Support the multiple inputs of keras model.fit.

src/TensorFlowNET.Core/Data/DatasetV2.cs

@@ -19,6 +19,8 @@ namespace Tensorflow
 
         public TensorSpec[] structure { get; set; }
 
+        public int FirstInputTensorCount { get; set; } = 1;
+
         public Shape[] output_shapes => structure.Select(x => x.shape).ToArray();
 
         public TF_DataType[] output_types => structure.Select(x => x.dtype).ToArray();
@@ -131,6 +133,7 @@ namespace Tensorflow
 
             // (4) Apply stats aggregator options
 
+            dataset.FirstInputTensorCount = this.FirstInputTensorCount;
             return dataset;
         }
 
@@ -142,7 +145,7 @@ namespace Tensorflow
             $"types: {string.Join(", ", structure.Select(x => "tf." + x.dtype.as_numpy_name()))}, " +
             $"len: {length}";
 
-        public IEnumerator<(Tensor, Tensor)> GetEnumerator()
+        public IEnumerator<(Tensors, Tensors)> GetEnumerator()
         {
             using var ownedIterator = new OwnedIterator(this);
 
@@ -158,7 +161,8 @@ namespace Tensorflow
                     break;
                 }
 
-                yield return (results[0], results.Length == 1 ? null : results[1]);
+                yield return (new Tensors(results.Take(FirstInputTensorCount)), results.Length == FirstInputTensorCount ? 
+                    null : new Tensors(results.Skip(FirstInputTensorCount)));
             }
         }
 

src/TensorFlowNET.Core/Data/IDatasetV2.cs

@@ -4,7 +4,7 @@ using Tensorflow.Framework.Models;
 
 namespace Tensorflow
 {
-    public interface IDatasetV2 : IEnumerable<(Tensor, Tensor)>
+    public interface IDatasetV2 : IEnumerable<(Tensors, Tensors)>
     {
         string[] class_names { get; set; }
 
@@ -18,6 +18,8 @@ namespace Tensorflow
 
         TensorSpec[] structure { get; set; }
 
+        int FirstInputTensorCount { get; set; }
+
         /// <summary>
         /// Caches the elements in this dataset.
         /// </summary>

src/TensorFlowNET.Core/Data/OwnedIterator.cs

@@ -27,7 +27,8 @@ namespace Tensorflow
             _dataset = dataset;
             _element_spec = dataset.element_spec;
             // _flat_output_types = 
-            (_iterator_resource, _deleter) = ops.anonymous_iterator_v2(_dataset.output_types, _dataset.output_shapes);
+            _iterator_resource = ops.anonymous_iterator_v3(_dataset.output_types, _dataset.output_shapes);
+            // TODO(Rinne): deal with graph mode.
             ops.make_iterator(dataset.variant_tensor, _iterator_resource);
         }
 
@@ -48,7 +49,7 @@ namespace Tensorflow
 
         public void Dispose()
         {
-            tf.Runner.Execute(tf.Context, "DeleteIterator", 0, new[] { _iterator_resource, _deleter }, null);
+            //tf.Runner.Execute(tf.Context, "DeleteIterator", 0, new[] { _iterator_resource, _deleter }, null);
         }
     }
 }

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

@@ -5,8 +5,8 @@ namespace Tensorflow.Keras.ArgsDefinition
 {
     public class DataAdapterArgs: IKerasConfig
     {
-        public Tensor X { get; set; }
-        public Tensor Y { get; set; }
+        public Tensors X { get; set; }
+        public Tensors Y { get; set; }
         public IDatasetV2 Dataset { get; set; }
         public int BatchSize { get; set; } = 32;
         public int Steps { get; set; }

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

@@ -5,8 +5,8 @@ namespace Tensorflow.Keras.ArgsDefinition
 {
     public class DataHandlerArgs: IKerasConfig
     {
-        public Tensor X { get; set; }
-        public Tensor Y { get; set; }
+        public Tensors X { get; set; }
+        public Tensors Y { get; set; }
         public IDatasetV2 Dataset { get; set; }
         public int BatchSize { get; set; } = 32;
         public int StepsPerEpoch { get; set; } = -1;

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

@@ -24,6 +24,17 @@ public interface IModel : ILayer
             int workers = 1,
             bool use_multiprocessing = false);
 
