fix unique_layer_name

src/TensorFlowNET.Core/Graphs/Graph.cs

@@ -69,7 +69,8 @@ namespace Tensorflow
         private List<Tensor> _unfeedable_tensors = new List<Tensor>();
 
         public string _name_stack = "";
-        public string _graph_key;
+        private string _graph_key;
+        public string graph_key => _graph_key;
         public string _last_loss_reduction;
 
         public Status Status { get; }

src/TensorFlowNET.Core/Keras/Engine/Sequential.cs

@@ -19,6 +19,10 @@ namespace Tensorflow.Keras.Engine
             
         }
 
+        /// <summary>
+        /// Adds a layer instance on top of the layer stack.
+        /// </summary>
+        /// <param name="layer"></param>
         public void add(Layer layer)
         {
             built = false;
@@ -32,7 +36,7 @@ namespace Tensorflow.Keras.Engine
                     var x = keras.layers.Input(
                       batch_shape: batch_shape,
                       dtype: dtype,
-                      name: layer._name + "_input");
+                      name: layer.name + "_input");
 
                     // This will build the current layer
                     // and create the node connecting the current layer

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 static Tensorflow.Python;
 
 namespace Tensorflow.Keras.Layers
 {
@@ -33,7 +34,8 @@ namespace Tensorflow.Keras.Layers
         protected InputSpec input_spec;
         protected bool supports_masking;
         protected List<RefVariable> _trainable_weights;
-        public string _name;
+        private string _name;
+        public string name => _name;
         protected string _base_name;
         protected bool _compute_previous_mask;
         protected List<Operation> _updates;
@@ -85,17 +87,24 @@ namespace Tensorflow.Keras.Layers
             // Handle Keras mask propagation from previous layer to current layer.
             Python.with(ops.name_scope(_name_scope()), delegate
             {
-                if (!built)
+                /*if (!built)
                 {
                     _maybe_build(inputs);
                     built = true;
-                }
+                }*/
 
                 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();
+                    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);
@@ -130,13 +139,17 @@ namespace Tensorflow.Keras.Layers
 
         protected virtual string _name_scope()
         {
-            return null;
+            return name;
         }
 
-        protected void _maybe_build(Tensor[] inputs)
+        protected void _maybe_build(Tensor input)
         {
-            var input_list = inputs;
-            build(input_list[0].GetShape());
+            // Check input assumptions set before layer building, e.g. input rank.
+            if (built)
+                return;
+
+            build(input.GetShape());
+            built = true;
         }
 
         protected virtual void build(TensorShape input_shape)
@@ -160,7 +173,7 @@ namespace Tensorflow.Keras.Layers
             var variable = _add_variable_with_custom_getter(name,
                 shape,
                 dtype: dtype,
-                getter: getter == null ? base_layer_utils.make_variable : getter,
+                //getter: getter == null ? base_layer_utils.make_variable : getter,
                 overwrite: true,
                 initializer: initializer,
                 trainable: trainable.Value);
@@ -176,12 +189,12 @@ namespace Tensorflow.Keras.Layers
             _updates.AddRange(updates_op);
         }
 
-        protected virtual void _init_set_name(string name)
+        protected virtual void _init_set_name(string name, bool zero_based = true)
         {
-            string base_name = name;
             if (name == null)
-                (_name, base_name) = _make_unique_name();
-            _base_name = base_name;
+                _name = base_layer_utils.unique_layer_name(generic_utils.to_snake_case(this.GetType().Name), zero_based: zero_based);
+            else
+                _name = name;
         }
 
         protected virtual (string, string) _make_unique_name()

src/TensorFlowNET.Core/Keras/Sequence.cs

@@ -30,26 +30,6 @@ namespace Tensorflow.Keras
             object value = null)
         {
             int[] length = new int[sequences.size];
-            switch (sequences.dtype.Name)
-            {
-                case "Object":
-                    for (int i = 0; i < sequences.size; i++)
-                    {
-                        switch (sequences.Data<object>(i))
-                        {
-                            case string data:
-                                length[i] = Regex.Matches(data, ",").Count;
-                                break;
-                        }
-                    }
-                    break;
-                case "Int32":
-                    for (int i = 0; i < sequences.size; i++)
-                        length[i] = Regex.Matches(sequences.Data<object>(i).ToString(), ",").Count;
-                    break;
-                default:
-                    throw new NotImplementedException($"pad_sequences: {sequences.dtype.Name}");
-            }
 
             if (maxlen == null)
                 maxlen = length.Max();

