fix dataset is disposed when zip dataset.

src/TensorFlowNET.Core/Data/ZipDataset.cs

@@ -6,8 +6,11 @@ namespace Tensorflow
 {
     public class ZipDataset : DatasetV2
     {
+        // keep all dataset references
+        IDatasetV2[] _inputs;
         public ZipDataset(params IDatasetV2[] ds)
         {
+            _inputs = ds;
             var input_datasets = ds.Select(x => x.variant_tensor).ToArray();
             var _structure = new List<TensorSpec>();
             foreach (var dataset in ds)

src/TensorFlowNET.Core/NumPy/NDArrayRender.cs

@@ -88,7 +88,7 @@ namespace Tensorflow.NumPy
             {
                 if (array.rank == 0)
                     return "'" + string.Join(string.Empty, array.StringBytes()[0]
-                        .Take(25)
+                        .Take(256)
                         .Select(x => x < 32 || x > 127 ? "\\x" + x.ToString("x") : Convert.ToChar(x).ToString())) + "'";
                 else
                     return $"'{string.Join("', '", array.StringData().Take(25))}'";

src/TensorFlowNET.Core/Tensorflow.Binding.csproj

@@ -20,7 +20,7 @@
     <Description>Google's TensorFlow full binding in .NET Standard.
 Building, training and infering deep learning models.
 https://tensorflownet.readthedocs.io</Description>
-    <AssemblyVersion>0.60.4.0</AssemblyVersion>
+    <AssemblyVersion>0.60.5.0</AssemblyVersion>
     <PackageReleaseNotes>tf.net 0.60.x and above are based on tensorflow native 2.6.0
 
 * Eager Mode is added finally.
@@ -35,7 +35,7 @@ Keras API is a separate package released as TensorFlow.Keras.
 tf.net 0.4x.x aligns with TensorFlow v2.4.1 native library.
 tf.net 0.5x.x aligns with TensorFlow v2.5.x native library.
 tf.net 0.6x.x aligns with TensorFlow v2.6.x native library.</PackageReleaseNotes>
-    <FileVersion>0.60.4.0</FileVersion>
+    <FileVersion>0.60.5.0</FileVersion>
     <PackageLicenseFile>LICENSE</PackageLicenseFile>
     <PackageRequireLicenseAcceptance>true</PackageRequireLicenseAcceptance>
     <SignAssembly>true</SignAssembly>

src/TensorFlowNET.Core/Tensors/tensor_util.cs

@@ -405,7 +405,7 @@ would not be rank 1.", tensor.op.get_attr("axis")));
 
             var ret = tensor.shape.unknown_shape((int)shape.dims[0]);
             var value = constant_value(tensor);
-            if (!(value is null))
+            if (value is not null)
             {
                 var d_ = new int[value.size];
                 foreach (var (index, d) in enumerate(value.ToArray<int>()))

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

@@ -1,5 +1,4 @@
-using System;
-using System.IO;
+using System.IO;
 using static Tensorflow.Binding;
 using Tensorflow.NumPy;
 
@@ -15,22 +14,8 @@ namespace Tensorflow.Keras
             int num_classes,
             string interpolation)
         {
-            // option 1: will load all images into memory, not efficient
-            var images = np.zeros((image_paths.Length, image_size[0], image_size[1], num_channels), np.float32);
-            for (int i = 0; i < len(images); i++)
-            {
-                var img = tf.io.read_file(image_paths[i]);
-                img = tf.image.decode_image(
-                    img, channels: num_channels, expand_animations: false);
-                var resized_image = tf.image.resize_images_v2(img, image_size, method: interpolation);
-                images[i] = resized_image.numpy();
-                tf_output_redirect.WriteLine(image_paths[i]);
-            };
-            var img_ds = tf.data.Dataset.from_tensor_slices(images);
-
-            // option 2: dynamic load, but has error, need to fix
-            // var path_ds = tf.data.Dataset.from_tensor_slices(image_paths);
-            // var img_ds = path_ds.map(x => path_to_image(x, image_size, num_channels, interpolation));
+            var path_ds = tf.data.Dataset.from_tensor_slices(image_paths);
+            var img_ds = path_ds.map(x => path_to_image(x, image_size, num_channels, interpolation));
             
             if (label_mode == "int")
             {
@@ -43,7 +28,7 @@ namespace Tensorflow.Keras
 
         Tensor path_to_image(Tensor path, Shape image_size, int num_channels, string interpolation)
         {
-            tf.print(path);
+            // tf.print(path);
             var img = tf.io.read_file(path);
             img = tf.image.decode_image(
                 img, channels: num_channels, expand_animations: false);
@@ -58,18 +43,8 @@ namespace Tensorflow.Keras
             int num_classes,
             int max_length = -1)
         {
-            var text = new string[image_paths.Length];
-            for (int i = 0; i < text.Length; i++)
-            {
-                text[i] = File.ReadAllText(image_paths[i]);
-                tf_output_redirect.WriteLine(image_paths[i]);
-            }
-
-            var images = np.array(text);
-            var string_ds = tf.data.Dataset.from_tensor_slices(images);
-
-            // var path_ds = tf.data.Dataset.from_tensor_slices(image_paths);
-            // var string_ds = path_ds.map(x => path_to_string_content(x, max_length));
+            var path_ds = tf.data.Dataset.from_tensor_slices(image_paths);
+            var string_ds = path_ds.map(x => path_to_string_content(x, max_length));
 
             if (label_mode == "int")
             {

src/TensorFlowNET.Keras/Tensorflow.Keras.csproj

@@ -37,8 +37,8 @@ Keras is an API designed for human beings, not machines. Keras follows best prac
     <RepositoryType>Git</RepositoryType>
     <SignAssembly>true</SignAssembly>
     <AssemblyOriginatorKeyFile>Open.snk</AssemblyOriginatorKeyFile>
-    <AssemblyVersion>0.6.4.0</AssemblyVersion>
-    <FileVersion>0.6.4.0</FileVersion>
+    <AssemblyVersion>0.6.5.0</AssemblyVersion>
+    <FileVersion>0.6.5.0</FileVersion>
     <PackageLicenseFile>LICENSE</PackageLicenseFile>
   </PropertyGroup>