Remove Range due to confilict.

Author: Oceania2018

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

@@ -60,13 +60,9 @@ public Tensor this[params Slice[] slices]
             }
         }
 
-        public Tensor this[Range slices]
-            => throw new NotImplementedException("");
-
         public Tensor this[params string[] slices]
             => this[slices.Select(x => new Slice(x)).ToArray()];
 
-
         public Tensor slice(Slice slice)
         {
             var slice_spec = new int[] { slice.Start.Value };

src/TensorFlowNET.Keras/Utils/KerasUtils.cs

@@ -0,0 +1,42 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Utils
+{
+    public class KerasUtils
+    {
+        /// <summary>
+        /// Downloads a file from a URL if it not already in the cache.
+        /// </summary>
+        /// <param name="fname">Name of the file</param>
+        /// <param name="origin">Original URL of the file</param>
+        /// <param name="untar"></param>
+        /// <param name="md5_hash"></param>
+        /// <param name="file_hash"></param>
+        /// <param name="cache_subdir"></param>
+        /// <param name="hash_algorithm"></param>
+        /// <param name="extract"></param>
+        /// <param name="archive_format"></param>
+        /// <param name="cache_dir"></param>
+        /// <returns></returns>
+        public string get_file(string fname, string origin,
+            bool untar = false,
+            string md5_hash = null,
+            string file_hash = null,
+            string cache_subdir = "datasets",
+            string hash_algorithm = "auto",
+            bool extract = false,
+            string archive_format = "auto",
+            string cache_dir = null)
+            => data_utils.get_file(fname, origin, 
+                untar: untar,
+                md5_hash: md5_hash,
+                file_hash: file_hash,
+                cache_subdir: cache_subdir,
+                hash_algorithm: hash_algorithm,
+                extract: extract,
+                archive_format: archive_format,
+                cache_dir: cache_dir);
+    }
+}

src/TensorFlowNET.Keras/Utils/RuntimeHelpers.cs

@@ -0,0 +1,37 @@
+using System;
+using System.Linq;
+using System.Collections.Generic;
+using System.IO;
+using System.Text;
+
+namespace Tensorflow.Keras.Utils
+{
+    public class data_utils
+    {
+        public static string get_file(string fname, string origin,
+            bool untar = false,
+            string md5_hash = null,
+            string file_hash = null,
+            string cache_subdir = "datasets",
+            string hash_algorithm = "auto",
+            bool extract = false,
+            string archive_format = "auto",
+            string cache_dir = null)
+        {
+            var datadir_base = cache_dir;
+            Directory.CreateDirectory(datadir_base);
+
+            var datadir = Path.Combine(datadir_base, cache_subdir);
+            Directory.CreateDirectory(datadir);
+
+            Web.Download(origin, datadir, fname);
+
+            if (untar)
+                Compress.ExtractTGZ(Path.Combine(datadir_base, fname), datadir_base);
+            else if (extract)
+                Compress.ExtractGZip(Path.Combine(datadir_base, fname), datadir_base);
+
+            return datadir;
+        }
+    }
+}

src/TensorFlowNET.Keras/Utils/data_utils.cs

@@ -67,7 +67,7 @@ public static void print_summary(Model model, int line_length = -1, float[] posi
                     line_length = 65;
                 if (positions == null)
                     positions = new[] { 0.45f, 0.85f, 1.0f };
-                if (positions[^1] <= 1)
+                if (positions.Last() <= 1)
                     positions = positions.Select(p => line_length * p).ToArray();
                 to_display = new[] { "Layer (type)", "Output Shape", "Param #" };
             }
@@ -77,7 +77,7 @@ public static void print_summary(Model model, int line_length = -1, float[] posi
                     line_length = 98;
                 if (positions == null)
                     positions = new[] { 0.33f, 0.55f, 0.67f, 1.0f };
-                if (positions[^1] <= 1)
+                if (positions.Last() <= 1)
                     positions = positions.Select(p => line_length * p).ToArray();
                 to_display = new[] { "Layer (type)", "Output Shape", "Param #", "Connected to" };
 
@@ -118,7 +118,7 @@ static void print_row(string[] fields, int[] positions)
             foreach (var i in range(fields.Length))
             {
                 if (i > 0)
-                    line = line[0..^1] + " ";
+                    line = line + " ";
                 line += fields[i];
                 line = string.Join("", line.Take(positions[i]));
                 line += string.Join("", range(positions[i] - len(line)).Select(x => " "));

src/TensorFlowNET.Keras/Utils/layer_utils.cs

@@ -1,7 +1,7 @@
 <Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
-    <TargetFramework>netcoreapp3.1</TargetFramework>
+    <TargetFramework>net5.0</TargetFramework>
 
     <IsPackable>false</IsPackable>
 

src/TensorFlowNET.Core/Util/Range.cs renamed to test/TensorFlowNET.Keras.UnitTest/Range.cs

@@ -1,7 +1,7 @@
 <Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
-    <TargetFramework>netcoreapp3.1</TargetFramework>
+    <TargetFramework>net5.0</TargetFramework>
 
     <IsPackable>false</IsPackable>
 

src/TensorFlowNET.Core/Util/RuntimeHelpers.cs renamed to test/TensorFlowNET.Keras.UnitTest/RuntimeHelpers.cs

@@ -119,7 +119,7 @@ public void Map()
             long value = 0;
 
             var dataset = tf.data.Dataset.range(0, 2);
-            dataset = dataset.map(x => x + 10);
+            dataset = dataset.map(x => x[0] + 10);
 
             foreach (var item in dataset)
             {
@@ -147,7 +147,7 @@ public void Cache()
         public void Cardinality()
         {
             var dataset = tf.data.Dataset.range(10);
-            dataset = dataset.map(x => x + 1);
+            dataset = dataset.map(x => x[0] + 1);
             var cardinality = dataset.dataset_cardinality();
             Assert.AreEqual(new long[] { 10 }, cardinality.numpy());
         }