+    ICallback fit(IEnumerable<NDArray> x, NDArray y,
+            int batch_size = -1,
+            int epochs = 1,
+            int verbose = 1,
+            float validation_split = 0f,
+            bool shuffle = true,
+            int initial_epoch = 0,
+            int max_queue_size = 10,
+            int workers = 1,
+            bool use_multiprocessing = false);
+
     void save(string filepath,
             bool overwrite = true,
             bool include_optimizer = true,

src/TensorFlowNET.Core/NumPy/NDArray.Implicit.cs

@@ -14,7 +14,76 @@ namespace Tensorflow.NumPy
             red = data[2];
         }
 
-        public static implicit operator NDArray(Array array)
+        public static implicit operator NDArray(int[] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(byte[] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(float[] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(double[] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(long[] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(bool[] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(uint[] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(ulong[] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(int[,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(byte[,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(float[,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(double[,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(long[,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(bool[,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(uint[,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(ulong[,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(int[,,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(byte[,,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(float[,,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(double[,,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(long[,,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(bool[,,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(uint[,,] array)
+            => new NDArray(array);
+
+        public static implicit operator NDArray(ulong[,,] array)
             => new NDArray(array);
 
         public unsafe static implicit operator bool(NDArray nd)

src/TensorFlowNET.Core/NumPy/Persistence/NpzDictionaryArray.cs

@@ -25,7 +25,7 @@ public class NpzDictionary
             return array;
 
         using var s = entry.Open();
-        return LoadMatrix(s);
+        return (NDArray)LoadMatrix(s);
     }
 
     public Array LoadMatrix(Stream stream)

src/TensorFlowNET.Core/Numpy/NDArray.cs

@@ -49,5 +49,8 @@ namespace Tensorflow.NumPy
 
         IEnumerator IEnumerable.GetEnumerator()
             => GetEnumerator();
+
+        public static explicit operator NDArray(Array array)
+            => new NDArray(array);
     }
 }

src/TensorFlowNET.Core/Operations/dataset_ops.cs

@@ -1,6 +1,9 @@
 using System;
+using Tensorflow.Contexts;
+using Tensorflow.Eager;
 using Tensorflow.Framework.Models;
 using Tensorflow.Functions;
+using Tensorflow.Operations;
 using static Tensorflow.Binding;
 
 namespace Tensorflow
@@ -220,6 +223,37 @@ namespace Tensorflow
             return (results[0], results[1]);
         }
 
+        public Tensor anonymous_iterator_v3(TF_DataType[] output_types, Shape[] output_shapes, string name = null)
+        {
+            var ctx = tf.Context;
+            Dictionary<string, object> attrs = new();
+            attrs["output_types"] = output_types;
+            attrs["output_shapes"] = output_shapes;
+            if (ctx.executing_eagerly())
+            {
+                try
+                {
+                    var result = tf.Runner.TFE_FastPathExecute(new FastPathOpExecInfo("AnonymousIteratorV3", name)
+                    {
+                        attrs = attrs
+                    });
+                    return result[0];
+                }
+                catch (Exception)
+                {
+                    return anonymous_iterator_v3_eager_fallback(output_types, output_shapes, name, ctx);
+                }
+            }
+            return tf.OpDefLib._apply_op_helper("AnonymousIteratorV3", name, attrs).outputs[0];
+        }
+
+        public Tensor anonymous_iterator_v3_eager_fallback(TF_DataType[] output_types, Shape[] output_shapes, string name, Context ctx)
+        {
+            object[] attrs = new object[] { output_types, output_shapes };
+            var result = execute.quick_execute("AnonymousIteratorV3", 1, new Tensor[] { }, attrs, ctx, name);
+            return result[0];
+        }
+
         /// <summary>
         /// Makes a new iterator from the given `dataset` and stores it in `iterator`.
         /// </summary>

src/TensorFlowNET.Keras/Engine/DataAdapters/DataAdapter.cs

@@ -10,7 +10,7 @@ namespace Tensorflow.Keras.Engine.DataAdapters
         protected DataAdapterArgs args;
         protected IDatasetV2 dataset;
 
-        public virtual bool CanHandle(Tensor x, Tensor y = null)
+        public virtual bool CanHandle(Tensors x, Tensors y = null)
             => throw new NotImplementedException();
 
         public virtual IDatasetV2 GetDataset()
@@ -19,12 +19,18 @@ namespace Tensorflow.Keras.Engine.DataAdapters
         public virtual int GetSize()
             => throw new NotImplementedException("");
 
