linspace and meshgrid.

src/TensorFlowNET.Console/MemoryBasicTest.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine.DataAdapters;

src/TensorFlowNET.Console/MemoryFuncGraphTest.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Text;

src/TensorFlowNET.Console/MemoryKerasTest.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using static Tensorflow.Binding;
 using static Tensorflow.KerasApi;

src/TensorFlowNET.Console/MemoryMonitor.cs

@@ -2,7 +2,7 @@
 using System.Diagnostics;
 using System.Threading;
 using System.Threading.Tasks;
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using static Tensorflow.Binding;
 using static Tensorflow.KerasApi;
 

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

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System.Collections.Generic;
 using System.Diagnostics;
 using System.Linq;

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

@@ -0,0 +1,29 @@
+/*****************************************************************************
+   Copyright 2021 Haiping Chen. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+******************************************************************************/
+
+using Tensorflow.NumPy;
+
+namespace Tensorflow
+{
+    public partial class tensorflow
+    {
+        /// <summary>
+        /// NumPy API on TensorFlow
+        /// https://www.tensorflow.org/api_docs/python/tf/experimental/numpy
+        /// </summary>
+        public NumPyImpl numpy => new NumPyImpl();
+    }
+}

src/TensorFlowNET.Core/Binding.Util.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections;
 using System.Collections.Generic;
@@ -174,6 +174,9 @@ namespace Tensorflow
             throw new NotImplementedException("len() not implemented for type: " + a.GetType());
         }
 
+        public static int min(int a, int b)
+            => Math.Min(a, b);
+
         public static float min(float a, float b)
             => Math.Min(a, b);
 

src/TensorFlowNET.Core/Data/DataSetBase.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Data/DatasetManager.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System.Collections.Generic;
 using Tensorflow.Data;
 

src/TensorFlowNET.Core/Data/Datasets.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Data/IDataSet.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Data/MnistDataSet.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Diagnostics;
 

src/TensorFlowNET.Core/Data/MnistModelLoader.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.IO;
 using System.Threading.Tasks;

src/TensorFlowNET.Core/Data/TensorDataset.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System.Linq;
 using static Tensorflow.Binding;
 

src/TensorFlowNET.Core/Data/TensorSliceDataset.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System.Linq;
 using static Tensorflow.Binding;
 

src/TensorFlowNET.Core/Eager/EagerTensor.Creation.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Linq;
 using static Tensorflow.Binding;

src/TensorFlowNET.Core/Framework/graph_util_impl.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;

src/TensorFlowNET.Core/Framework/tensor_shape.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Linq;
 using System.Text;

src/TensorFlowNET.Core/Gradients/array_grad.cs

@@ -18,7 +18,7 @@ using System.Collections.Generic;
 using System.Linq;
 using Tensorflow.Eager;
 using Tensorflow.Framework;
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using static Tensorflow.Binding;
 
 namespace Tensorflow.Gradients

src/TensorFlowNET.Core/Gradients/math_grad.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Linq;
 using Tensorflow.Eager;

src/TensorFlowNET.Core/Interfaces/ITensorOrOperation.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/ZeroPadding2DArgs.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 
 namespace Tensorflow.Keras.ArgsDefinition
 {

src/TensorFlowNET.Core/Keras/ArgsDefinition/TensorFlowOpLayerArgs.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System.Collections.Generic;
 
 namespace Tensorflow.Keras.ArgsDefinition

src/TensorFlowNET.Core/NumPy/Implementation/NumPyImpl.Creation.cs

@@ -0,0 +1,71 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using static Tensorflow.Binding;
+
+namespace Tensorflow.NumPy
+{
+    public partial class NumPyImpl
+    {
+        public NDArray eye(int N, int? M = null, int k = 0, NumpyDType dtype = NumpyDType.Double)
+        {
+            if (!M.HasValue)
+                M = N;
+
+            var diag_len = min(N, M.Value);
+            if (k > 0)
+            {
+                if (N >= M)
+                    diag_len -= k;
+                else if (N + k > M)
+                    diag_len = M.Value - k;
+            }
+            else
+            {
+                if (M >= N)
+                    diag_len += k;
+                else if (M - k > N)
+                    diag_len = N + k;
+            }
+
+            var diagonal_ = array_ops.ones(new TensorShape(diag_len), dtype: dtype.as_tf_dtype());
+            var tensor = array_ops.matrix_diag(diagonal: diagonal_, num_rows: N, num_cols: M.Value, k: k);
+            return new NDArray(tensor);
+        }
+
+        public NDArray linspace<T>(T start, T stop, int num = 50, bool endpoint = true, bool retstep = false,
+            NumpyDType dtype = NumpyDType.Double, int axis = 0)
+        {
+            var start_tensor = array_ops.constant(start, dtype: dtype.as_tf_dtype());
+            var stop_tensor = array_ops.constant(stop, dtype: dtype.as_tf_dtype());
+            var num_tensor = array_ops.constant(num);
+            // var step_tensor = array_ops.constant(np.nan);
+            Tensor result = null;
+
+            if (endpoint)
+            {
+                result = math_ops.linspace(start_tensor, stop_tensor, num_tensor, axis: axis);
+            }
+            else
+            {
+                if (num > 1)
+                {
+                    var step = (stop_tensor - start_tensor) / num;
+                    var new_stop = math_ops.cast(stop_tensor, step.dtype) - step;
+                    start_tensor = math_ops.cast(start_tensor, new_stop.dtype);
+                    result = math_ops.linspace(start_tensor, new_stop, num_tensor, axis: axis);
+                }
+                else
+                    result = math_ops.linspace(start_tensor, stop_tensor, num_tensor, axis: axis);
+            }
+
+            return new NDArray(result);
+        }
+
+        public (NDArray, NDArray) meshgrid<T>(T[] array, bool copy = true, bool sparse = false)
+        {
+            var tensors = array_ops.meshgrid(array, copy: copy, sparse: sparse);
+            return (new NDArray(tensors[0]), new NDArray(tensors[1]));
+        }
+    }
+}

src/TensorFlowNET.Core/NumPy/Implementation/NumPyImpl.cs

@@ -0,0 +1,10 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.NumPy
+{
+    public partial class NumPyImpl
+    {
+    }
+}

src/TensorFlowNET.Core/Numpy/IMemoryBlock.cs

@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using System.Text;
 