src/TensorFlowNET.Core/Keras/Utils/base_layer_utils.cs

@@ -1,18 +1,39 @@
 using System;
 using System.Collections.Generic;
+using System.Linq;
 using System.Text;
+using static Tensorflow.Python;
 
 namespace Tensorflow.Keras.Utils
 {
     public class base_layer_utils
     {
+        /// <summary>
+        /// Adds a new variable to the layer.
+        /// </summary>
+        /// <param name="name"></param>
+        /// <param name="shape"></param>
+        /// <param name="dtype"></param>
+        /// <param name="initializer"></param>
+        /// <param name="trainable"></param>
+        /// <returns></returns>
         public static RefVariable make_variable(string name,
             int[] shape,
             TF_DataType dtype = TF_DataType.TF_FLOAT,
             IInitializer initializer = null,
-            bool trainable = false)
+            bool trainable = true,
+            bool use_resource = true)
         {
-            throw new NotImplementedException("");
+            var initializing_from_value = false;
+
+            ops.init_scope();
+
+            Func<Tensor> init_val = ()=> initializer.call(new TensorShape(shape), dtype: dtype);
+
+            var variable_dtype = dtype.as_base_dtype();
+            var v = tf.Variable(init_val);
+
+            return v;
         }
 
         /// <summary>
@@ -20,16 +41,56 @@ namespace Tensorflow.Keras.Utils
         /// </summary>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static string unique_layer_name(string name)
+        public static string unique_layer_name(string name, Dictionary<(string, string), int> name_uid_map = null,
+            string[] avoid_names = null, string @namespace = "", bool zero_based = false)
         {
-            int number = get_default_graph_uid_map();
-            return $"{name}_{number}";
+            if(name_uid_map == null)
+                name_uid_map = get_default_graph_uid_map();
+            if (avoid_names == null)
+                avoid_names = new string[0];
+
+            string proposed_name = null;
+            while(proposed_name == null || avoid_names.Contains(proposed_name))
+            {
+                var name_key = (@namespace, name);
+                if (!name_uid_map.ContainsKey(name_key))
+                    name_uid_map[name_key] = 0;
+
+                if (zero_based)
+                {
+                    int number =  name_uid_map[name_key];
+                    if (number > 0)
+                        proposed_name = $"{name}_{number}";
+                    else
+                        proposed_name = name;
+
+                    name_uid_map[name_key] += 1;
+                }
+                else
+                {
+                    name_uid_map[name_key] += 1;
+                    proposed_name = $"{name}_{name_uid_map[name_key]}";
+                }
+            }
+
+            return proposed_name;
         }
 
-        public static int get_default_graph_uid_map()
+        public static Dictionary<(string, string), int> get_default_graph_uid_map()
         {
             var graph = ops.get_default_graph();
-            return graph._next_id();
+            Dictionary<(string, string), int> name_uid_map = null;
+            if (backend.PER_GRAPH_LAYER_NAME_UIDS.ContainsKey(graph.graph_key))
+            {
+                name_uid_map = backend.PER_GRAPH_LAYER_NAME_UIDS[graph.graph_key];
+            }
+            else
+            {
+                name_uid_map = new Dictionary<(string, string), int>();
+                backend.PER_GRAPH_LAYER_NAME_UIDS[graph.graph_key] = name_uid_map;
+            }
+
+            return name_uid_map;
         }
     }
 }

src/TensorFlowNET.Core/Keras/backend.cs

@@ -6,6 +6,13 @@ namespace Tensorflow.Keras
 {
     public class backend
     {
+        /// <summary>
+        /// A global dictionary mapping graph objects to an index of counters used
+        /// for various layer names in each graph.
+        /// 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 void track_variable(RefVariable v)
         {
 

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

@@ -1,34 +1,12 @@
 using System;
 using System.Collections.Generic;
 using System.Text;
+using Tensorflow.Train;
 
 namespace Tensorflow
 {
-    public abstract class CheckpointableBase
+    public abstract class CheckpointableBase : Trackable
     {
-        /// <summary>
-        /// Restore-on-create for a variable be saved with this `Checkpointable`.
-        /// </summary>
-        /// <returns></returns>
-        protected virtual RefVariable _add_variable_with_custom_getter(string name,
-            int[] shape,
-            TF_DataType dtype = TF_DataType.TF_FLOAT,
-            IInitializer initializer = null,
-            Func<string, int[], TF_DataType, IInitializer, bool, RefVariable> getter = null,
-            bool overwrite = false,
-            bool trainable = false)
-        {
-            var new_variable = getter(name, shape, dtype, initializer, trainable);
-            if (!overwrite || new_variable is RefVariable)
-                return _track_checkpointable(new_variable, name: name,
-                                        overwrite: overwrite);
-            else
-                return new_variable;
-        }
 