-        public virtual (Tensor, Tensor) Expand1d(Tensor x, Tensor y)
+        public virtual (Tensors, Tensors) Expand1d(Tensors x, Tensors y)
         {
-            if (x.shape.ndim == 1)
-                x = array_ops.expand_dims(x, axis: -1);
-            if (y.shape.ndim == 1)
-                y = array_ops.expand_dims(y, axis: -1);
+            for(int i = 0; i < x.Length; i++)
+            {
+                if (x[i].shape.ndim == 1)
+                    x[i] = array_ops.expand_dims(x[i], axis: -1);
+            }
+            for (int i = 0; i < y.Length; i++)
+            {
+                if (y[i].shape.ndim == 1)
+                    y[i] = array_ops.expand_dims(y[i], axis: -1);
+            }
             return (x, y);
         }
 

src/TensorFlowNET.Keras/Engine/DataAdapters/DataHandler.cs

@@ -93,11 +93,15 @@ namespace Tensorflow.Keras.Engine.DataAdapters
 
         public IEnumerable<(int, OwnedIterator)> enumerate_epochs()
         {
+            var data_iterator = new OwnedIterator(_dataset);
             foreach (var epoch in range(_initial_epoch, _epochs))
             {
                 if (_insufficient_data)
                     break;
-                using var data_iterator = new OwnedIterator(_dataset);
+                if (_adapter.ShouldRecreateIterator())
+                {
+                    data_iterator = new OwnedIterator(_dataset);
+                }
                 yield return (epoch, data_iterator);
             }
             // _adapter.on_epoch_end()

src/TensorFlowNET.Keras/Engine/DataAdapters/IDataAdapter.cs

@@ -13,10 +13,10 @@
         /// <param name="x">input features</param>
         /// <param name="y">target labels</param>
         /// <returns></returns>
-        bool CanHandle(Tensor x, Tensor y = null);
+        bool CanHandle(Tensors x, Tensors y = null);
         IDatasetV2 GetDataset();
         int GetSize();
-        (Tensor, Tensor) Expand1d(Tensor x, Tensor y);
+        (Tensors, Tensors) Expand1d(Tensors x, Tensors y);
         bool ShouldRecreateIterator();
     }
 }

src/TensorFlowNET.Keras/Engine/DataAdapters/TensorLikeDataAdapter.cs

@@ -1,4 +1,5 @@
 using System;
+using System.Diagnostics;
 using System.Linq;
 using Tensorflow.Keras.ArgsDefinition;
 using static Tensorflow.Binding;
@@ -20,7 +21,7 @@ namespace Tensorflow.Keras.Engine.DataAdapters
         {
             this.args = args;
             _process_tensorlike();
-            num_samples = (int)args.X.shape[0];
+            num_samples = (int)args.X[0].shape[0];
             var batch_size = args.BatchSize == -1 ? 32 : args.BatchSize;
             _batch_size = batch_size;
             _size = Convert.ToInt32(Math.Ceiling(num_samples / (batch_size + 0.0f)));
@@ -33,10 +34,11 @@ namespace Tensorflow.Keras.Engine.DataAdapters
             indices_dataset = indices_dataset.flat_map(slice_batch_indices);
             var inputs = new Tensors();
             if (args.X != null)
-                inputs.Add(args.X);
+                inputs.AddRange(args.X);
             if (args.Y != null)
-                inputs.Add(args.Y);
+                inputs.AddRange(args.Y);
             dataset = slice_inputs(indices_dataset, inputs);
+            dataset.FirstInputTensorCount = args.X.Length;
         }
 
         Tensors permutation(Tensors tensor)
@@ -87,8 +89,9 @@ namespace Tensorflow.Keras.Engine.DataAdapters
             return dataset.with_options(new DatasetOptions { });
         }
 
-        public override int GetSize()
-            => _size;
+        public override int GetSize() => _size;
+
+        public override bool ShouldRecreateIterator() => false;
 
         void _process_tensorlike()
         {

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

@@ -59,7 +59,62 @@ namespace Tensorflow.Keras.Engine
                 StepsPerExecution = _steps_per_execution
             });
 