-namespace Tensorflow.Numpy
+namespace Tensorflow.NumPy
 {
     public interface IMemoryBlock
     {

src/TensorFlowNET.Core/Numpy/InfoOf.cs

@@ -3,7 +3,7 @@ using System.Collections.Generic;
 using System.Runtime.InteropServices;
 using System.Text;
 
-namespace Tensorflow.Numpy
+namespace Tensorflow.NumPy
 {
     public class InfoOf<T>
     {

src/TensorFlowNET.Core/Numpy/IteratorType.cs

@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using System.Text;
 
-namespace Tensorflow.Numpy
+namespace Tensorflow.NumPy
 {
     public enum IteratorType
     {

src/TensorFlowNET.Core/Numpy/NDArray.Creation.cs

@@ -3,7 +3,7 @@ using System.Collections.Generic;
 using System.Text;
 using static Tensorflow.Binding;
 
-namespace Tensorflow.Numpy
+namespace Tensorflow.NumPy
 {
     public partial class NDArray
     {

src/TensorFlowNET.Core/Numpy/NDArray.cs

@@ -3,12 +3,11 @@ using System.Collections.Generic;
 using System.Linq;
 using System.Text;
 
-namespace Tensorflow.Numpy
+namespace Tensorflow.NumPy
 {
     public partial class NDArray
     {
         Tensor _tensor;
-
         public NumpyDType dtype => _tensor.dtype.as_numpy_typecode();
         public ulong size => _tensor.size;
         public ulong dtypesize => _tensor.itemsize;
@@ -166,6 +165,9 @@ namespace Tensorflow.Numpy
         public static implicit operator NDArray(float[] value)
             => new NDArray(value);
 
+        public static implicit operator Tensor(NDArray nd)
+            => nd._tensor;
+
         public static NDArray operator /(NDArray x, NDArray y) => throw new NotImplementedException("");
 
         public override string ToString()

src/TensorFlowNET.Core/Numpy/NDIterator.Generic.cs

@@ -3,7 +3,7 @@ using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
-namespace Tensorflow.Numpy
+namespace Tensorflow.NumPy
 {
     public partial class NDIterator<TOut> : NDIterator, IEnumerable<TOut>, IDisposable where TOut : unmanaged
     {

src/TensorFlowNET.Core/Numpy/NDIterator.cs

@@ -3,7 +3,7 @@ using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
-namespace Tensorflow.Numpy
+namespace Tensorflow.NumPy
 {
     public delegate ref T MoveNextReferencedDelegate<T>() where T : unmanaged;
 

src/TensorFlowNET.Core/Numpy/NpzDictionary.cs

@@ -6,7 +6,7 @@ using System.IO.Compression;
 using System.Linq;
 using System.Text;
 
-namespace Tensorflow.Numpy
+namespace Tensorflow.NumPy
 {
     public class NpzDictionary<T> : IDisposable, IReadOnlyDictionary<string, T>, ICollection<T>
         where T : class,

src/TensorFlowNET.Core/Numpy/Numpy.Creation.cs

@@ -5,13 +5,10 @@ using System.Numerics;
 using System.Text;
 using static Tensorflow.Binding;
 
-namespace Tensorflow.Numpy
+namespace Tensorflow.NumPy
 {
     public partial class np
     {
-        public static NDArray array(object data)
-            => throw new NotImplementedException("");
-
         public static NDArray array(Array data)
             => new NDArray(tf.constant(data));
 
@@ -19,9 +16,6 @@ namespace Tensorflow.Numpy
             where T : unmanaged
             => new NDArray(tf.constant(data));
 
-        public static NDArray array(params float[] data)
-            => throw new NotImplementedException("");
-
         public static NDArray arange<T>(T end)
             where T : unmanaged
             => new NDArray(tf.range(default(T), limit: end));
@@ -33,6 +27,19 @@ namespace Tensorflow.Numpy
         public static NDArray empty(Shape shape, NumpyDType dtype = NumpyDType.Double)
             => new NDArray(tf.zeros(shape, dtype: dtype.as_tf_dtype()));
 
+        public static NDArray eye(int N, int? M = null, int k = 0, NumpyDType dtype = NumpyDType.Double)
+            => tf.numpy.eye(N, M: M, k: k, dtype: dtype);
+
+        public static NDArray full<T>(Shape shape, T fill_value)
+            => new NDArray(tf.fill(tf.constant(shape), fill_value));
+
+        public static NDArray linspace<T>(T start, T stop, int num = 50, bool endpoint = true, bool retstep = false,
+            NumpyDType dtype = NumpyDType.Double, int axis = 0) where T : unmanaged
+            => tf.numpy.linspace(start, stop, num: num, endpoint: endpoint, retstep: retstep, dtype: dtype, axis: axis);
+
+        public static (NDArray, NDArray) meshgrid<T>(T x, T y, bool copy = true, bool sparse = false)
+            => tf.numpy.meshgrid(new[] { x, y }, copy: copy, sparse: sparse);
+
         public static NDArray ones(Shape shape, NumpyDType dtype = NumpyDType.Double)
             => new NDArray(tf.ones(shape, dtype: dtype.as_tf_dtype()));
 
@@ -41,8 +48,5 @@ namespace Tensorflow.Numpy
 
         public static NDArray zeros(Shape shape, NumpyDType dtype = NumpyDType.Double)
             => new NDArray(tf.zeros(shape, dtype: dtype.as_tf_dtype()));
-
-        public static NDArray full<T>(Shape shape, T fill_value)
-            => new NDArray(tf.fill(tf.constant(shape), fill_value));
     }
 }

src/TensorFlowNET.Core/Numpy/Numpy.cs

@@ -4,7 +4,7 @@ using System.Collections.Generic;
 using System.Numerics;
 using System.Text;
 