-        protected RefVariable _track_checkpointable(RefVariable checkpointable, string name, bool overwrite = false)
-        {
-            return checkpointable;
-        }
     }
 }

src/TensorFlowNET.Core/Train/Trackable.cs

@@ -0,0 +1,34 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Train
+{
+    public abstract class Trackable
+    {
+        /// <summary>
+        /// Restore-on-create for a variable be saved with this `Checkpointable`.
+        /// </summary>
+        /// <returns></returns>
+        protected virtual RefVariable _add_variable_with_custom_getter(string name,
+            int[] shape,
+            TF_DataType dtype = TF_DataType.TF_FLOAT,
+            IInitializer initializer = null,
+            Func<string, int[], TF_DataType, IInitializer, bool, RefVariable> getter = null,
+            bool overwrite = false,
+            bool trainable = false)
+        {
+            var new_variable = getter(name, shape, dtype, initializer, trainable);
+            if (!overwrite || new_variable is RefVariable)
+                return _track_checkpointable(new_variable, name: name,
+                                        overwrite: overwrite);
+            else
+                return new_variable;
+        }
+
+        protected RefVariable _track_checkpointable(RefVariable checkpointable, string name, bool overwrite = false)
+        {
+            return checkpointable;
+        }
+    }
+}

src/TensorFlowNET.Core/Variables/RefVariable.cs

@@ -111,7 +111,7 @@ namespace Tensorflow
 
             // Store the graph key so optimizers know how to only retrieve variables from
             // this graph.
-            _graph_key = ops.get_default_graph()._graph_key;
+            _graph_key = ops.get_default_graph().graph_key;
 
             _trainable = trainable;
             if (trainable && !collections.Contains(ops.GraphKeys.TRAINABLE_VARIABLES))

test/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs

@@ -18,69 +18,14 @@ namespace TensorFlowNET.Examples
         public string Name => "Naive Bayes Classifier";
         public bool ImportGraph { get; set; } = false;
 
