change shape to int[], comply with NumSharp v0.9

src/TensorFlowNET.Core/Keras/Sequence.cs

@@ -62,10 +62,6 @@ namespace Tensorflow.Keras
             {
                 switch(sequences[i])
                 {
-                    case int[] data:
-                        for (int j = 0; j < nd.shape[1]; j++)
-                            nd[i, j] = j < data.Length ? data[j] : value;
-                        break;
                     default:
                         throw new NotImplementedException("pad_sequences");
                 }

src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -291,7 +291,7 @@ namespace Tensorflow
                 !keepdims &&
                 axis == null)
                 // We want set_shape to be reflected in the C API graph for when we run it.
-                output.shape = new long[0];
+                output.shape = new int[0];
             return output;
         }
 
@@ -300,7 +300,7 @@ namespace Tensorflow
             if (!common_shapes.has_fully_defined_shape(output) &&
                 !keepdims &&
                 axis == null)
-                output.shape = new long[0];
+                output.shape = new int[0];
             return output;
         }
 

src/TensorFlowNET.Core/Sessions/_FetchHandler.cs

@@ -52,7 +52,24 @@ namespace Tensorflow
             {
                 if (is_op)
                 {
-                    full_values.Add(null);
+                    if(tensor_values.Length > 0)
+                    {
+                        switch (tensor_values[0].dtype.Name)
+                        {
+                            case "Int32":
+                                full_values.Add(float.NaN);
+                                break;
+                            case "Single":
+                                full_values.Add(float.NaN);
+                                break;
+                            default:
+                                throw new NotImplementedException($"build_results tensor_values[0] {tensor_values[0].dtype.Name}");
+                        }
+                    }
+                    else
+                    {
+                        full_values.Add(null);
+                    }
                 }
                 else
                 {

src/TensorFlowNET.Core/Sessions/_FetchMapper.cs

@@ -1,6 +1,7 @@
 using NumSharp;
 using System;
 using System.Collections.Generic;
+using System.Linq;
 using System.Text;
 
 namespace Tensorflow
@@ -21,7 +22,17 @@ namespace Tensorflow
 
         public virtual NDArray build_results(List<object> values)
         {
-            return values.ToArray();
+            var type = values[0].GetType();
+            var nd = new NDArray(type, values.Count);
+
+            switch (type.Name)
+            {
+                case "Single":
+                    nd.SetData(values.Select(x => (float)x).ToArray());
+                    break;
+            }
+
+            return nd;
         }
 
         public virtual List<object> unique_fetches()

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -46,7 +46,7 @@ namespace Tensorflow
 
         private TF_Output? _tf_output;
 
-        public long[] shape
+        public int[] shape
         {
             get
             {
@@ -63,7 +63,7 @@ namespace Tensorflow
                         dims[i] = c_api.TF_Dim(_handle, i);
                 }
 
-                return dims;
+                return dims.Select(x => Convert.ToInt32(x)).ToArray();
             }
 
             set
@@ -71,7 +71,7 @@ namespace Tensorflow
                 if (value == null)
                     c_api.TF_GraphSetTensorShape(this.graph, this._as_tf_output(), null, -1, status);
                 else
-                    c_api.TF_GraphSetTensorShape(this.graph, this._as_tf_output(), value, value.Length, status);
+                    c_api.TF_GraphSetTensorShape(this.graph, this._as_tf_output(), value.Select(x => Convert.ToInt64(x)).ToArray(), value.Length, status);
             }
         }
 

src/TensorFlowNET.Core/Tensors/tensor_util.cs

@@ -321,6 +321,11 @@ namespace Tensorflow
             return new TensorShape(dims.Select(x => (int)x).ToArray());
         }
 
+        public static TensorShape to_shape(int[] dims)
+        {
+            return new TensorShape(dims);
+        }
+
         public static TensorShape as_shape(this Shape shape)
         {
             return new TensorShape(shape.Dimensions);

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

@@ -21,7 +21,7 @@ namespace Tensorflow
         /// <param name="container"></param>
         /// <param name="shared_name"></param>
         /// <returns></returns>
-        public static Tensor variable_v2(long[] shape, TF_DataType dtype, string name = null, string container = "", string shared_name = "")
+        public static Tensor variable_v2(int[] shape, TF_DataType dtype, string name = null, string container = "", string shared_name = "")
         {
             var _op = _op_def_lib._apply_op_helper("VariableV2", name: name, args: new { dtype, shape, container, shared_name });
 

src/TensorFlowNET.Core/Variables/state_ops.cs

@@ -15,7 +15,7 @@ namespace Tensorflow
         /// <param name="container"></param>
         /// <param name="shared_name"></param>
         /// <returns></returns>
-        public static Tensor variable_op_v2(long[] shape,
+        public static Tensor variable_op_v2(int[] shape,
             TF_DataType dtype,
             string name = "Variable",
             string container = "",

test/TensorFlowNET.Examples/NaiveBayesClassifier.cs

@@ -19,64 +19,64 @@ namespace TensorFlowNET.Examples
         public Normal dist { get; set; }
         public bool Run()
         {
-            var X = np.array<float>(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 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<int>(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);
+            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);
             fit(X, y);
             // Create a regular grid and classify each point 
             float x_min = X.amin(0).Data<float>(0) - 0.5f;
@@ -88,19 +88,18 @@ namespace TensorFlowNET.Examples
             var s = tf.Session();
             if (xx.dtype ==  typeof(float))
             {
-                var samples = np.hstack<float>(xx.ravel().reshape(-1,1), yy.ravel().reshape(-1,1));
+                var samples = np.hstack<float>(xx.ravel().reshape(xx.size,1), yy.ravel().reshape(yy.size,1));
                 var Z = s.run(predict(samples));
             }
 
-
             return true;
         }
 
         public void fit(NDArray X, NDArray y)
         {
-            NDArray unique_y = y.unique<long>();
+            var unique_y = y.unique<int>();
 
-            Dictionary<long, List<List<float>>> dic = new Dictionary<long, List<List<float>>>();
+            var dic = new Dictionary<int, List<List<float>>>();
             // Init uy in dic
             foreach (int uy in unique_y.Data<int>())
             {
@@ -111,11 +110,11 @@ namespace TensorFlowNET.Examples
             int maxCount = 0;
             for (int i = 0; i < y.size; i++)
             {
-                long curClass = (long)y[i];
-                List<List<float>> l = dic[curClass];
-                List<float> pair = new List<float>();
-                pair.Add((float)X[i,0]);
-                pair.Add((float)X[i, 1]);
+                var curClass = y[i];
+                var l = dic[curClass];
+                var pair = new List<float>();
+                pair.Add(X[i,0]);
+                pair.Add(X[i, 1]);
                 l.Add(pair);
                 if (l.Count > maxCount)
                 {
@@ -124,7 +123,7 @@ namespace TensorFlowNET.Examples
                 dic[curClass] = l;
             }
             float[,,] points = new float[dic.Count, maxCount, X.shape[1]];
-            foreach (KeyValuePair<long, List<List<float>>> kv in dic)
+            foreach (KeyValuePair<int, List<List<float>>> kv in dic)
             {
                 int j = (int) kv.Key;
                 for (int i = 0; i < maxCount; i++)
@@ -136,7 +135,7 @@ namespace TensorFlowNET.Examples
                 }
 
             }
-            NDArray points_by_class = np.array<float>(points);
+            var points_by_class = np.array(points);
             // estimate mean and variance for each class / feature
             // shape : nb_classes * nb_features
             var cons = tf.constant(points_by_class);