-namespace Tensorflow.Numpy
+namespace Tensorflow.NumPy
 {
     public partial class np
     {

src/TensorFlowNET.Core/Numpy/NumpyDType.cs

@@ -3,7 +3,7 @@ using System.Collections.Generic;
 using System.Numerics;
 using System.Text;
 
-namespace Tensorflow.Numpy
+namespace Tensorflow.NumPy
 {
     /// <summary>
     ///     Represents all available types in numpy.

src/TensorFlowNET.Core/Numpy/Shape.cs

@@ -3,7 +3,7 @@ using System.Collections.Generic;
 using System.Linq;
 using System.Text;
 
-namespace Tensorflow.Numpy
+namespace Tensorflow.NumPy
 {
     public class Shape
     {

src/TensorFlowNET.Core/Numpy/Slice.cs

@@ -4,7 +4,7 @@ using System.Linq;
 using System.Text;
 using System.Text.RegularExpressions;
 
-namespace Tensorflow.Numpy
+namespace Tensorflow.NumPy
 {
     /// <summary>                                                                                                                                         <br></br>
     /// NDArray can be indexed using slicing                                                                                                              <br></br>

src/TensorFlowNET.Core/Operations/NnOps/rnn.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;

src/TensorFlowNET.Core/Operations/Operation.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;

src/TensorFlowNET.Core/Operations/array_ops.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;
@@ -186,7 +186,7 @@ namespace Tensorflow
 
         private static Tensor _constant_if_small(int value, Tensor shape)
         {
-            return shape < 1000;
+            return shape < 1000L;
         }
 
         private static Tensor _constant_if_small<T>(T value, TensorShape shape, TF_DataType dtype, string name)
@@ -212,7 +212,7 @@ namespace Tensorflow
             return _autopacking_helper(v, dtype, name == null ? "packed" : name);
         }
 
-        private static TF_DataType _get_dtype_from_nested_lists(IEnumerable<object> list_or_tuple)
+        private static TF_DataType _get_dtype_from_nested_lists<T>(IEnumerable<T> list_or_tuple)
         {
             TF_DataType dtype = TF_DataType.DtInvalid;
 
@@ -223,6 +223,9 @@ namespace Tensorflow
                     case Tensor t:
                         dtype = t.dtype.as_base_dtype();
                         break;
+                    case NDArray t:
+                        dtype = t.dtype.as_tf_dtype();
+                        break;
                 }
 
                 if (dtype != TF_DataType.DtInvalid)
@@ -574,7 +577,7 @@ namespace Tensorflow
         public static Tensor shape_v2(Tensor input, string name = null, TF_DataType out_type = TF_DataType.TF_INT32)
             => shape_internal(input, name, optimize: true, out_type: out_type);
 
-        public static Tensor size(Tensor input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
+        public static Tensor size<T>(T input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
             => size_internal(input, name, optimize: optimize, out_type: out_type);
 
         public static Tensor shape_internal(Tensor input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
@@ -597,7 +600,7 @@ namespace Tensorflow
             });
         }
 
-        private static Tensor size_internal(Tensor input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
+        private static Tensor size_internal<T>(T input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
         {
             return tf_with(ops.name_scope(name, "Size", new { input }), scope =>
             {
@@ -613,7 +616,7 @@ namespace Tensorflow
                     }
                 }
 
-                return gen_array_ops.size(input, name: name, out_type: out_type);
+                return gen_array_ops.size(input_tensor, name: name, out_type: out_type);
             });
         }
 
@@ -777,7 +780,7 @@ namespace Tensorflow
                 int k = 0,
                 int num_rows = -1,
                 int num_cols = -1,
-                float padding_value = 0,
+                double padding_value = 0,
                 string align = "RIGHT_LEFT")
             => tf.Context.ExecuteOp("MatrixDiagV3", name, 
                 new ExecuteOpArgs(diagonal, k, num_rows, num_cols, padding_value)
@@ -791,6 +794,40 @@ namespace Tensorflow
                 => tf.Context.ExecuteOp("MatrixSetDiagV3", name, new ExecuteOpArgs(input, diagonal, k)
                     .SetAttributes(new { align }));
 
+        public static Tensor[] meshgrid<T>(T[] array, bool copy = true, bool sparse = false, string indexing = "xy")
+        {
+            return tf_with(ops.name_scope(null, "meshgrid", array), scope =>
+            {
+                var ndim = array.Length;
+                var s0 = range(ndim).Select(x => 1).ToArray();
+
+                var output = new List<Tensor>();
+                foreach (var (i, x) in enumerate(array))
+                {
+                    var shape = s0[..i].concat(new[] { -1 }).concat(s0[(i + 1)..]);
+                    output.add(reshape(stack(x), shape));
+                }
+
+                // Create parameters for broadcasting each tensor to the full size
+                var shapes = array.Select(x => size(x)).ToArray();
+                var output_dtype = _get_dtype_from_nested_lists(array).as_base_dtype();
+                if (indexing == "xy" && ndim > 1)
+                {
+                    output[0] = reshape(output[0], new[] { 1, -1 }.concat(range(ndim - 2).Select(x => 1).ToArray()));
+                    output[1] = reshape(output[1], new[] { -1, 1 }.concat(range(ndim - 2).Select(x => 1).ToArray()));
+                    (shapes[0], shapes[1]) = (shapes[1], shapes[0]);
+                }
+
+                if(sparse)
+                    return output.ToArray();
+                else
+                {
+                    var mult_fact = ones(shapes, output_dtype);
+                    return output.Select(x => x * mult_fact).ToArray();
+                }
+            });
+        }
+
         /// <summary>
         /// Computes the shape of a broadcast given symbolic shapes.
         /// When shape_x and shape_y are Tensors representing shapes(i.e.the result of

src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;
@@ -430,6 +430,42 @@ namespace Tensorflow
         public static Tensor lgamma(Tensor x, string name = null)
             => gen_math_ops.lgamma(x, name: name);
 