-
+        public NDArray X, y;
         public Normal dist { get; set; }
         public bool Run()
         {
-            var X = np.array(new float[][] { new float[] { 5.1f, 3.5f},new float[] { 4.9f, 3.0f },new float[] { 4.7f, 3.2f },
-                            new float[] { 4.6f, 3.1f },new float[] { 5.0f, 3.6f },new float[] { 5.4f, 3.9f },
-                            new float[] { 4.6f, 3.4f },new float[] { 5.0f, 3.4f },new float[] { 4.4f, 2.9f },
-                            new float[] { 4.9f, 3.1f },new float[] { 5.4f, 3.7f },new float[] {4.8f, 3.4f },
-                            new float[] {4.8f, 3.0f },new float[] {4.3f, 3.0f },new float[] {5.8f, 4.0f },
-                            new float[] {5.7f, 4.4f },new float[] {5.4f, 3.9f },new float[] {5.1f, 3.5f },
-                            new float[] {5.7f, 3.8f },new float[] {5.1f, 3.8f },new float[] {5.4f, 3.4f },
-                            new float[] {5.1f, 3.7f },new float[] {5.1f, 3.3f },new float[] {4.8f, 3.4f },
-                            new float[] {5.0f, 3.0f },new float[] {5.0f , 3.4f },new float[] {5.2f, 3.5f },
-                            new float[] {5.2f, 3.4f },new float[] {4.7f, 3.2f },new float[] {4.8f, 3.1f },
-                            new float[] {5.4f, 3.4f },new float[] {5.2f, 4.1f},new float[] {5.5f, 4.2f },
-                            new float[] {4.9f, 3.1f },new float[] {5.0f , 3.2f },new float[] {5.5f, 3.5f },
-                            new float[] {4.9f, 3.6f },new float[] {4.4f, 3.0f },new float[] {5.1f, 3.4f },
-                            new float[] {5.0f , 3.5f },new float[] {4.5f, 2.3f },new float[] {4.4f, 3.2f },
-                            new float[] {5.0f , 3.5f },new float[] {5.1f, 3.8f },new float[] {4.8f, 3.0f},
-                            new float[] {5.1f, 3.8f },new float[] {4.6f, 3.2f },new float[] { 5.3f, 3.7f },
-                            new float[] {5.0f , 3.3f },new float[] {7.0f , 3.2f },new float[] {6.4f, 3.2f },
-                            new float[] {6.9f, 3.1f },new float[] {5.5f, 2.3f },new float[] {6.5f, 2.8f },
-                            new float[] {5.7f, 2.8f },new float[] {6.3f, 3.3f },new float[] {4.9f, 2.4f },
-                            new float[] {6.6f, 2.9f },new float[] {5.2f, 2.7f },new float[] {5.0f , 2.0f },
-                            new float[] {5.9f, 3.0f },new float[] {6.0f , 2.2f },new float[] {6.1f, 2.9f },
-                            new float[] {5.6f, 2.9f },new float[] {6.7f, 3.1f },new float[] {5.6f, 3.0f },
-                            new float[] {5.8f, 2.7f },new float[] {6.2f, 2.2f },new float[] {5.6f, 2.5f },
-                            new float[] {5.9f, 3.0f},new float[] {6.1f, 2.8f},new float[] {6.3f, 2.5f},
-                            new float[] {6.1f, 2.8f},new float[] {6.4f, 2.9f},new float[] {6.6f, 3.0f },
-                            new float[] {6.8f, 2.8f},new float[] {6.7f, 3.0f },new float[] {6.0f , 2.9f},
-                            new float[] {5.7f, 2.6f},new float[] {5.5f, 2.4f},new float[] {5.5f, 2.4f},
-                            new float[] {5.8f, 2.7f},new float[] {6.0f , 2.7f},new float[] {5.4f, 3.0f},
-                            new float[] {6.0f , 3.4f},new float[] {6.7f, 3.1f},new float[] {6.3f, 2.3f},
-                            new float[] {5.6f, 3.0f },new float[] {5.5f, 2.5f},new float[] {5.5f, 2.6f},
-                            new float[] {6.1f, 3.0f },new float[] {5.8f, 2.6f},new float[] {5.0f, 2.3f},
-                            new float[] {5.6f, 2.7f},new float[] {5.7f, 3.0f },new float[] {5.7f, 2.9f},
-                            new float[] {6.2f, 2.9f},new float[] {5.1f, 2.5f},new float[] {5.7f, 2.8f},
-                            new float[] {6.3f, 3.3f},new float[] {5.8f, 2.7f},new float[] {7.1f, 3.0f },
-                            new float[] {6.3f, 2.9f},new float[] {6.5f, 3.0f },new float[] {7.6f, 3.0f },
-                            new float[] {4.9f, 2.5f},new float[] {7.3f, 2.9f},new float[] {6.7f, 2.5f},
-                            new float[] {7.2f, 3.6f},new float[] {6.5f, 3.2f},new float[] {6.4f, 2.7f},
-                            new float[] {6.8f, 3.00f },new float[] {5.7f, 2.5f},new float[] {5.8f, 2.8f},
-                            new float[] {6.4f, 3.2f},new float[] {6.5f, 3.0f },new float[] {7.7f, 3.8f},
-                            new float[] {7.7f, 2.6f},new float[] {6.0f , 2.2f},new float[] {6.9f, 3.2f},
-                            new float[] {5.6f, 2.8f},new float[] {7.7f, 2.8f},new float[] {6.3f, 2.7f},
-                            new float[] {6.7f, 3.3f},new float[] {7.2f, 3.2f},new float[] {6.2f, 2.8f},
-                            new float[] {6.1f, 3.0f },new float[] {6.4f, 2.8f},new float[] {7.2f, 3.0f },
-                            new float[] {7.4f, 2.8f},new float[] {7.9f, 3.8f},new float[] {6.4f, 2.8f},
-                            new float[] {6.3f, 2.8f},new float[] {6.1f, 2.6f},new float[] {7.7f, 3.0f },
-                            new float[] {6.3f, 3.4f},new float[] {6.4f, 3.1f},new float[] {6.0f, 3.0f},
-                            new float[] {6.9f, 3.1f},new float[] {6.7f, 3.1f},new float[] {6.9f, 3.1f},
-                            new float[] {5.8f, 2.7f},new float[] {6.8f, 3.2f},new float[] {6.7f, 3.3f},
-                            new float[] {6.7f, 3.0f },new float[] {6.3f, 2.5f},new float[] {6.5f, 3.0f },
-                            new float[] {6.2f, 3.4f},new float[] {5.9f, 3.0f }, new float[] {5.8f, 3.0f }});
-            
-            var y = np.array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                            0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-                            1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-                            1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-                            2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-                            2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2);
+            PrepareData();
+
             fit(X, y);
+
             // Create a regular grid and classify each point 
             float x_min = X.amin(0).Data<float>(0) - 0.5f;
             float y_min = X.amin(0).Data<float>(1) - 0.5f;
@@ -88,12 +33,11 @@ namespace TensorFlowNET.Examples
             float y_max = X.amax(0).Data<float>(1) + 0.5f;
 
