TextVectorization

src/TensorFlowNET.Core/Data/MapDataset.cs

@@ -17,9 +17,11 @@ namespace Tensorflow
         {
             var func = new ConcreteFunction($"{map_func.Method.Name}_{Guid.NewGuid()}");
             func.Enter();
-            var input = tf.placeholder(input_dataset.element_spec[0].dtype);
-            var output = map_func(input);
-            func.ToGraph(input, output);
+            var inputs = new Tensors();
+            foreach (var input in input_dataset.element_spec)
+                inputs.Add(tf.placeholder(input.dtype, shape: input.shape));
+            var outputs = map_func(inputs);
+            func.ToGraph(inputs, outputs);
             func.Exit();
 
             structure = func.OutputStructure;

src/TensorFlowNET.Core/Tensorflow.Binding.csproj

@@ -86,7 +86,7 @@ tf.net 0.4x.x aligns with TensorFlow v2.4.1 native library.</PackageReleaseNotes
   <ItemGroup>
     <PackageReference Include="MethodBoundaryAspect.Fody" Version="2.0.138" />
     <PackageReference Include="Microsoft.Extensions.DependencyInjection" Version="5.0.1" />
-    <PackageReference Include="NumSharp.Lite" Version="0.1.12" />
+    <PackageReference Include="NumSharp" Version="0.30.0" />
     <PackageReference Include="Protobuf.Text" Version="0.5.0" />
     <PackageReference Include="Serilog.Sinks.Console" Version="3.1.1" />
   </ItemGroup>

src/TensorFlowNET.Keras/Layers/Preprocessing/TextVectorization.cs

@@ -13,8 +13,40 @@ namespace Tensorflow.Keras.Layers
         public TextVectorization(TextVectorizationArgs args)
             : base(args)
         {
+            this.args = args;
             args.DType = TF_DataType.TF_STRING;
             // string standardize = "lower_and_strip_punctuation",
         }
+
+        /// <summary>
+        /// Fits the state of the preprocessing layer to the dataset.
+        /// </summary>
+        /// <param name="data"></param>
+        /// <param name="reset_state"></param>
+        public void adapt(IDatasetV2 data, bool reset_state = true)
+        {
+            var shape = data.output_shapes[0];
+            if (shape.rank == 1)
+                data = data.map(tensor => array_ops.expand_dims(tensor, -1));
+            build(data.variant_tensor);
+            var preprocessed_inputs = data.map(_preprocess);
+        }
+
+        protected override void build(Tensors inputs)
+        {
+            base.build(inputs);
+        }
+
+        Tensors _preprocess(Tensors inputs)
+        {
+            if (args.Standardize != null)
+                inputs = args.Standardize(inputs);
+            if (!string.IsNullOrEmpty(args.Split))
+            {
+                if (inputs.shape.ndim > 1)
+                    inputs = array_ops.squeeze(inputs, axis: new[] { -1 });
+            }
+            return inputs;
+        }
     }
 }

src/TensorFlowNET.Keras/Preprocessings/Preprocessing.cs

@@ -11,7 +11,7 @@ namespace Tensorflow.Keras
         public DatasetUtils dataset_utils => new DatasetUtils();
 
         public TextVectorization TextVectorization(Func<Tensor, Tensor> standardize = null,
-            string split = "standardize",
+            string split = "whitespace",
             int max_tokens = -1,
             string output_mode = "int",
             int output_sequence_length = -1) => new TextVectorization(new TextVectorizationArgs