+        public static Tensor linspace(Tensor start, Tensor stop, Tensor num, string name = null, int axis = 0)
+        {
+            return tf_with(ops.name_scope(name, "linspace", new { start, stop }), scope =>
+            {
+                var expanded_start = array_ops.expand_dims(start, axis: axis);
+                var expanded_stop = array_ops.expand_dims(stop, axis: axis);
+                var shape = array_ops.shape(expanded_start);
+                var ndims = array_ops.shape(shape)[0];
+
+                var axis_tensor = array_ops.where_v2(constant_op.constant(axis >= 0), x: axis, y: ndims + axis);
+
+                // The purpose is to avoid having negative values when repeating.
+                var num_fill = gen_math_ops.maximum(num - 2, 0);
+                var n_steps = gen_math_ops.maximum(num - 1, 1);
+                var delta = (expanded_stop - expanded_start) / cast(n_steps, expanded_stop.dtype);
+
+                var range_end = array_ops.where_v2(num >= 0, n_steps, -1);
+                var desired_range = cast(range(1, range_end, dtype: dtypes.int64), delta.dtype);
+                var mask = gen_math_ops.equal(axis, range(ndims));
+                var desired_range_shape = array_ops.where_v2(mask, num_fill, 1);
+                desired_range = array_ops.reshape(desired_range, desired_range_shape);
+                var res = expanded_start + delta * desired_range;
+
+                // Add the start and endpoints to the result, and slice out the desired
+                // portion.
+                var all_tensors = new[] { expanded_start, res, expanded_stop };
+                var concatenated = array_ops.concat(all_tensors, axis: axis);
+                var begin = array_ops.zeros_like(shape);
+                var size = array_ops.where_v2(mask, num, shape);
+
+                return array_ops.slice(concatenated, begin, size);
+            });
+
+            throw new NotImplementedException("");
+        }
+
         /// <summary>
         /// Helper function for reduction ops.
         /// </summary>
@@ -725,15 +761,24 @@ namespace Tensorflow
                 start = 0;
             }
 
+            if (dtype == TF_DataType.DtInvalid)
+            {
+                if (limit is Tensor tensor)
+                    dtype = tensor.dtype;
+                else
+                    dtype = limit.GetType().as_tf_dtype();
+            }
+                
+
             if (delta == null)
             {
-                if (limit is int)
+                if (dtype == TF_DataType.TF_INT32)
                     delta = 1;
-                else if (limit is long)
+                else if (dtype == TF_DataType.TF_INT64)
                     delta = 1L;
-                else if (limit is float)
+                else if (dtype == TF_DataType.TF_FLOAT)
                     delta = 1.0f;
-                else if (limit is double)
+                else if (dtype == TF_DataType.TF_DOUBLE)
                     delta = 1.0d;
                 else
                     delta = 1;

src/TensorFlowNET.Core/Operations/nn_impl.py.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using Tensorflow.Operations;
 using static Tensorflow.Binding;
 

src/TensorFlowNET.Core/Operations/string_ops.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using Tensorflow.Framework;
 using static Tensorflow.Binding;
 

src/TensorFlowNET.Core/Sessions/BaseSession.cs

@@ -15,7 +15,7 @@
 ******************************************************************************/
 
 using Google.Protobuf;
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections;
 using System.Collections.Generic;

src/TensorFlowNET.Core/Sessions/_ElementFetchMapper.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 

src/TensorFlowNET.Core/Sessions/_FetchHandler.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 

src/TensorFlowNET.Core/Sessions/_FetchMapper.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System.Collections.Generic;
 
 namespace Tensorflow

src/TensorFlowNET.Core/Tensors/Ragged/RaggedTensor.cs

@@ -20,7 +20,7 @@ using System.Text;
 using System.Linq;
 using Tensorflow.Framework;
 using static Tensorflow.Binding;
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Tensors/Tensor.Assign.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Tensors/Tensor.Conversions.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Diagnostics.CodeAnalysis;
 using System.Text;

src/TensorFlowNET.Core/Tensors/Tensor.Creation.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Diagnostics.CodeAnalysis;
 using System.Linq;
@@ -70,11 +70,15 @@ namespace Tensorflow
 
         unsafe void InitTensor(Array array, Shape shape)
         {
-            var dtype = array.GetType().GetElementType().as_dtype();
+            var dtype = array.GetType().GetElementType().as_tf_dtype();
             var length = (ulong)(array.Length * dtype.get_datatype_size());
 
             switch (array)
             {
+                case bool[] val:
+                    fixed (void* addr = &val[0])
+                        _handle = TF_NewTensor(shape, dtype, addr, length);
+                    break;
                 case int[] val:
                     fixed (void* addr = &val[0])
                         _handle = TF_NewTensor(shape, dtype, addr, length);
@@ -150,7 +154,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(sbyte[] data, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(sbyte)), new long[] { data.Length }, data, sizeof(sbyte));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(sbyte)), new long[] { data.Length }, data, sizeof(sbyte));
         }
 
         /// <summary>
@@ -158,7 +162,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(sbyte[] data, long[] shape, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(sbyte)), shape, data, sizeof(sbyte));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(sbyte)), shape, data, sizeof(sbyte));
         }
 
         /// <summary>
@@ -166,7 +170,7 @@ namespace Tensorflow
         /// </summary>
         public unsafe Tensor(sbyte value, TF_DataType? dType = null)
         {
-            _handle = TF_AllocateTensor(dType ?? dtypes.as_dtype(typeof(sbyte)), dims: new long[0], num_dims: 0, len: sizeof(sbyte));
+            _handle = TF_AllocateTensor(dType ?? dtypes.as_tf_dtype(typeof(sbyte)), dims: new long[0], num_dims: 0, len: sizeof(sbyte));
             *(sbyte*)TF_TensorData(_handle) = value;
             AllocationType = AllocationType.Tensorflow;
         }
@@ -176,7 +180,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(bool[] data, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(bool)), new long[] { data.Length }, data, sizeof(bool));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(bool)), new long[] { data.Length }, data, sizeof(bool));
         }
 
         /// <summary>
@@ -184,12 +188,12 @@ namespace Tensorflow
         /// </summary>
         public Tensor(bool[] data, long[] shape, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(bool)), shape, data, sizeof(bool));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(bool)), shape, data, sizeof(bool));
         }
 
         internal Tensor(float[] data, long[] shape, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(bool)), shape, data, sizeof(bool));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(bool)), shape, data, sizeof(bool));
         }
 