             var (xx, yy) = np.meshgrid(np.linspace(x_min, x_max, 30), np.linspace(y_min, y_max, 30));
-            var s = tf.Session();
-            if (xx.dtype ==  typeof(float))
+            with(tf.Session(), sess =>
             {
-                var samples = np.hstack<float>(xx.ravel().reshape(xx.size,1), yy.ravel().reshape(yy.size,1));
-                var Z = s.run(predict(samples));
-            }
+                var samples = np.hstack<float>(xx.ravel().reshape(xx.size, 1), yy.ravel().reshape(yy.size, 1));
+                var Z = sess.run(predict(samples));
+            });
 
             return true;
         }
@@ -189,7 +133,42 @@ namespace TensorFlowNET.Examples
 
         public void PrepareData()
         {
-            
+            #region Training data
+            X = np.array(new float[,] {
+                {5.1f, 3.5f}, {4.9f, 3.0f}, {4.7f, 3.2f}, {4.6f, 3.1f}, {5.0f, 3.6f}, {5.4f, 3.9f},
+                {4.6f, 3.4f}, {5.0f, 3.4f}, {4.4f, 2.9f}, {4.9f, 3.1f}, {5.4f, 3.7f}, {4.8f, 3.4f},
+                {4.8f, 3.0f}, {4.3f, 3.0f}, {5.8f, 4.0f}, {5.7f, 4.4f}, {5.4f, 3.9f}, {5.1f, 3.5f},
+                {5.7f, 3.8f}, {5.1f, 3.8f}, {5.4f, 3.4f}, {5.1f, 3.7f}, {5.1f, 3.3f}, {4.8f, 3.4f},
+                {5.0f, 3.0f}, {5.0f, 3.4f}, {5.2f, 3.5f}, {5.2f, 3.4f}, {4.7f, 3.2f}, {4.8f, 3.1f},
+                {5.4f, 3.4f}, {5.2f, 4.1f}, {5.5f, 4.2f}, {4.9f, 3.1f}, {5.0f, 3.2f}, {5.5f, 3.5f},
+                {4.9f, 3.6f}, {4.4f, 3.0f}, {5.1f, 3.4f}, {5.0f, 3.5f}, {4.5f, 2.3f}, {4.4f, 3.2f},
+                {5.0f, 3.5f}, {5.1f, 3.8f}, {4.8f, 3.0f}, {5.1f, 3.8f}, {4.6f, 3.2f}, {5.3f, 3.7f},
+                {5.0f, 3.3f}, {7.0f, 3.2f}, {6.4f, 3.2f}, {6.9f, 3.1f}, {5.5f, 2.3f}, {6.5f, 2.8f},
+                {5.7f, 2.8f}, {6.3f, 3.3f}, {4.9f, 2.4f}, {6.6f, 2.9f}, {5.2f, 2.7f}, {5.0f, 2.0f},
+                {5.9f, 3.0f}, {6.0f, 2.2f}, {6.1f, 2.9f}, {5.6f, 2.9f}, {6.7f, 3.1f}, {5.6f, 3.0f},
+                {5.8f, 2.7f}, {6.2f, 2.2f}, {5.6f, 2.5f}, {5.9f, 3.0f}, {6.1f, 2.8f}, {6.3f, 2.5f},
+                {6.1f, 2.8f}, {6.4f, 2.9f}, {6.6f, 3.0f}, {6.8f, 2.8f}, {6.7f, 3.0f}, {6.0f, 2.9f},
+                {5.7f, 2.6f}, {5.5f, 2.4f}, {5.5f, 2.4f}, {5.8f, 2.7f}, {6.0f, 2.7f}, {5.4f, 3.0f},
+                {6.0f, 3.4f}, {6.7f, 3.1f}, {6.3f, 2.3f}, {5.6f, 3.0f}, {5.5f, 2.5f}, {5.5f, 2.6f},
+                {6.1f, 3.0f}, {5.8f, 2.6f}, {5.0f, 2.3f}, {5.6f, 2.7f}, {5.7f, 3.0f}, {5.7f, 2.9f},
+                {6.2f, 2.9f}, {5.1f, 2.5f}, {5.7f, 2.8f}, {6.3f, 3.3f}, {5.8f, 2.7f}, {7.1f, 3.0f},
+                {6.3f, 2.9f}, {6.5f, 3.0f}, {7.6f, 3.0f}, {4.9f, 2.5f}, {7.3f, 2.9f}, {6.7f, 2.5f},
+                {7.2f, 3.6f}, {6.5f, 3.2f}, {6.4f, 2.7f}, {6.8f, 3.0f}, {5.7f, 2.5f}, {5.8f, 2.8f},
+                {6.4f, 3.2f}, {6.5f, 3.0f}, {7.7f, 3.8f}, {7.7f, 2.6f}, {6.0f, 2.2f}, {6.9f, 3.2f},
+                {5.6f, 2.8f}, {7.7f, 2.8f}, {6.3f, 2.7f}, {6.7f, 3.3f}, {7.2f, 3.2f}, {6.2f, 2.8f},
+                {6.1f, 3.0f}, {6.4f, 2.8f}, {7.2f, 3.0f}, {7.4f, 2.8f}, {7.9f, 3.8f}, {6.4f, 2.8f},
+                {6.3f, 2.8f}, {6.1f, 2.6f}, {7.7f, 3.0f}, {6.3f, 3.4f}, {6.4f, 3.1f}, {6.0f, 3.0f},
+                {6.9f, 3.1f}, {6.7f, 3.1f}, {6.9f, 3.1f}, {5.8f, 2.7f}, {6.8f, 3.2f}, {6.7f, 3.3f},
+                {6.7f, 3.0f}, {6.3f, 2.5f}, {6.5f, 3.0f}, {6.2f, 3.4f}, {5.9f, 3.0f}, {5.8f, 3.0f}});
+
+            y = np.array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+                        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+                        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
+                        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
+                        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2);
+            #endregion
         }
     }
 }

