Consolidate dataset map args.

src/TensorFlowNET.Core/Data/DatasetV2.cs

@@ -60,7 +60,7 @@ namespace Tensorflow
                 preserve_cardinality: preserve_cardinality,
                 use_legacy_function: use_legacy_function);
 
-        public IDatasetV2 map(Func<Tensor, (Tensor, Tensor), (Tensor, Tensor)> map_func, int num_parallel_calls = -1)
+        public IDatasetV2 map(Func<Tensors, Tensors> map_func, int num_parallel_calls = -1)
             => new ParallelMapDataset(this, map_func, num_parallel_calls: num_parallel_calls);
 
         public IDatasetV2 flat_map(Func<Tensor, IDatasetV2> map_func)

src/TensorFlowNET.Core/Data/IDatasetV2.cs

@@ -60,7 +60,7 @@ namespace Tensorflow
             bool preserve_cardinality = true,
             bool use_legacy_function = false);
 
-        IDatasetV2 map(Func<Tensor, (Tensor, Tensor), (Tensor, Tensor)> map_func,
+        IDatasetV2 map(Func<Tensors, Tensors> map_func,
             int num_parallel_calls = -1);
 
         IDatasetV2 flat_map(Func<Tensor, IDatasetV2> map_func);

src/TensorFlowNET.Core/Data/ParallelMapDataset.cs

@@ -9,7 +9,7 @@ namespace Tensorflow
     public class ParallelMapDataset : UnaryDataset
     {
         public ParallelMapDataset(IDatasetV2 input_dataset,
-            Func<Tensor, (Tensor, Tensor), (Tensor, Tensor)> map_func,
+            Func<Tensors, Tensors> map_func,
             int num_parallel_calls = -1,
             bool use_inter_op_parallelism = true,
             bool preserve_cardinality = false,

src/TensorFlowNET.Core/Data/TensorDataset.cs

@@ -12,8 +12,7 @@ namespace Tensorflow
         public TensorDataset(Tensors elements)
         {
             _tensors = elements;
-            var batched_spec = _tensors.Select(x => x.ToTensorSpec()).ToArray();
-            structure = batched_spec.Select(x => x._unbatch()).ToArray();
+            structure = _tensors.Select(x => x.ToTensorSpec()).ToArray();
 
             variant_tensor = ops.tensor_dataset(_tensors, output_shapes);
         }

src/TensorFlowNET.Core/Functions/ConcreteFunction.cs

@@ -83,7 +83,7 @@ namespace Tensorflow.Functions
             graph.Exit();
         }
 
-        public ConcreteFunction(Func<Tensor, (Tensor, Tensor), (Tensor, Tensor)> func,
+        public ConcreteFunction(Func<Tensors, Tensors> func,
             TF_DataType[] dtypes, TensorShape[] shapes)
         {
             string func_name = $"{func.Method.Name}_{Guid.NewGuid()}";
@@ -92,19 +92,14 @@ namespace Tensorflow.Functions
             using var graph = new FuncGraph(func_name);
             graph.as_default();
 
-            var input1 = tf.placeholder(dtypes[0], shape: shapes[0], name: "args");
-            var input2 = tf.placeholder(dtypes[1], shape: shapes[1], name: "args");
-            var input3 = tf.placeholder(dtypes[2], shape: shapes[2], name: "args");
-            var outputs = func(input1, (input2, input3));
-
-            Outputs = new[] { outputs.Item1, outputs.Item2 };
-            OutputStructure = new[] { outputs.Item1.ToTensorSpec(), outputs.Item2.ToTensorSpec() };
+            var inputs = new Tensors();
+            foreach(var (i, dtype) in enumerate(dtypes))
+                inputs.Add(tf.placeholder(dtypes[i], shape: shapes[i], name: "args"));
+            Outputs = func(inputs);
+            OutputStructure = Outputs.Select(x => x.ToTensorSpec()).ToArray();
 
             var opers = graph._nodes_by_name.Values.Select(x => x as Operation).ToArray();
-            _handle = graph.ToGraph(opers,
-                new[] { input1, input2, input3 },
-                new[] { outputs.Item1, outputs.Item2 },
-                null);
+            _handle = graph.ToGraph(opers, inputs, Outputs, null);
             graph.Exit();
         }
 

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

@@ -1,4 +1,5 @@
 using System;
+using System.Linq;
 using Tensorflow.Keras.ArgsDefinition;
 using static Tensorflow.Binding;
 
@@ -21,7 +22,7 @@ namespace Tensorflow.Keras.Engine.DataAdapters
             num_samples = args.X.shape[0];
             var batch_size = args.BatchSize == -1 ? 32 : args.BatchSize;
             _batch_size = batch_size;
-            _size = Convert.ToInt32(Math.Ceiling(num_samples / (batch_size + 0f)));
+            _size = Convert.ToInt32(Math.Floor(num_samples / (batch_size + 0f)));
             num_full_batches = num_samples / batch_size;
             var _partial_batch_size = num_samples % batch_size;
 
@@ -29,12 +30,12 @@ namespace Tensorflow.Keras.Engine.DataAdapters
             indices_dataset = indices_dataset.repeat(args.Epochs);
             indices_dataset = indices_dataset.map(permutation).prefetch(1);
             indices_dataset = indices_dataset.flat_map(slice_batch_indices);
-            var elements = new Tensors();
+            var inputs = new Tensors();
             if (args.X != null)
-                elements.Add(args.X);
+                inputs.Add(args.X);
             if (args.Y != null)
-                elements.Add(args.Y);
-            dataset = slice_inputs(indices_dataset, elements);
+                inputs.Add(args.Y);
+            dataset = slice_inputs(indices_dataset, inputs);
         }
 
         Tensor permutation(Tensor tensor)
@@ -64,11 +65,13 @@ namespace Tensorflow.Keras.Engine.DataAdapters
             var dataset2 = tf.data.Dataset.from_tensor(elements).repeat();
             var dataset = tf.data.Dataset.zip(indices_dataset, dataset2);
 
-            dataset = dataset.map((batch, data) =>
+            dataset = dataset.map(inputs =>
             {
-                var x = gen_array_ops.gather_v2(data.Item1, batch, 0);
-                var y = gen_array_ops.gather_v2(data.Item2, batch, 0);
-                return (x, y);
+                var indices = inputs[0];
+                var results = inputs.Skip(1)
+                    .Select(x => gen_array_ops.gather_v2(x, indices, 0))
+                    .ToArray();
+                return new Tensors(results);
             });
 
             dataset = dataset.with_options(new DatasetOptions { });

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

@@ -97,7 +97,7 @@ namespace Tensorflow.Keras.Engine
                     // callbacks.on_train_batch_begin(step)
                     var results = step_function(iterator);
                     var result_pairs = string.Join(", ", results.Select(x => $"{x.Item1}: {(float)x.Item2:F6}"));
-                    Console.WriteLine($"[Epoch: {epoch + 1:D3}/{epochs:D3}, Step: {step + 1:D4}/{data_handler.Inferredsteps:D4}, {result_pairs}]");
+                    Console.WriteLine($"Epoch: {epoch + 1:D3}/{epochs:D3}, Step: {step + 1:D4}/{data_handler.Inferredsteps:D4}, {result_pairs}");
                 }
             }
         }