         /// <summary>
@@ -197,7 +201,7 @@ namespace Tensorflow
         /// </summary>
         public unsafe Tensor(bool value, TF_DataType? dType = null)
         {
-            _handle = TF_AllocateTensor(dType ?? dtypes.as_dtype(typeof(bool)), dims: new long[0], num_dims: 0, len: sizeof(bool));
+            _handle = TF_AllocateTensor(dType ?? dtypes.as_tf_dtype(typeof(bool)), dims: new long[0], num_dims: 0, len: sizeof(bool));
             *(bool*)TF_TensorData(_handle) = value;
             AllocationType = AllocationType.Tensorflow;
         }
@@ -207,7 +211,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(byte[] data, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(byte)), new long[] { data.Length }, data, sizeof(byte));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(byte)), new long[] { data.Length }, data, sizeof(byte));
         }
 
         /// <summary>
@@ -215,7 +219,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(byte[] data, long[] shape, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(byte)), shape, data, sizeof(byte));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(byte)), shape, data, sizeof(byte));
         }
 
         /// <summary>
@@ -223,7 +227,7 @@ namespace Tensorflow
         /// </summary>
         public unsafe Tensor(byte value, TF_DataType? dType = null)
         {
-            _handle = TF_AllocateTensor(dType ?? dtypes.as_dtype(typeof(byte)), dims: new long[0], num_dims: 0, len: sizeof(byte));
+            _handle = TF_AllocateTensor(dType ?? dtypes.as_tf_dtype(typeof(byte)), dims: new long[0], num_dims: 0, len: sizeof(byte));
             *(byte*)TF_TensorData(_handle) = value;
             AllocationType = AllocationType.Tensorflow;
         }
@@ -233,7 +237,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(short[] data, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(short)), new long[] { data.Length }, data, sizeof(short));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(short)), new long[] { data.Length }, data, sizeof(short));
         }
 
         /// <summary>
@@ -241,7 +245,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(short[] data, long[] shape, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(short)), shape, data, sizeof(short));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(short)), shape, data, sizeof(short));
         }
 
         /// <summary>
@@ -249,7 +253,7 @@ namespace Tensorflow
         /// </summary>
         public unsafe Tensor(short value, TF_DataType? dType = null)
         {
-            _handle = TF_AllocateTensor(dType ?? dtypes.as_dtype(typeof(short)), dims: new long[0], num_dims: 0, len: sizeof(short));
+            _handle = TF_AllocateTensor(dType ?? dtypes.as_tf_dtype(typeof(short)), dims: new long[0], num_dims: 0, len: sizeof(short));
             *(short*)TF_TensorData(_handle) = value;
             AllocationType = AllocationType.Tensorflow;
         }
@@ -259,7 +263,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(ushort[] data, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(ushort)), new long[] { data.Length }, data, sizeof(ushort));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(ushort)), new long[] { data.Length }, data, sizeof(ushort));
         }
 
         /// <summary>
@@ -267,7 +271,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(ushort[] data, long[] shape, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(ushort)), shape, data, sizeof(ushort));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(ushort)), shape, data, sizeof(ushort));
         }
 
         /// <summary>
@@ -275,7 +279,7 @@ namespace Tensorflow
         /// </summary>
         public unsafe Tensor(ushort value, TF_DataType? dType = null)
         {
-            _handle = TF_AllocateTensor(dType ?? dtypes.as_dtype(typeof(ushort)), dims: new long[0], num_dims: 0, len: sizeof(ushort));
+            _handle = TF_AllocateTensor(dType ?? dtypes.as_tf_dtype(typeof(ushort)), dims: new long[0], num_dims: 0, len: sizeof(ushort));
             *(ushort*)TF_TensorData(_handle) = value;
             AllocationType = AllocationType.Tensorflow;
         }
@@ -285,7 +289,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(int[] data, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(int)), new long[] { data.Length }, data, sizeof(int));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(int)), new long[] { data.Length }, data, sizeof(int));
         }
 
         /// <summary>
@@ -293,7 +297,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(int[] data, long[] shape, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(int)), shape, data, sizeof(int));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(int)), shape, data, sizeof(int));
         }
 
         /// <summary>
@@ -301,7 +305,7 @@ namespace Tensorflow
         /// </summary>
         public unsafe Tensor(int value, TF_DataType? dType = null)
         {
-            _handle = TF_AllocateTensor(dType ?? dtypes.as_dtype(typeof(int)), dims: new long[0], num_dims: 0, len: sizeof(int));
+            _handle = TF_AllocateTensor(dType ?? dtypes.as_tf_dtype(typeof(int)), dims: new long[0], num_dims: 0, len: sizeof(int));
             *(int*)TF_TensorData(_handle) = value;
             AllocationType = AllocationType.Tensorflow;
         }
@@ -311,7 +315,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(uint[] data, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(uint)), new long[] { data.Length }, data, sizeof(uint));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(uint)), new long[] { data.Length }, data, sizeof(uint));
         }
 
         /// <summary>
@@ -319,7 +323,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(uint[] data, long[] shape, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(uint)), shape, data, sizeof(uint));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(uint)), shape, data, sizeof(uint));
         }
 
         /// <summary>
@@ -327,7 +331,7 @@ namespace Tensorflow
         /// </summary>
         public unsafe Tensor(uint value, TF_DataType? dType = null)
         {
-            _handle = TF_AllocateTensor(dType ?? dtypes.as_dtype(typeof(uint)), dims: new long[0], num_dims: 0, len: sizeof(uint));
+            _handle = TF_AllocateTensor(dType ?? dtypes.as_tf_dtype(typeof(uint)), dims: new long[0], num_dims: 0, len: sizeof(uint));
             *(uint*)TF_TensorData(_handle) = value;
             AllocationType = AllocationType.Tensorflow;
         }
@@ -337,7 +341,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(long[] data, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(long)), new long[] { data.Length }, data, sizeof(long));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(long)), new long[] { data.Length }, data, sizeof(long));
         }
 
