values = null,
+              Tensor[] values = null,
               bool? reuse = null,
               bool auxiliary_name_scope = true) => new variable_scope(scope,
                   default_name,

src/TensorFlowNET.Core/Keras/Layers/Layer.cs

@@ -4,6 +4,7 @@ using System.Linq;
 using System.Text;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Utils;
+using Tensorflow.Train;
 using static Tensorflow.Python;
 
 namespace Tensorflow.Keras.Layers
@@ -14,7 +15,7 @@ namespace Tensorflow.Keras.Layers
     /// as convolution, batch norm, etc. These operations require managing weights,
     /// losses, updates, and inter-layer connectivity.
     /// </summary>
-    public class Layer : CheckpointableBase
+    public class Layer : AutoTrackable
     {
         /// <summary>
         /// Indicates whether `build` needs to be called upon layer call, to create
@@ -84,32 +85,35 @@ namespace Tensorflow.Keras.Layers
             // models using the functional API).
             bool build_graph = tf_utils.are_all_symbolic_tensors(input_list);
 
-            // Handle Keras mask propagation from previous layer to current layer.
-            Python.with(ops.name_scope(_name_scope()), delegate
+            if (build_graph)
             {
-                /*if (!built)
-                {
-                    _maybe_build(inputs);
-                    built = true;
-                }*/
+                // Only create Keras history if at least one tensor originates from a
+                // `keras.Input`. Otherwise this Layer may be being used outside the Keras
+                // framework.
+                // base_layer_utils.create_keras_history(inputs)
+            }
 
-                if (build_graph)
+            // with base_layer_utils.call_context(self):
+
+            // Handle Keras mask propagation from previous layer to current layer.
+            // with base_layer_utils.call_context(self):
+            // Check input assumptions set after layer building, e.g. input shape.
+            if (build_graph)
+            {
+                // Symbolic execution on symbolic tensors. We will attempt to build
+                // the corresponding TF subgraph inside `backend.get_graph()`
+                var graph = backend.get_graph().as_default();
+                with(ops.name_scope(_name_scope()), delegate
                 {
-                    // Symbolic execution on symbolic tensors. We will attempt to build
-                    // the corresponding TF subgraph inside `backend.get_graph()`
-                    var graph = backend.get_graph().as_default();
-                    with(ops.name_scope(_name_scope()), delegate
-                    {
-                        // Build layer if applicable (if the `build` method has been
-                        // overridden).
-                        _maybe_build(inputs[0]);
-                    });
-
-                    outputs = call(inputs[0], training: training);
-                    _handle_activity_regularization(inputs[0], outputs);
-                    _set_mask_metadata(inputs[0], outputs, null);
-                }
-            });
+                    // Build layer if applicable (if the `build` method has been
+                    // overridden).
+                    _maybe_build(inputs[0]);
+                });
+
+               outputs = call(inputs[0], training: training);
+                _handle_activity_regularization(inputs[0], outputs);
+                _set_mask_metadata(inputs[0], outputs, null);
+            }
 
             return outputs;
         }
@@ -147,6 +151,8 @@ namespace Tensorflow.Keras.Layers
             // Check input assumptions set before layer building, e.g. input rank.
             if (built)
                 return;
+            if (_dtype == TF_DataType.DtInvalid)
+                _dtype = input.dtype;
 
             build(input.GetShape());
             built = true;
@@ -170,10 +176,21 @@ namespace Tensorflow.Keras.Layers
             if (trainable == null)
                 trainable = true;
 
+            // Initialize variable when no initializer provided
+            if(initializer == null)
+            {
+                // If dtype is DT_FLOAT, provide a uniform unit scaling initializer
+                if (dtype.is_floating())
+                    initializer = tf.glorot_uniform_initializer;
+                else if (dtype.is_integer())
+                    initializer = tf.zeros_initializer;
+                else
+                    throw new ValueError($"An initializer for variable {name} of type {dtype.as_base_dtype()} is required for layer {this.name}");
+            }
             var variable = _add_variable_with_custom_getter(name,
                 shape,
                 dtype: dtype,
-                //getter: getter == null ? base_layer_utils.make_variable : getter,
+                getter: getter, // getter == null ? base_layer_utils.make_variable : getter,
                 overwrite: true,
                 initializer: initializer,
                 trainable: trainable.Value);

