Align keras.Input with tensorflow python.

src/TensorFlowNET.Core/Keras/Layers/ILayersApi.cs

@@ -1,4 +1,5 @@
 using System;
+using Tensorflow.Framework.Models;
 using Tensorflow.NumPy;
 using static Google.Protobuf.Reflection.FieldDescriptorProto.Types;
 
@@ -133,11 +134,16 @@ public ILayer EinsumDense(string equation,
         public ILayer GlobalMaxPooling1D(string data_format = "channels_last");
         public ILayer GlobalMaxPooling2D(string data_format = "channels_last");
 
-        public Tensors Input(Shape shape,
+        public Tensors Input(Shape shape = null,
             int batch_size = -1,
             string name = null,
+            TF_DataType dtype = TF_DataType.DtInvalid,
             bool sparse = false,
-            bool ragged = false);
+            Tensor tensor = null,
+            bool ragged = false,
+            TypeSpec type_spec = null,
+            Shape batch_input_shape = null,
+            Shape batch_shape = null);
         public ILayer InputLayer(Shape input_shape,
             string name = null,
             bool sparse = false,

src/TensorFlowNET.Keras/KerasInterface.cs

@@ -12,6 +12,7 @@
 using Tensorflow.Keras.Optimizers;
 using Tensorflow.Keras.Utils;
 using System.Threading;
+using Tensorflow.Framework.Models;
 
 namespace Tensorflow.Keras
 {
@@ -66,33 +67,16 @@ public Functional Model(Tensors inputs, Tensors outputs, string name = null)
         /// If set, the layer will not create a placeholder tensor.
         /// </param>
         /// <returns></returns>
-        public Tensor Input(Shape shape = null,
-                int batch_size = -1,
-                Shape batch_input_shape = null,
-                TF_DataType dtype = TF_DataType.DtInvalid,
-                string name = null,
-                bool sparse = false,
-                bool ragged = false,
-                Tensor tensor = null)
-        {
-            if (batch_input_shape != null)
-                shape = batch_input_shape.dims.Skip(1).ToArray();
-
-            var args = new InputLayerArgs
-            {
-                Name = name,
-                InputShape = shape,
-                BatchInputShape = batch_input_shape,
-                BatchSize = batch_size,
-                DType = dtype,
-                Sparse = sparse,
-                Ragged = ragged,
-                InputTensor = tensor
-            };
-
-            var layer = new InputLayer(args);
-
-            return layer.InboundNodes[0].Outputs;
-        }
+        public Tensors Input(Shape shape = null,
+            int batch_size = -1,
+            string name = null,
+            TF_DataType dtype = TF_DataType.DtInvalid,
+            bool sparse = false,
+            Tensor tensor = null,
+            bool ragged = false,
+            TypeSpec type_spec = null,
+            Shape batch_input_shape = null,
+            Shape batch_shape = null) => keras.layers.Input(shape, batch_size, name,
+                dtype, sparse, tensor, ragged, type_spec, batch_input_shape, batch_shape);
     }
 }

src/TensorFlowNET.Keras/Layers/LayersApi.cs

@@ -1,4 +1,5 @@
 using System;
+using Tensorflow.Framework.Models;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.ArgsDefinition.Core;
 using Tensorflow.Keras.ArgsDefinition.Rnn;
@@ -471,20 +472,56 @@ public ILayer Flatten(string data_format = null)
         /// In this case, values of 'None' in the 'shape' argument represent ragged dimensions. For more information about RaggedTensors, see this guide.
         /// </param>
         /// <returns>A tensor.</returns>
-        public Tensors Input(Shape shape,
+        public Tensors Input(Shape shape = null,
             int batch_size = -1,
             string name = null,
+            TF_DataType dtype = TF_DataType.DtInvalid, 
             bool sparse = false,
-            bool ragged = false)
+            Tensor tensor = null, 
+            bool ragged = false, 
+            TypeSpec type_spec = null, 
+            Shape batch_input_shape = null, 
+            Shape batch_shape = null)
         {
-            var input_layer = new InputLayer(new InputLayerArgs
+            if(sparse && ragged)
+            {
+                throw new ValueError("Cannot set both `sparse` and `ragged` to `true` in a Keras `Input`.");
+            }
+
+            InputLayerArgs input_layer_config = new()
             {
-                InputShape = shape,
-                BatchSize= batch_size,
                 Name = name,
+                DType = dtype,
                 Sparse = sparse,
-                Ragged = ragged
-            });
+                Ragged = ragged,
+                InputTensor = tensor,
+                // skip the `type_spec`
+            };
+
+            if(shape is not null && batch_input_shape is not null)
+            {
+                throw new ValueError("Only provide the `shape` OR `batch_input_shape` argument "
+                                    + "to Input, not both at the same time.");
+            }
+
+            if(batch_input_shape is null && shape is null && tensor is null && type_spec is null)
+            {
+                throw new ValueError("Please provide to Input a `shape` or a `tensor` or a `type_spec` argument. Note that " +
+                    "`shape` does not include the batch dimension.");
+            }
+
+            if(batch_input_shape is not null)
+            {
+                shape = batch_input_shape["1:"];
+                input_layer_config.BatchInputShape = batch_input_shape;
+            }
+            else
+            {
+                input_layer_config.BatchSize = batch_size;
+                input_layer_config.InputShape = shape;
+            }
+
+            var input_layer = new InputLayer(input_layer_config);
 
             return input_layer.InboundNodes[0].Outputs;
         }

test/TensorFlowNET.Keras.UnitTest/Layers/AttentionTest.cs

@@ -158,7 +158,7 @@ public void test_masked_attention()
             var value = keras.Input(shape: (2, 8));
             var mask_tensor = keras.Input(shape:(4, 2));
             var attention_layer = keras.layers.MultiHeadAttention(num_heads: 2, key_dim: 2);
-            attention_layer.Apply(new[] { query, value, mask_tensor });
+            attention_layer.Apply(new Tensor[] { query, value, mask_tensor });
 
             var from_data = 10 * np.random.randn(batch_size, 4, 8);
             var to_data = 10 * np.random.randn(batch_size, 2, 8);