         /// <summary>
@@ -345,7 +349,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(long[] data, long[] shape, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(long)), shape, data, sizeof(long));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(long)), shape, data, sizeof(long));
         }
 
         /// <summary>
@@ -353,7 +357,7 @@ namespace Tensorflow
         /// </summary>
         public unsafe Tensor(long value, TF_DataType? dType = null)
         {
-            _handle = TF_AllocateTensor(dType ?? dtypes.as_dtype(typeof(long)), dims: new long[0], num_dims: 0, len: sizeof(long));
+            _handle = TF_AllocateTensor(dType ?? dtypes.as_tf_dtype(typeof(long)), dims: new long[0], num_dims: 0, len: sizeof(long));
             *(long*)TF_TensorData(_handle) = value;
             AllocationType = AllocationType.Tensorflow;
         }
@@ -363,7 +367,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(ulong[] data, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(ulong)), new long[] { data.Length }, data, sizeof(ulong));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(ulong)), new long[] { data.Length }, data, sizeof(ulong));
         }
 
         /// <summary>
@@ -371,7 +375,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(ulong[] data, long[] shape, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(ulong)), shape, data, sizeof(ulong));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(ulong)), shape, data, sizeof(ulong));
         }
 
         /// <summary>
@@ -379,7 +383,7 @@ namespace Tensorflow
         /// </summary>
         public unsafe Tensor(ulong value, TF_DataType? dType = null)
         {
-            _handle = TF_AllocateTensor(dType ?? dtypes.as_dtype(typeof(ulong)), dims: new long[0], num_dims: 0, len: sizeof(ulong));
+            _handle = TF_AllocateTensor(dType ?? dtypes.as_tf_dtype(typeof(ulong)), dims: new long[0], num_dims: 0, len: sizeof(ulong));
             *(ulong*)TF_TensorData(_handle) = value;
             AllocationType = AllocationType.Tensorflow;
         }
@@ -397,7 +401,7 @@ namespace Tensorflow
         /// </summary>
         public unsafe Tensor(float value, TF_DataType? dType = null)
         {
-            _handle = TF_AllocateTensor(dType ?? dtypes.as_dtype(typeof(float)), dims: new long[0], num_dims: 0, len: sizeof(float));
+            _handle = TF_AllocateTensor(dType ?? dtypes.as_tf_dtype(typeof(float)), dims: new long[0], num_dims: 0, len: sizeof(float));
             *(float*)TF_TensorData(_handle) = value;
             AllocationType = AllocationType.Tensorflow;
         }
@@ -407,7 +411,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(double[] data, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(double)), new long[] { data.Length }, data, sizeof(double));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(double)), new long[] { data.Length }, data, sizeof(double));
         }
 
         /// <summary>
@@ -415,7 +419,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(double[] data, long[] shape, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(double)), shape, data, sizeof(double));
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(double)), shape, data, sizeof(double));
         }
 
         /// <summary>
@@ -423,7 +427,7 @@ namespace Tensorflow
         /// </summary>
         public unsafe Tensor(double value, TF_DataType? dType = null)
         {
-            _handle = TF_AllocateTensor(dType ?? dtypes.as_dtype(typeof(double)), dims: new long[0], num_dims: 0, len: sizeof(double));
+            _handle = TF_AllocateTensor(dType ?? dtypes.as_tf_dtype(typeof(double)), dims: new long[0], num_dims: 0, len: sizeof(double));
             *(double*)TF_TensorData(_handle) = value;
             AllocationType = AllocationType.Tensorflow;
         }
@@ -433,7 +437,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(Complex[] data, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(Complex)), new long[] { data.Length }, data, Marshal.SizeOf<Complex>());
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(Complex)), new long[] { data.Length }, data, Marshal.SizeOf<Complex>());
         }
 
         /// <summary>
@@ -441,7 +445,7 @@ namespace Tensorflow
         /// </summary>
         public Tensor(Complex[] data, long[] shape, TF_DataType? dType = null)
         {
-            _handle = CreateTensorFromArray(dType ?? dtypes.as_dtype(typeof(Complex)), shape, data, Marshal.SizeOf<Complex>());
+            _handle = CreateTensorFromArray(dType ?? dtypes.as_tf_dtype(typeof(Complex)), shape, data, Marshal.SizeOf<Complex>());
         }
 
         /// <summary>
@@ -449,7 +453,7 @@ namespace Tensorflow
         /// </summary>
         public unsafe Tensor(Complex value, TF_DataType? dType = null)
         {
-            _handle = TF_AllocateTensor(dType ?? dtypes.as_dtype(typeof(Complex)), dims: new long[0], num_dims: 0, len: (ulong)sizeof(Complex));
+            _handle = TF_AllocateTensor(dType ?? dtypes.as_tf_dtype(typeof(Complex)), dims: new long[0], num_dims: 0, len: (ulong)sizeof(Complex));
             *(Complex*)TF_TensorData(_handle) = value;
             AllocationType = AllocationType.Tensorflow;
         }

src/TensorFlowNET.Core/Tensors/Tensor.Implicit.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using static Tensorflow.Binding;
 
@@ -19,8 +19,5 @@ namespace Tensorflow
 
         public static implicit operator Tensor(IntPtr handle)
             => new Tensor(handle);
-
-        public static implicit operator Tensor(NDArray nd)
-            => tf.convert_to_tensor(nd);
     }
 }

src/TensorFlowNET.Core/Tensors/Tensor.Index.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;

src/TensorFlowNET.Core/Tensors/Tensor.Operators.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Numerics;

src/TensorFlowNET.Core/Tensors/Tensor.Value.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Text;
 using static Tensorflow.Binding;
@@ -21,7 +21,7 @@ namespace Tensorflow
         public T[] ToArray<T>() where T : unmanaged
         {
             //Are the types matching?
-            if (typeof(T).as_dtype() == dtype)
+            if (typeof(T).as_tf_dtype() == dtype)
             {
                 if (NDims == 0 && size == 1)  //is it a scalar?
                 {

