Change filter as IVariableV1 in conv2d.

Author: Oceania2018

src/TensorFlowNET.Core/APIs/tf.nn.cs

@@ -32,7 +32,7 @@ public Tensor conv2d(Tensor input, IVariableV1 filter, int[] strides, string pad
                 var parameters = new Conv2dParams
                 {
                     Input = input,
-                    Filter = filter.AsTensor(),
+                    Filter = filter,
                     Strides = strides,
                     Padding = padding,
                     UseCudnnOnGpu = use_cudnn_on_gpu,
@@ -153,7 +153,7 @@ public Tensor bias_add(Tensor value, IVariableV1 bias, string data_format = null
                 return tf_with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
                 {
                     name = scope;
-                    return gen_nn_ops.bias_add(value, bias.AsTensor(), data_format: data_format, name: name);
+                    return gen_nn_ops.bias_add(value, bias, data_format: data_format, name: name);
                 });
             }
 

src/TensorFlowNET.Core/Eager/EagerRunner.TFE_FastPathExecute.cs

@@ -172,7 +172,7 @@ public Tensor[] TFE_FastPathExecute(Context ctx,
 
         SafeOpHandle GetOp(Context ctx, string op_or_function_name, Status status)
         {
-            if (thread_local_eager_operation_map.find(ctx, out var op))
+            /*if (thread_local_eager_operation_map.find(ctx, out var op))
                 c_api.TFE_OpReset(op, op_or_function_name, ctx.DeviceName, status.Handle);
             else
             {
@@ -181,7 +181,8 @@ SafeOpHandle GetOp(Context ctx, string op_or_function_name, Status status)
             }
 
             status.Check(true);
-            return op;
+            return op;*/
+            return c_api.TFE_NewOp(ctx.Handle, op_or_function_name, status.Handle);
         }
 
         bool HasAccumulator()

src/TensorFlowNET.Core/Operations/NnOps/BasicLSTMCell.cs

@@ -85,7 +85,7 @@ protected Tensors Call(Tensors inputs, Tensor state = null, bool is_training = f
                 throw new NotImplementedException("BasicLstmCell call");
             }
             var gate_inputs = math_ops.matmul(array_ops.concat(new[] { (Tensor)inputs, h }, 1), _kernel.AsTensor());
-            gate_inputs = nn_ops.bias_add(gate_inputs, _bias.AsTensor());
+            gate_inputs = nn_ops.bias_add(gate_inputs, _bias);
 
             // i = input_gate, j = new_input, f = forget_gate, o = output_gate
             var tensors = array_ops.split(value: gate_inputs, num_split: 4, axis: one);

src/TensorFlowNET.Core/Operations/NnOps/BasicRNNCell.cs

@@ -71,7 +71,7 @@ protected Tensors Call(Tensors inputs, Tensor state = null, bool is_training = f
             // Most basic RNN: output = new_state = act(W * input + U * state + B).
             var concat = array_ops.concat(new Tensor[] { inputs, state }, 1);
             var gate_inputs = math_ops.matmul(concat, _kernel.AsTensor());
-            gate_inputs = nn_ops.bias_add(gate_inputs, _bias.AsTensor());
+            gate_inputs = nn_ops.bias_add(gate_inputs, _bias);
             var output = _activation(gate_inputs, null);
             return new Tensors(output, output);
         }

src/TensorFlowNET.Core/Operations/NnOps/Conv2dParams.cs

@@ -42,7 +42,7 @@ public class Conv2dParams
         /// <summary>
         /// A 4-D tensor of shape
         /// </summary>
-        public Tensor Filter { get; set; }
+        public IVariableV1 Filter { get; set; }
 
         /// <summary>
         /// An integer vector representing the tensor shape of `filter`

src/TensorFlowNET.Core/Operations/NnOps/ConvolutionInternal.cs

@@ -60,12 +60,10 @@ public Tensor Apply(Tensors input, IVariableV1 filters)
                 name = scope;
                 if (num_spatial_dims == 2)
                 {
-                    var filters_tensor = filters.AsTensor();
-
                     result = gen_nn_ops.conv2d(new Conv2dParams
                     {
                         Input = input,
-                        Filter = filters_tensor,
+                        Filter = filters,
                         Strides = strides,
                         Padding = padding,
                         DataFormat = data_format,

src/TensorFlowNET.Core/Operations/NnOps/gen_nn_ops.cs

@@ -171,7 +171,7 @@ public static Tensor conv2d_backprop_input(Tensor input_sizes, Tensor filter, Te
         }
 
         public static Tensor bias_add(Tensor value,
-            Tensor bias,
+            IVariableV1 bias,
             string data_format = null,
             string name = null)
         {

src/TensorFlowNET.Core/Operations/nn_ops.cs

@@ -46,16 +46,14 @@ public static ConvolutionInternal convolution_internal(string padding,
         /// <param name="name"></param>
         /// <returns></returns>
         public static Tensor bias_add(Tensor value,
-            Tensor bias,
+            IVariableV1 bias,
             string data_format = null,
             string name = null)
         {
             return tf_with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
             {
                 name = scope;
-                value = ops.convert_to_tensor(value, name: "input");
-                var bias_tensor = ops.convert_to_tensor(bias, dtype: value.dtype, name: "bias");
-                return gen_nn_ops.bias_add(value, bias_tensor, data_format: data_format, name: name);
+                return gen_nn_ops.bias_add(value, bias, data_format: data_format, name: name);
             });
         }
 

src/TensorFlowNET.Keras/Layers/Convolution/Convolutional.cs

@@ -110,7 +110,7 @@ protected override Tensors Call(Tensors inputs, Tensor state = null, bool traini
                 }
                 else
                 {
-                    outputs = nn_ops.bias_add(outputs, bias.AsTensor(), data_format: "NHWC");
+                    outputs = nn_ops.bias_add(outputs, bias, data_format: "NHWC");
                 }
             }
 

test/TensorFlowNET.UnitTest/Basics/VariableTest.cs

@@ -121,5 +121,15 @@ public void ShouldReturnNegative()
             Assert.IsTrue(Enumerable.SequenceEqual(new[] { 1, 2 }, neg_x.shape));
             Assert.IsTrue(Enumerable.SequenceEqual(new[] { -1, -2 }, neg_x.numpy().ToArray<int>()));
         }
+
+        [TestMethod]
+        public void IdentityOriginalTensor()
+        {
+            var a = tf.Variable(5);
+            var a_identity = tf.identity(a);
+            a.assign_add(1);
+            Assert.AreEqual(5, (int)a_identity.numpy());
+            Assert.AreEqual(6, (int)a.numpy());
+        }
     }
 }