src/TensorFlowNET.Core/Keras/backend.cs

@@ -12,10 +12,11 @@ namespace Tensorflow.Keras
         /// Allows to give unique autogenerated names to layers, in a graph-specific way.
         /// </summary>
         public static Dictionary<string, Dictionary<(string, string), int>> PER_GRAPH_LAYER_NAME_UIDS = new Dictionary<string, Dictionary<(string, string), int>>();
-
+        public static Dictionary<string, RefVariable> _GRAPH_VARIABLES = new Dictionary<string, RefVariable>();
         public static void track_variable(RefVariable v)
         {
-
+            var graph = v.graph;
+            _GRAPH_VARIABLES[graph.graph_key] = v;
         }
 
         public static Tensor placeholder(int[] shape = null, 

src/TensorFlowNET.Core/Layers/Layer.cs

@@ -51,9 +51,14 @@ namespace Tensorflow.Layers
                     auxiliary_name_scope: false);
             }
 
-            with(scope_context_manager, scope2 => _current_scope = scope2);
-            // Actually call layer
-            var outputs = base.__call__(new Tensor[] { inputs }, training: training);
+            Tensor outputs = null;
+            with(scope_context_manager, scope2 =>
+            {
+                _current_scope = scope2;
+                // Actually call layer
+                outputs = base.__call__(new Tensor[] { inputs }, training: training);
+            });
+
 
             // Update global default collections.
             _add_elements_to_collection(_updates.ToArray(), new string[] { ops.GraphKeys.UPDATE_OPS });
@@ -80,6 +85,17 @@ namespace Tensorflow.Layers
             }
         }
 
+        /// <summary>
+        /// Adds a new variable to the layer, or gets an existing one; returns it.
+        /// </summary>
+        /// <param name="name"></param>
+        /// <param name="shape"></param>
+        /// <param name="dtype"></param>
+        /// <param name="initializer"></param>
+        /// <param name="trainable"></param>
+        /// <param name="synchronization"></param>
+        /// <param name="aggregation"></param>
+        /// <returns></returns>
         protected virtual RefVariable add_weight(string name,
             int[] shape,
             TF_DataType dtype = TF_DataType.DtInvalid,
@@ -157,7 +173,10 @@ namespace Tensorflow.Layers
                 else
                 {
                     with(tf.variable_scope(scope, default_name: _base_name),
-                        captured_scope => _scope = captured_scope);
+                        captured_scope =>
+                        {
+                            _scope = captured_scope;
+                        });
                 }
 
             }

src/TensorFlowNET.Core/Train/AutoTrackable.cs

@@ -0,0 +1,10 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Train
+{
+    public abstract class AutoTrackable : Trackable
+    {
+    }
+}

src/TensorFlowNET.Core/Train/Checkpointable/CheckpointableBase.cs

@@ -5,7 +5,7 @@ using Tensorflow.Train;
 
 namespace Tensorflow
 {
-    public abstract class CheckpointableBase : Trackable
+    public abstract class CheckpointableBase : AutoTrackable
     {
 
     }

src/TensorFlowNET.Core/Train/Trackable.cs

@@ -18,7 +18,13 @@ namespace Tensorflow.Train
             bool overwrite = false,
             bool trainable = false)
         {
+            var checkpoint_initializer = true;
             var new_variable = getter(name, shape, dtype, initializer, trainable);
+
+            // If we set an initializer and the variable processed it, tracking will not
+            // assign again. It will add this variable to our dependencies, and if there
+            // is a non-trivial restoration queued, it will handle that. This also
+            // handles slot variables.
             if (!overwrite || new_variable is RefVariable)
                 return _track_checkpointable(new_variable, name: name,
                                         overwrite: overwrite);

src/TensorFlowNET.Core/Variables/PureVariableScope.cs

@@ -35,7 +35,7 @@ namespace Tensorflow
             _old_name_scope = old_name_scope;
             _var_store = variable_scope._get_default_variable_store();
             _var_scope_store = variable_scope.get_variable_scope_store();
-            _new_name = _scope._name;
+            _new_name = _scope.name;
 