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Diagnostics.CodeAnalysis;
 using System.Globalization;

src/TensorFlowNET.Core/Tensors/TensorConverter.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Threading.Tasks;
 using Tensorflow.Util;
@@ -122,7 +122,7 @@ namespace Tensorflow
             if (astype == null)
             {
                 //No conversion required
-                var constantType = typeof(T).as_dtype();
+                var constantType = typeof(T).as_tf_dtype();
                 if (constantType == TF_DataType.TF_INT8)
                     return new Tensor((sbyte)(object)constant);
 

src/TensorFlowNET.Core/Tensors/TensorShape.Convert.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Tensors/TensorShape.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Diagnostics.CodeAnalysis;

src/TensorFlowNET.Core/Tensors/Tensors.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections;
 using System.Collections.Generic;

src/TensorFlowNET.Core/Tensors/c_api.tensor.cs

@@ -17,7 +17,7 @@
 using System;
 using System.Runtime.CompilerServices;
 using System.Runtime.InteropServices;
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Tensors/constant_op.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;
@@ -127,7 +127,7 @@ namespace Tensorflow
             if (dtype != TF_DataType.DtInvalid &&
                 value.GetType().Name != "NDArray" &&
                 value.GetType().BaseType.Name != "Array" &&
-                dtypes.as_base_dtype(dtype) != dtypes.as_dtype(value.GetType()))
+                dtypes.as_base_dtype(dtype) != dtypes.as_tf_dtype(value.GetType()))
             {
                 switch (dtype)
                 {
@@ -169,9 +169,9 @@ namespace Tensorflow
                 case string[] val:
                     return new EagerTensor(val, ctx.DeviceName);
                 case bool val:
-                    return new EagerTensor(val, ctx.DeviceName);
+                    return new EagerTensor(new[] { val }, Shape.Scalar);
                 case byte val:
-                    return new EagerTensor(val, ctx.DeviceName);
+                    return new EagerTensor(new[] { val }, Shape.Scalar);
                 case byte[] val:
                     return new EagerTensor(val, ctx.DeviceName);
                 case byte[,] val:
@@ -187,7 +187,7 @@ namespace Tensorflow
                 case int[,,] val:
                     return new EagerTensor(val, ctx.DeviceName);
                 case long val:
-                    return new EagerTensor(val, ctx.DeviceName);
+                    return new EagerTensor(new[] { val }, Shape.Scalar);
                 case long[] val:
                     return new EagerTensor(val, new Shape(val.Length));
                 case long[,] val:

src/TensorFlowNET.Core/Tensors/dtypes.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Numerics;
 
@@ -126,7 +126,7 @@ namespace Tensorflow
         /// <param name="dtype"></param>
         /// <returns></returns>
         /// <exception cref="ArgumentException">When <paramref name="type"/> has no equivalent <see cref="TF_DataType"/></exception>
-        public static TF_DataType as_dtype(this Type type, TF_DataType? dtype = null)
+        public static TF_DataType as_tf_dtype(this Type type, TF_DataType? dtype = null)
         {
             switch (type.Name)
             {

src/TensorFlowNET.Core/Tensors/tensor_util.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;
@@ -75,19 +75,19 @@ namespace Tensorflow
             }
             else if (tensor.Dtype == DataType.DtHalf || tensor.Dtype == DataType.DtBfloat16)
             {
-                return np.array(tensor.HalfVal).reshape(shape);
+                return np.array(tensor.HalfVal.ToArray()).reshape(shape);
             }
             else if (tensor.Dtype == DataType.DtFloat)
             {
-                return np.array(tensor.FloatVal).reshape(shape);
+                return np.array(tensor.FloatVal.ToArray()).reshape(shape);
             }
             else if (new DataType[] { DataType.DtInt32, DataType.DtUint8 }.Contains(tensor.Dtype))
             {
-                return np.array(tensor.IntVal).reshape(shape);
+                return np.array(tensor.IntVal.ToArray()).reshape(shape);
             }
             else if (tensor.Dtype == DataType.DtBool)
             {
-                return np.array(tensor.BoolVal).reshape(shape);
+                return np.array(tensor.BoolVal.ToArray()).reshape(shape);
             }
 
             throw new NotImplementedException("MakeNdarray");
@@ -284,7 +284,7 @@ scalar with value '-1' to describe an unknown shape.", value_));
                 var pre_cast = constant_value_as_shape(tensor.op.inputs[0]);
                 if (pre_cast.dims == null)
                     return pre_cast;
-                var cast_dtype = dtypes.as_dtype((Type)tensor.op.get_attr("DstT"));
+                var cast_dtype = dtypes.as_tf_dtype((Type)tensor.op.get_attr("DstT"));
                 if (!Array.Exists(new[] { dtypes.int32, dtypes.int64 }, cast_dtype_ => cast_dtype_ == cast_dtype))
                     return tensor.TensorShape.unknown_shape((int)shape.dims[0]);
 
@@ -594,35 +594,43 @@ would not be rank 1.", tensor.op.get_attr("axis")));
             {
                 return "<unprintable>";
             }
+            else if (dtype == TF_DataType.TF_BOOL)
+            {
+                var array = tensor.ToArray<bool>();
+                return DisplayArrayAsString(array, tensor.shape);
+            }
             else if (dtype == TF_DataType.TF_INT32)
             {
                 var array = tensor.ToArray<int>();
-                return DisplayArrayAsString(array);
+                return DisplayArrayAsString(array, tensor.shape);
             }
             else if (dtype == TF_DataType.TF_INT64)
             {
                 var array = tensor.ToArray<long>();
-                return DisplayArrayAsString(array);
+                return DisplayArrayAsString(array, tensor.shape);
             }
             else if (dtype == TF_DataType.TF_FLOAT)
             {
                 var array = tensor.ToArray<float>();
-                return DisplayArrayAsString(array);
+                return DisplayArrayAsString(array, tensor.shape);
             }
             else if(dtype == TF_DataType.TF_DOUBLE)
             {
                 var array = tensor.ToArray<double>();
-                return DisplayArrayAsString(array);
+                return DisplayArrayAsString(array, tensor.shape);
             }
             else
             {
                 var array = tensor.ToArray<byte>();
-                return DisplayArrayAsString(array);
+                return DisplayArrayAsString(array, tensor.shape);
             }
         }
 