-            return FitInternal(data_handler, epochs, verbose);
+            return FitInternal(data_handler, epochs, verbose, validation_data: null,
+                    train_step_func: train_step_function);
+        }
+
+        public ICallback fit(IEnumerable<NDArray> x, NDArray y,
+            int batch_size = -1,
+            int epochs = 1,
+            int verbose = 1,
+            float validation_split = 0f,
+            bool shuffle = true,
+            int initial_epoch = 0,
+            int max_queue_size = 10,
+            int workers = 1,
+            bool use_multiprocessing = false)
+        {
+            foreach(var tx in x)
+            {
+                if (tx.dims[0] != y.dims[0])
+                {
+                    throw new InvalidArgumentError(
+                        $"The array x and y should have same value at dim 0, but got {tx.dims[0]} and {y.dims[0]}");
+                }
+            }
+            int train_count = Convert.ToInt32(y.dims[0] * (1 - validation_split));
+            
+            var train_x = x.Select(x => x[new Slice(0, train_count)] as Tensor);
+            var train_y = y[new Slice(0, train_count)];
+            var val_x = x.Select(x => x[new Slice(train_count)] as Tensor);
+            var val_y = y[new Slice(train_count)];
+
+            var data_handler = new DataHandler(new DataHandlerArgs
+            {
+                X = new Tensors(train_x),
+                Y = train_y,
+                BatchSize = batch_size,
+                InitialEpoch = initial_epoch,
+                Epochs = epochs,
+                Shuffle = shuffle,
+                MaxQueueSize = max_queue_size,
+                Workers = workers,
+                UseMultiprocessing = use_multiprocessing,
+                Model = this,
+                StepsPerExecution = _steps_per_execution
+            });
+
+            if (data_handler.DataAdapter.GetDataset().structure.Length > 2 ||
+                data_handler.DataAdapter.GetDataset().FirstInputTensorCount > 1)
+            {
+                return FitInternal(data_handler, epochs, verbose, validation_data: null,
+                    train_step_func: train_step_multi_inputs_function);
+            }
+            else
+            {
+                return FitInternal(data_handler, epochs, verbose, validation_data: null,
+                    train_step_func: train_step_function);
+            }
         }
 
         public History fit(IDatasetV2 dataset, 
@@ -88,10 +143,12 @@ namespace Tensorflow.Keras.Engine
                 StepsPerExecution = _steps_per_execution
             });
 
-            return FitInternal(data_handler, epochs, verbose, validation_data: validation_data);
+            return FitInternal(data_handler, epochs, verbose, validation_data: validation_data,
+                    train_step_func: train_step_function);
         }
 
-        History FitInternal(DataHandler data_handler, int epochs, int verbose, IDatasetV2 validation_data = null)
+        History FitInternal(DataHandler data_handler, int epochs, int verbose, IDatasetV2 validation_data, 
+            Func<DataHandler, OwnedIterator, Dictionary<string, float>> train_step_func)
         {
             stop_training = false;
             _train_counter.assign(0);
@@ -113,7 +170,7 @@ namespace Tensorflow.Keras.Engine
                 foreach (var step in data_handler.steps())
                 {
                     callbacks.on_train_batch_begin(step);
-                    logs = train_step_function(data_handler, iterator);
+                    logs = train_step_func(data_handler, iterator);
                     var end_step = step + data_handler.StepIncrement;
                     callbacks.on_train_batch_end(end_step, logs);
                 }

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

@@ -17,12 +17,21 @@ namespace Tensorflow.Keras.Engine
             return outputs;
         }
 
+        Dictionary<string, float> train_step_multi_inputs_function(DataHandler data_handler, OwnedIterator iterator)
+        {
+            var data = iterator.next();
+            var x_size = data_handler.DataAdapter.GetDataset().FirstInputTensorCount;
+            var outputs = train_step(data_handler, new Tensors(data.Take(x_size)), new Tensors(data.Skip(x_size)));
+            tf_with(ops.control_dependencies(new object[0]), ctl => _train_counter.assign_add(1));
+            return outputs;
+        }
+
         /// <summary>
         /// The logic for one training step.
         /// </summary>
         /// <param name="data"></param>
         /// <returns></returns>
-        Dictionary<string, float> train_step(DataHandler data_handler, Tensor x, Tensor y)
+        Dictionary<string, float> train_step(DataHandler data_handler, Tensors x, Tensors y)
         {
             (x, y) = data_handler.DataAdapter.Expand1d(x, y);
             using var tape = tf.GradientTape();

