SciSharp · Oceania2018 · Mar 4, 2023 · Mar 4, 2023 · Mar 4, 2023 · Mar 4, 2023
@@ -19,6 +19,8 @@ public class DatasetV2 : IDatasetV2
 
         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 @@ public IDatasetV2 apply_options()
 
             // (4) Apply stats aggregator options
 
+            dataset.FirstInputTensorCount = this.FirstInputTensorCount;
             return dataset;
         }
 
@@ -142,7 +145,7 @@ public override string ToString()
             $"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 @@ public override string ToString()
                     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)));
             }
         }
 

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

@@ -27,7 +27,8 @@ void _create_iterator(IDatasetV2 dataset)
             _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 @@ public Tensor[] next()
 
         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);
         }
     }
 }
@@ -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; }

@@ -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;

@@ -24,6 +24,17 @@ ICallback fit(NDArray x, NDArray y,
             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,

@@ -14,7 +14,76 @@ public void Deconstruct(out byte blue, out byte green, out byte red)
             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)

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

@@ -49,5 +49,8 @@ public IEnumerator<NDArray> GetEnumerator()
 
         IEnumerator IEnumerable.GetEnumerator()
             => GetEnumerator();
+
+        public static explicit operator NDArray(Array array)
+            => new NDArray(array);
     }
 }
@@ -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 @@ public Tensor model_dataset(Tensor input_dataset,
             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>

@@ -10,7 +10,7 @@ public abstract class DataAdapter
         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 @@ public virtual IDatasetV2 GetDataset()
         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);
         }
 

@@ -93,11 +93,15 @@ long _infer_steps(int steps_per_epoch, IDatasetV2 dataset)
 
         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()

@@ -13,10 +13,10 @@ public interface IDataAdapter
         /// <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();
     }
 }
@@ -1,4 +1,5 @@
 using System;
+using System.Diagnostics;
 using System.Linq;
 using Tensorflow.Keras.ArgsDefinition;
 using static Tensorflow.Binding;
@@ -20,7 +21,7 @@ public TensorLikeDataAdapter(DataAdapterArgs args)
         {
             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 @@ public TensorLikeDataAdapter(DataAdapterArgs args)
             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 @@ IDatasetV2 slice_inputs(IDatasetV2 indices_dataset, Tensors elements)
             return dataset.with_options(new DatasetOptions { });
         }
 
-        public override int GetSize()
-            => _size;
+        public override int GetSize() => _size;
+
+        public override bool ShouldRecreateIterator() => false;
 
         void _process_tensorlike()
         {