             string name_scope = _scope._name_scope;
             variable_scope_object = new VariableScope(_reuse,
@@ -55,7 +55,7 @@ namespace Tensorflow
             }
             else
             {
-                _new_name = string.IsNullOrEmpty(_old._name) ? _name : _old._name + "/" + _name;
+                _new_name = string.IsNullOrEmpty(_old.name) ? _name : _old.name + "/" + _name;
                 _reuse = _reuse || _old.resue;
                 string name_scope = _old_name_scope == null ? _name : _old_name_scope;
 

src/TensorFlowNET.Core/Variables/VariableScope.cs

@@ -15,7 +15,8 @@ namespace Tensorflow
         public bool resue;
 
         private TF_DataType _dtype;
-        public string _name { get; set; }
+        string _name;
+        public string name => _name;
         public string _name_scope { get; set; }
         public string original_name_scope => _name_scope;
 

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

@@ -19,15 +19,17 @@ namespace Tensorflow
         private string _name;
         private VariableScope _scope;
         private string _default_name;
-        private object _values;
+        private Tensor[] _values;
         private ops.NameScope _current_name_scope;
         private bool _auxiliary_name_scope;
         private PureVariableScope _cached_pure_variable_scope;
         private bool? _reuse;
+        bool _in_graph_mode;
+        protected Graph _graph;
 
         public variable_scope(string name, 
-            string default_name = "", 
-            object values = null,
+            string default_name = "",
+            Tensor[] values = null,
             bool? reuse = null,
             bool auxiliary_name_scope = true)
         {
@@ -45,7 +47,7 @@ namespace Tensorflow
 
         public variable_scope(VariableScope scope,
             string default_name = "",
-            object values = null,
+            Tensor[] values = null,
             bool? reuse = null,
             bool auxiliary_name_scope = true)
         {
@@ -58,6 +60,11 @@ namespace Tensorflow
             if (_default_name == null && _scope == null)
                 throw new TypeError("If default_name is None then scope is required");
 
+            if (_values == null)
+                _values = new Tensor[0];
+            _in_graph_mode = true;
+            if (_in_graph_mode)
+                _graph = ops._get_graph_from_inputs(_values);
             _auxiliary_name_scope = auxiliary_name_scope;
         }
 
@@ -87,7 +94,7 @@ namespace Tensorflow
 
             if (_name != null || _scope != null)
             {
-                var name_scope = _name == null ? _scope._name.Split('/').Last() : _name;
+                var name_scope = _name == null ? _scope.name.Split('/').Last() : _name;
                 if (name_scope != null || current_name_scope != null)
                     current_name_scope = ops.name_scope(name_scope);
                 current_name_scope.__enter__();
@@ -124,7 +131,7 @@ namespace Tensorflow
         {
             var var_scope_store = get_variable_scope_store();
             var current_scope = get_variable_scope();
-            string name = !string.IsNullOrEmpty(current_scope._name) ? current_scope._name + "/" + prefix : prefix;
+            string name = !string.IsNullOrEmpty(current_scope.name) ? current_scope.name + "/" + prefix : prefix;
             if (var_scope_store.variable_scope_count(name) == 0)
                 return prefix;
             throw new NotImplementedException("_get_unique_variable_scope");

test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs

@@ -174,8 +174,9 @@ namespace TensorFlowNET.Examples
                 x_emb = tf.expand_dims(x_emb, -1);
             });
 
-            foreach(var filter_size in filter_sizes)
+            for(int len = 0; len < filter_sizes.Rank; len++)
             {
+                int filter_size = filter_sizes.GetLength(len);
                 var conv = tf.layers.conv2d(
                     x_emb,
                     filters: num_filters,
@@ -183,6 +184,12 @@ namespace TensorFlowNET.Examples
                     strides: new int[] { 1, 1 },
                     padding: "VALID",
                     activation: tf.nn.relu());
+
+                var pool = tf.layers.max_pooling2d(
+                    conv,
+                    pool_size: new[] { document_max_len - filter_size + 1, 1 },
+                    strides: new[] { 1, 1 },
+                    padding: "VALID");
             }
 
             return graph;