test/TensorFlowNET.Keras.UnitTest/Helpers/RandomDataset.cs

@@ -0,0 +1,30 @@
+using System;
+using System.Collections.Generic;
+using System.Diagnostics;
+using System.Linq;
+using System.Text;
+using System.Threading.Tasks;
+using Tensorflow.NumPy;
+
+namespace Tensorflow.Keras.UnitTest.Helpers
+{
+    public class RandomDataSet : DataSetBase
+    {
+        private Shape _shape;
+
+        public RandomDataSet(Shape shape, int count)
+        {
+            _shape = shape;
+            Debug.Assert(_shape.ndim == 3);
+            long[] dims = new long[4];
+            dims[0] = count;
+            for (int i = 1; i < 4; i++)
+            {
+                dims[i] = _shape[i - 1];
+            }
+            Shape s = new Shape(dims);
+            Data = np.random.normal(0, 2, s);
+            Labels = np.random.uniform(0, 1, (count, 1));
+        }
+    }
+}

test/TensorFlowNET.Keras.UnitTest/MultiInputModelTest.cs

@@ -0,0 +1,69 @@
+using Microsoft.VisualStudio.TestPlatform.Utilities;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+using System.Threading.Tasks;
+using System.Xml.Linq;
+using Tensorflow.Operations;
+using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
+using Tensorflow.NumPy;
+using Microsoft.VisualBasic;
+using static HDF.PInvoke.H5T;
+using Tensorflow.Keras.UnitTest.Helpers;
+using Tensorflow.Keras.Optimizers;
+
+namespace Tensorflow.Keras.UnitTest
+{
+    [TestClass]
+    public class MultiInputModelTest
+    {
+        [TestMethod]
+        public void SimpleModel()
+        {
+            var inputs = keras.Input((28, 28, 1));
+            var conv1 = keras.layers.Conv2D(16, (3, 3), activation: "relu", padding: "same").Apply(inputs);
+            var pool1 = keras.layers.MaxPooling2D((2, 2), 2).Apply(conv1);
+            var conv2 = keras.layers.Conv2D(32, (3, 3), activation: "relu", padding: "same").Apply(pool1);
+            var pool2 = keras.layers.MaxPooling2D((2, 2), 2).Apply(conv2);
+            var flat1 = keras.layers.Flatten().Apply(pool2);
+
+            var inputs_2 = keras.Input((28, 28, 1));
+            var conv1_2 = keras.layers.Conv2D(16, (3, 3), activation: "relu", padding: "same").Apply(inputs_2);
+            var pool1_2 = keras.layers.MaxPooling2D((4, 4), 4).Apply(conv1_2);
+            var conv2_2 = keras.layers.Conv2D(32, (1, 1), activation: "relu", padding: "same").Apply(pool1_2);
+            var pool2_2 = keras.layers.MaxPooling2D((2, 2), 2).Apply(conv2_2);
+            var flat1_2 = keras.layers.Flatten().Apply(pool2_2);
+
+            var concat = keras.layers.Concatenate().Apply((flat1, flat1_2));
+            var dense1 = keras.layers.Dense(512, activation: "relu").Apply(concat);
+            var dense2 = keras.layers.Dense(128, activation: "relu").Apply(dense1);
+            var dense3 = keras.layers.Dense(10, activation:  "relu").Apply(dense2);
+            var output = keras.layers.Softmax(-1).Apply(dense3);
+
+            var model = keras.Model((inputs, inputs_2), output);
+            model.summary();
+
+            var data_loader = new MnistModelLoader();
+
+            var dataset = data_loader.LoadAsync(new ModelLoadSetting
+            {
+                TrainDir = "mnist",
+                OneHot = false,
+                ValidationSize = 59000,
+            }).Result;
+
+            var loss = keras.losses.SparseCategoricalCrossentropy();
+            var optimizer = new Adam(0.001f);
+            model.compile(optimizer, loss, new string[] { "accuracy" });
+
+            NDArray x1 = np.reshape(dataset.Train.Data, (dataset.Train.Data.shape[0], 28, 28, 1));
+            NDArray x2 = x1;
+
+            var x = new NDArray[] { x1, x2 };
+            model.fit(x, dataset.Train.Labels, batch_size: 8, epochs: 3);
+        }
+    }
+}

test/TensorFlowNET.Keras.UnitTest/SaveModel/SequentialModelLoad.cs

@@ -13,6 +13,7 @@ using Tensorflow;
 using Tensorflow.Keras.Optimizers;
 using static Tensorflow.KerasApi;
 using Tensorflow.NumPy;
+using Tensorflow.Keras.UnitTest.Helpers;
 using static TensorFlowNET.Keras.UnitTest.SaveModel.SequentialModelSave;
 
 namespace TensorFlowNET.Keras.UnitTest.SaveModel;

test/TensorFlowNET.Keras.UnitTest/SaveModel/SequentialModelSave.cs

@@ -6,7 +6,7 @@ using Tensorflow.Keras;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Losses;
 using Tensorflow.Keras.Optimizers;
-using Tensorflow.NumPy;
+using Tensorflow.Keras.UnitTest.Helpers;
 using static Tensorflow.Binding;
 using static Tensorflow.KerasApi;
 