-        static string DisplayArrayAsString<T>(T[] array)
+        static string DisplayArrayAsString<T>(T[] array, Shape shape)
         {
+            if (shape.ndim == 0)
+                return array[0].ToString();
+
             var display = "[";
             if (array.Length < 10)
                 display += string.Join(", ", array);

src/TensorFlowNET.Core/Training/Saving/Saver.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.IO;

src/TensorFlowNET.Core/Util/nest.py.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections;
 using System.Collections.Generic;

src/TensorFlowNET.Core/Variables/BaseResourceVariable.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using Tensorflow.Eager;
 using Tensorflow.Variables;

src/TensorFlowNET.Core/Variables/IVariableV1.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Variables/RefVariable.cs

@@ -15,7 +15,7 @@
 ******************************************************************************/
 
 using Google.Protobuf;
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;

src/TensorFlowNET.Core/Variables/ResourceVariable.Index.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Text;

src/TensorFlowNET.Core/Variables/ResourceVariable.Operators.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/ops.cs

@@ -16,7 +16,7 @@
 
 using Google.Protobuf;
 using Google.Protobuf.Collections;
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;
@@ -126,8 +126,15 @@ namespace Tensorflow
 
             if (value is EagerTensor eager_tensor)
             {
+                if (dtype == TF_DataType.DtInvalid)
+                    dtype = eager_tensor.dtype;
+
                 if (tf.executing_eagerly())
+                {
+                    if (dtype != eager_tensor.dtype)
+                        return gen_math_ops.cast(eager_tensor, dtype.as_base_dtype(), name: name);
                     return eager_tensor;
+                }
                 else
                 {
                     var graph = get_default_graph();
@@ -136,6 +143,8 @@ namespace Tensorflow
                     return (graph as FuncGraph).capture(eager_tensor, name: name);
                 }
             }
+            else if (value is NDArray nd)
+                return nd;
 
             Tensor ret = value switch
             {

src/TensorFlowNET.Keras/BackendImpl.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Linq;
 using System.Collections.Generic;

src/TensorFlowNET.Keras/Datasets/Cifar10.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.IO;

src/TensorFlowNET.Keras/Datasets/DatasetPass.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 
 namespace Tensorflow.Keras.Datasets
 {

src/TensorFlowNET.Keras/Datasets/Imdb.cs

@@ -3,7 +3,7 @@ using System.Collections.Generic;
 using System.IO;
 using System.Text;
 using Tensorflow.Keras.Utils;
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System.Linq;
 
 namespace Tensorflow.Keras.Datasets

src/TensorFlowNET.Keras/Datasets/MNIST.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.IO;
 using Tensorflow.Keras.Utils;

src/TensorFlowNET.Keras/Engine/Model.Evaluate.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;

src/TensorFlowNET.Keras/Engine/Model.Fit.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;

src/TensorFlowNET.Keras/Engine/Model.Predict.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;

src/TensorFlowNET.Keras/Engine/Model.Training.cs

@@ -3,7 +3,7 @@ using System.Collections.Generic;
 using System.Text;
 using HDF.PInvoke;
 using HDF5CSharp;
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Engine

src/TensorFlowNET.Keras/Layers/LayersApi.Merging.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Text;

src/TensorFlowNET.Keras/Layers/LayersApi.Reshaping.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Text;

src/TensorFlowNET.Keras/Layers/LayersApi.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System.Collections.Generic;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;

src/TensorFlowNET.Keras/Layers/Reshaping/ZeroPadding2D.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Utils;

src/TensorFlowNET.Keras/Layers/TensorFlowOpLayer.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;

src/TensorFlowNET.Keras/Preprocessings/DatasetUtils.index_directory.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.IO;

src/TensorFlowNET.Keras/Preprocessings/Preprocessing.image_dataset_from_directory.cs

@@ -1,6 +1,6 @@
 using static Tensorflow.KerasApi;
 using static Tensorflow.Binding;
-using Tensorflow.Numpy; 
+using Tensorflow.NumPy; 
 
 namespace Tensorflow.Keras
 {

src/TensorFlowNET.Keras/Preprocessings/Tokenizer.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using Serilog.Debugging;
 using System;
 using System.Collections.Generic;

src/TensorFlowNET.Keras/Saving/hdf5_format.cs

@@ -2,7 +2,7 @@ using System;
 using System.Collections.Generic;
 using System.Text;
 using HDF.PInvoke;
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using Tensorflow.Keras.Engine;
 using HDF5CSharp;
 using static Tensorflow.Binding;

src/TensorFlowNET.Keras/Sequence.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;

src/TensorFlowNET.Keras/Utils/base_layer_utils.cs

@@ -14,7 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;

src/TensorFlowNET.Keras/Utils/layer_utils.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;

src/TensorFlowNET.Keras/Utils/np_utils.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Text;

src/TensorFlowNET.Text/Tokenizers/WhitespaceTokenizer.cs

@@ -1,4 +1,4 @@
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Text;

src/TensorFlowNet.Benchmarks/Crash/RepeatDataSetCrash.cs

@@ -1,7 +1,7 @@
 using BenchmarkDotNet.Attributes;
 using System;
 using System.Collections.Generic;
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using static Tensorflow.Binding;
 using static Tensorflow.KerasApi;
 

src/TensorFlowNet.Benchmarks/Leak/GpuLeakByCNN.cs

@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using System.Text;
 using Tensorflow.Keras.Layers;
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using Tensorflow.Keras;
 using static Tensorflow.Binding;
 using static Tensorflow.KerasApi;

test/TensorFlowNET.Keras.UnitTest/Layers/ActivationTest.cs

@@ -2,7 +2,7 @@
 using System;
 using System.Collections.Generic;
 using System.Text;
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using static Tensorflow.KerasApi;
 using Tensorflow;
 

test/TensorFlowNET.Keras.UnitTest/Layers/CosineSimilarity.Test.cs

@@ -1,5 +1,5 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using Tensorflow.Numpy;
+using Tensorflow.NumPy;
 using Tensorflow;
 using Tensorflow.Keras.Losses;
 using static Tensorflow.Binding;