test/TensorFlowNET.Examples/TextProcess/BinaryTextClassification.cs

@@ -30,12 +30,12 @@ namespace TensorFlowNET.Examples
         {
             PrepareData();
 
-            Console.WriteLine($"Training entries: {train_data.size}, labels: {train_labels.size}");
+            Console.WriteLine($"Training entries: {train_data.len}, labels: {train_labels.len}");
 
             // A dictionary mapping words to an integer index
             var word_index = GetWordIndex();
             
-            train_data = keras.preprocessing.sequence.pad_sequences(train_data,
+            /*train_data = keras.preprocessing.sequence.pad_sequences(train_data,
                 value: word_index["<PAD>"],
                 padding: "post",
                 maxlen: 256);
@@ -43,13 +43,14 @@ namespace TensorFlowNET.Examples
             test_data = keras.preprocessing.sequence.pad_sequences(test_data,
                 value: word_index["<PAD>"],
                 padding: "post",
-                maxlen: 256);
+                maxlen: 256);*/
 
             // input shape is the vocabulary count used for the movie reviews (10,000 words)
             int vocab_size = 10000;
 
             var model = keras.Sequential();
-            model.add(keras.layers.Embedding(vocab_size, 16));
+            var layer = keras.layers.Embedding(vocab_size, 16);
+            model.add(layer);
 
             return false;
         }
@@ -78,17 +79,23 @@ namespace TensorFlowNET.Examples
             labels_test = labels_test[indices_test];
 
             // not completed
-            var xs = x_train.hstack<int>(x_test);
+            var xs = x_train.hstack<string>(x_test);
             var labels = labels_train.hstack<int>(labels_test);
 
             var idx = x_train.size;
             var y_train = labels_train;
             var y_test = labels_test;
 
-            x_train = train_data;
-            train_labels = y_train;
+            // convert x_train
+            train_data = new NDArray(np.int32, (x_train.size, 256));
+            for (int i = 0; i < x_train.size; i++)
+                train_data[i] = x_train[i].Data<string>(0).Split(',').Select(x => int.Parse(x)).ToArray();
 
-            test_data = x_test;
+            test_data = new NDArray(np.int32, (x_test.size, 256));
+            for (int i = 0; i < x_test.size; i++)
+                test_data[i] = x_test[i].Data<string>(0).Split(',').Select(x => int.Parse(x)).ToArray();
+
+            train_labels = y_train;
             test_labels = y_test;
         }