@@ -175,24 +175,4 @@ public class SequentialModelSave
         //    )
         #endregion
     }
-
-    public class RandomDataSet : DataSetBase
-    {
-        private Shape _shape;
-
-        public RandomDataSet(Shape shape, int count)
-        {
-            _shape = shape;
-            Debug.Assert(_shape.ndim == 3);
-            long[] dims = new long[4];
-            dims[0] = count;
-            for (int i = 1; i < 4; i++)
-            {
-                dims[i] = _shape[i - 1];
-            }
-            Shape s = new Shape(dims);
-            Data = np.random.normal(0, 2, s);
-            Labels = np.random.uniform(0, 1, (count, 1));
-        }
-    }
 }

test/TensorFlowNET.UnitTest/Dataset/DatasetTest.cs

@@ -20,7 +20,7 @@ namespace TensorFlowNET.UnitTest.Dataset
                 Assert.AreEqual(iStep, step);
                 iStep++;
 
-                Assert.AreEqual(value, (long)item.Item1);
+                Assert.AreEqual(value, (long)item.Item1[0]);
                 value++;
             }
         }
@@ -39,7 +39,7 @@ namespace TensorFlowNET.UnitTest.Dataset
                 Assert.AreEqual(iStep, step);
                 iStep++;
 
-                Assert.AreEqual(value, (long)item.Item1);
+                Assert.AreEqual(value, (long)item.Item1[0]);
                 value += 2;
             }
         }
@@ -54,7 +54,7 @@ namespace TensorFlowNET.UnitTest.Dataset
             int n = 0;
             foreach (var (item_x, item_y) in dataset)
             {
-                print($"x:{item_x.numpy()},y:{item_y.numpy()}");
+                print($"x:{item_x[0].numpy()},y:{item_y[0].numpy()}");
                 n += 1;
             }
             Assert.AreEqual(5, n);
@@ -69,7 +69,7 @@ namespace TensorFlowNET.UnitTest.Dataset
             int n = 0;
             foreach (var x in dataset)
             {
-                Assert.IsTrue(X.SequenceEqual(x.Item1.ToArray<int>()));
+                Assert.IsTrue(X.SequenceEqual(x.Item1[0].ToArray<int>()));
                 n += 1;
             }
             Assert.AreEqual(1, n);
@@ -85,7 +85,7 @@ namespace TensorFlowNET.UnitTest.Dataset
 
             foreach (var item in dataset2)
             {
-                Assert.AreEqual(value, (long)item.Item1);
+                Assert.AreEqual(value, (long)item.Item1[0]);
                 value += 3;
             }
 
@@ -93,7 +93,7 @@ namespace TensorFlowNET.UnitTest.Dataset
             var dataset3 = dataset1.shard(num_shards: 3, index: 1);
             foreach (var item in dataset3)
             {
-                Assert.AreEqual(value, (long)item.Item1);
+                Assert.AreEqual(value, (long)item.Item1[0]);
                 value += 3;
             }
         }
@@ -108,7 +108,7 @@ namespace TensorFlowNET.UnitTest.Dataset
 
             foreach (var item in dataset)
             {
-                Assert.AreEqual(value, (long)item.Item1);
+                Assert.AreEqual(value, (long)item.Item1[0]);
                 value++;
             }
         }
@@ -123,7 +123,7 @@ namespace TensorFlowNET.UnitTest.Dataset
 
             foreach (var item in dataset)
             {
-                Assert.AreEqual(value + 10, (long)item.Item1);
+                Assert.AreEqual(value + 10, (long)item.Item1[0]);
                 value++;
             }
         }
@@ -138,7 +138,7 @@ namespace TensorFlowNET.UnitTest.Dataset
 
             foreach (var item in dataset)
             {
-                Assert.AreEqual(value, (long)item.Item1);
+                Assert.AreEqual(value, (long)item.Item1[0]);
                 value++;
             }
         }