Fix Sequential model.

Author: Oceania2018

README.md

@@ -26,12 +26,12 @@ In comparison to other projects, like for instance [TensorFlowSharp](https://www
 
 ### How to use
 
-| TensorFlow                 | tf native1.14 | tf native 1.15 | tf native 2.3 |
-| -------------------------- | ------------- | -------------- | ------------- |
-| tf.net 0.3x, tf.keras 0.2 |               |                | x             |
-| tf.net 0.2x                |               | x              | x             |
-| tf.net 0.15                | x             | x              |               |
-| tf.net 0.14                | x             |                |               |
+| TensorFlow                 | tf native1.14, cuda 10.0 | tf native 1.15, cuda 10.0 | tf native 2.3, cuda 10.1 | tf native 2.4, cuda 11 |
+| -------------------------- | ------------- | -------------- | ------------- | ------------- |
+| tf.net 0.3x, tf.keras 0.2 |               |                | x | not compatible |
+| tf.net 0.2x          |               | x | x |              |
+| tf.net 0.15          | x | x |               |               |
+| tf.net 0.14          | x |                |               |               |
 
 Troubleshooting of running example or installation, please  refer [here](tensorflowlib/README.md).
 

src/SciSharp.TensorFlow.Redist/README.md

@@ -22,11 +22,19 @@ https://www.nuget.org/packages/SciSharp.TensorFlow.Redist
 
 Related merged [commits](https://github.com/SciSharp/TensorFlow.NET/commit/854a5ba61ad0e400623821236bd117cc24c6cb77).
 
+
+
+#### Download pre-build package
+
+[Mac OSX CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.4.0.tar.gz), [Linux CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.4.0.tar.gz), [Linux GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.4.0.tar.gz), [Windows CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.4.0.tar.gz), [Windows GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-2.4.0.zip)
+
+
+
 ####  Pack and Deploy ####
 
 On Windows, the tar command does not support extracting archives with symlinks. So when `dotnet pack` runs on Windows it will only package the Windows binaries.
 
 1. Run `dotnet pack SciSharp.TensorFlow.Redist.nupkgproj` under `src/SciSharp.TensorFlow.Redist` directory in Linux.
-2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.2.3.1.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json -t 600`
+2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.2.4.0.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json -t 600`
 
 

src/TensorFlowNET.Console/TensorFlowNET.Console.csproj

@@ -8,7 +8,7 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.3.0" />
+    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.3.1" />
   </ItemGroup>
 
   <ItemGroup>

src/TensorFlowNET.Core/Tensors/tensor_util.cs

@@ -574,7 +574,7 @@ public static string to_numpy_string(Tensor tensor)
                     return string.Join(string.Empty, nd.ToArray<byte>()
                         .Select(x => x < 32 || x > 127 ? "\\x" + x.ToString("x") : Convert.ToChar(x).ToString()));
                 case TF_DataType.TF_BOOL:
-                    return (nd.GetByte(0) > 0).ToString();
+                    return nd.GetBoolean(0).ToString();
                 case TF_DataType.TF_VARIANT:
                 case TF_DataType.TF_RESOURCE:
                     return "<unprintable>";

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

@@ -37,19 +37,38 @@ public DataHandler(DataHandlerArgs args)
                 _steps_per_execution_value = args.StepsPerExecution.numpy();
             }
 
-            _adapter = new TensorLikeDataAdapter(new TensorLikeDataAdapterArgs
+            if(args.Dataset == null)
             {
-                X = args.X,
-                Y = args.Y,
-                BatchSize = args.BatchSize,
-                Steps = args.StepsPerEpoch,
-                Epochs = args.Epochs - args.InitialEpoch,
-                Shuffle = args.Shuffle,
-                MaxQueueSize = args.MaxQueueSize,
-                Worker = args.Workers,
-                UseMultiprocessing = args.UseMultiprocessing,
-                Model = args.Model
-            });
+                _adapter = new TensorLikeDataAdapter(new DataAdapterArgs
+                {
+                    X = args.X,
+                    Y = args.Y,
+                    BatchSize = args.BatchSize,
+                    Steps = args.StepsPerEpoch,
+                    Epochs = args.Epochs - args.InitialEpoch,
+                    Shuffle = args.Shuffle,
+                    MaxQueueSize = args.MaxQueueSize,
+                    Worker = args.Workers,
+                    UseMultiprocessing = args.UseMultiprocessing,
+                    Model = args.Model
+                });
+            }
+            else
+            {
+                _adapter = new DatasetAdapter(new DataAdapterArgs
+                {
+                    Dataset = args.Dataset,
+                    BatchSize = args.BatchSize,
+                    Steps = args.StepsPerEpoch,
+                    Epochs = args.Epochs - args.InitialEpoch,
+                    Shuffle = args.Shuffle,
+                    MaxQueueSize = args.MaxQueueSize,
+                    Worker = args.Workers,
+                    UseMultiprocessing = args.UseMultiprocessing,
+                    Model = args.Model
+                });
+            }
+            
             _dataset = _adapter.GetDataset();
             _inferred_steps = _infer_steps(args.StepsPerEpoch, _dataset);
             _current_step = 0;
@@ -66,7 +85,8 @@ int _infer_steps(int steps_per_epoch, IDatasetV2 dataset)
             if (adapter_steps > -1)
                 return adapter_steps;
 
