- Starting to add operator overloads to the Tensor class;

- Updating Accord libraries to latest pre-release (3.6.4-alpha);
- Updating sessions / backend instances to be thread-local so unit tests can be run in parallel;
- Updating TensorFlowBackend to follow the Keras' Python counterpart more closely;
- Miscellaneous fixes to make GH-3 pass:

Fixes GH-3: Make the first example for a Dense layer pass.

Sources/Backends/Base/IBackend.cs

@@ -53,7 +53,7 @@ public interface IBackend : IDisposable
         TFDataType floatx();
 
         Tensor greater_equal(Tensor w, double v);
-
+        void clear_session();
         Tensor cast(object v1, object v2);
 
         Tensor dropout(object p, double retain_prob, object noise_shape, object seed);
@@ -75,7 +75,23 @@ public interface IBackend : IDisposable
 
         Tensor hard_sigmoid(Tensor x);
 
-        Tensor mul(double scale, Tensor tensor);
+
+
+        Tensor mul<T>(T a, Tensor b);
+
+        Tensor mul<T>(Tensor a, T b);
+
+        Tensor mul(List<Tensor> batch_outs, int length);
+
+
+
+        Tensor add(Tensor a, Tensor b);
+
+        Tensor add<T>(Tensor a, T b);
+
+        Tensor add<T>(T a, Tensor b);
+
+
 
         Tensor elu(Tensor x, double alpha);
 
@@ -95,13 +111,13 @@ public interface IBackend : IDisposable
 
         object eval(Tensor tensor);
 
-        Tensor add(Tensor desired, Tensor v);
+
 
         Tensor mul(Tensor w, Tensor tensor);
 
         Tensor clip(Tensor norms, double min_value, double max_value);
 
-        Tensor add(double v, Tensor tensor);
+
 
         Tensor random_uniform(int?[] shape, double minvalue = 0.0, double maxvalue = 1.0, TFDataType dtype = Utils.DEFAULT_DTYPE, int? seed = null, string name = null);
 
@@ -113,8 +129,6 @@ public interface IBackend : IDisposable
 
         Tensor mean(Tensor tensor, int axis);
 
-        Tensor const_(int v);
-
         Tensor abs(Tensor input);
 
         Tensor categorical_crossentropy(Tensor expected, Tensor actual);
@@ -131,7 +145,7 @@ public interface IBackend : IDisposable
 
         double subtract(double v, Tensor expected);
 
-        Tensor add(object v1, double v2);
+
 
         Tensor variable(Array array, string name = null);
 
@@ -143,7 +157,7 @@ public interface IBackend : IDisposable
 
         TFDataType? dtype(Tensor input_tensor);
 
-        Tensor constant<T>(T value, int?[] shape, TFDataType dtype = Utils.DEFAULT_DTYPE, string name = null);
+        Tensor constant<T>(T value, int?[] shape = null, TFDataType? dtype = null, string name = null);
 
         int get_uid(string prefix);
 
@@ -180,12 +194,8 @@ public interface IBackend : IDisposable
 
         Tensor placeholder(int ndim, string name);
 
-        Tensor mul(double rate, Tensor tensor1, Tensor tensor2);
-
         Tensor add(Tensor tensor);
 
-        Tensor mul(double rate, double v, Tensor tensor);
-
         object get_variable_shape(Tensor p);
 
         Tensor get_variable_shape(object s);
@@ -198,26 +208,19 @@ public interface IBackend : IDisposable
 
         Tensor add(object total_loss, object v);
 
-        Tensor const_(double v);
-
         object learning_phase();
 
         Function function(object inputs, List<Tensor> list, Func<List<List<Tensor>>> updates, string name);
 
         Function function(object inputs, List<Tensor> list, List<List<Tensor>> updates, string name);
 
-        Tensor mul(Tensor momentum, object v);
 
         Function function<TSource>(List<Tensor> inputs, List<object> list, List<TSource> updates, string name);
 
         Function function(List<Tensor> inputs, List<object> list, Func<List<object>> updates, string name);
 
         Tensor update(object m, object v);
 
-        double mul(Tensor batch_out, int length);
-
-        Tensor mul(List<Tensor> batch_outs, int length);
-
         Tensor div(Tensor tensor, int samples);
 
         Tensor truncated_normal(int[] shape, double v, double stddev, TFDataType dtype, int? seed);

Sources/Backends/Current.cs

@@ -32,11 +32,17 @@ namespace KerasSharp.Backends
     using System.Text;
     using System.Threading.Tasks;
     using KerasSharp.Engine.Topology;
+    using System.Threading;
 
     public static class Current
     {
 
-        public static IBackend K { get; set; } = new TensorFlowBackend();
+        private static ThreadLocal<IBackend> backend = new ThreadLocal<IBackend>(() => new TensorFlowBackend());
 
+        public static IBackend K
+        {
+            get { return backend.Value; }
+            set { backend.Value = value; }
+        }
     }
 }