-            throw new NotImplementedException("");
+            var size = dataset.dataset_cardinality();
+            return size.numpy();
         }
 
         public IEnumerable<(int, OwnedIterator)> enumerate_epochs()

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

@@ -0,0 +1,35 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras.ArgsDefinition;
+
+namespace Tensorflow.Keras.Engine.DataAdapters
+{
+    public class DatasetAdapter : IDataAdapter
+    {
+        DataAdapterArgs args;
+        IDatasetV2 _dataset => args.Dataset;
+        public DatasetAdapter(DataAdapterArgs args)
+        {
+            this.args = args;
+        }
+
+        public bool CanHandle(Tensor x, Tensor y = null)
+        {
+            throw new NotImplementedException();
+        }
+
+        public IDatasetV2 GetDataset()
+            => _dataset;
+
+        public int GetSize()
+            => -1;
+
+        public (Tensor, Tensor) Expand1d(Tensor x, Tensor y)
+        {
+            if (y.TensorShape.ndim == 1)
+                y = array_ops.expand_dims(y, axis: -1);
+            return (x, y);
+        }
+    }
+}

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

@@ -9,14 +9,14 @@ namespace Tensorflow.Keras.Engine.DataAdapters
     /// </summary>
     public class TensorLikeDataAdapter : IDataAdapter
     {
-        TensorLikeDataAdapterArgs args;
+        DataAdapterArgs args;
         int _size;
         int _batch_size;
         int num_samples;
         int num_full_batches;
         IDatasetV2 _dataset;
 
-        public TensorLikeDataAdapter(TensorLikeDataAdapterArgs args)
+        public TensorLikeDataAdapter(DataAdapterArgs args)
         {
             this.args = args;
             _process_tensorlike();

src/TensorFlowNET.Keras/Engine/Functional.cs

@@ -39,10 +39,12 @@ public Functional(Tensors inputs, Tensors outputs, string name = null)
             _input_coordinates = new List<KerasHistory>();
             _output_coordinates = new List<KerasHistory>();
             tensor_usage_count = new Dictionary<int, int>();
+            if (this is Sequential)
+                return;
             _init_graph_network(inputs, outputs);
         }
 
-        void _init_graph_network(Tensors inputs, Tensors outputs)
+        protected void _init_graph_network(Tensors inputs, Tensors outputs)
         {
             _is_graph_network = true;
             this.inputs = inputs;

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

@@ -9,10 +9,6 @@ public partial class Model
     {
         LossesContainer compiled_loss;
         MetricsContainer compiled_metrics;
-        public void compile(string optimizerName, ILossFunc lossName)
-        {
-            throw new NotImplementedException("");
-        }
 
         public void compile(ILossFunc loss, OptimizerV2 optimizer, string[] metrics)
         {
@@ -29,12 +25,12 @@ public void compile(ILossFunc loss, OptimizerV2 optimizer, string[] metrics)
             this.loss = loss;
         }
 
-        public void compile(string optimizerName, string lossName)
+        public void compile(string optimizer, string loss, string[] metrics)
         {
-            switch (optimizerName)
+            switch (optimizer)
             {
                 case "rmsprop":
-                    optimizer = new RMSprop(new RMSpropArgs
+                    this.optimizer = new RMSprop(new RMSpropArgs
                     {
 
                     });

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

@@ -68,5 +68,49 @@ public void fit(NDArray x, NDArray y,
                 Console.WriteLine($"epoch: {epoch + 1}, " + string.Join(", ", results.Select(x => $"{x.Item1}: {(float)x.Item2}")));
             }
         }
+
+        public void fit(IDatasetV2 dataset, 
+            IDatasetV2 validation_data = null,
+            int batch_size = -1,
+            int epochs = 1,
+            int verbose = 1,
+            float validation_split = 0f,
+            bool shuffle = true,
+            int initial_epoch = 0,
+            int max_queue_size = 10,
+            int workers = 1,
+            bool use_multiprocessing = false)
+        {
+            data_handler = new DataHandler(new DataHandlerArgs
+            {
+                Dataset = dataset,
+                BatchSize = batch_size,
+                InitialEpoch = initial_epoch,
+                Epochs = epochs,
+                Shuffle = shuffle,
+                MaxQueueSize = max_queue_size,
+                Workers = workers,
+                UseMultiprocessing = use_multiprocessing,
+                Model = this,
+                StepsPerExecution = _steps_per_execution
+            });
+
+            stop_training = false;
+            _train_counter.assign(0);
+            Console.WriteLine($"Training...");
+            foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
+            {
+                // reset_metrics();
+                // callbacks.on_epoch_begin(epoch)
+                // data_handler.catch_stop_iteration();
+                IEnumerable<(string, Tensor)> results = null;
+                foreach (var step in data_handler.steps())
+                {
+                    // callbacks.on_train_batch_begin(step)
+                    results = step_function(iterator);
+                }
+                Console.WriteLine($"epoch: {epoch + 1}, " + string.Join(", ", results.Select(x => $"{x.Item1}: {(float)x.Item2}")));
+            }
+        }
     }
 }