gradient function Conv2DBackpropInput #895

Author: Oceania2018

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

@@ -26,7 +26,7 @@ public partial class tensorflow
 
         public class nn_internal
         {
-            public Tensor conv2d(Tensor input, IVariableV1 filter, int[] strides, string padding, bool use_cudnn_on_gpu = true,
+            public Tensor conv2d(Tensor input, Tensor filter, int[] strides, string padding, bool use_cudnn_on_gpu = true,
                 string data_format = "NHWC", int[] dilations = null, string name = null)
             {
                 var parameters = new Conv2dParams

src/TensorFlowNET.Core/Gradients/nn_grad.cs

@@ -133,6 +133,46 @@ public static Tensor[] _SquaredDifferenceGrad(Operation op, Tensor[] grads)
                 -x_grad
             };
         }
+
+        /// <summary>
+        /// The derivatives for deconvolution.
+        /// </summary>
+        /// <param name="op">The Deconvolution op.</param>
+        /// <param name="grads">The tensor representing the gradient w.r.t. the output</param>
+        /// <returns>The gradients w.r.t. the input and the filter</returns>
+        [RegisterGradient("Conv2DBackpropInput")]
+        public static Tensor[] _Conv2DBackpropInputGrad(Operation op, Tensor[] grads)
+        {
+            var grad = grads[0];
+            var dilations = op.get_attr_list<int>("dilations");
+            var strides = op.get_attr_list<int>("strides");
+            var padding = op.get_attr<string>("padding");
+            var explicit_paddings = op.get_attr_list<int>("explicit_paddings");
+            var use_cudnn_on_gpu = op.get_attr<bool>("use_cudnn_on_gpu");
+            var data_format = op.get_attr<string>("data_format");
+
+            return new Tensor[]
+            {
+                gen_nn_ops.conv2d_backprop_filter(grad, array_ops.shape(op.inputs[1]), op.inputs[2],
+                    strides, padding,
+                    use_cudnn_on_gpu: use_cudnn_on_gpu,
+                    explicit_paddings: explicit_paddings,
+                    dilations: dilations,
+                    data_format: data_format),
+                gen_nn_ops.conv2d(new Conv2dParams
+                    {
+                        Input = grad,
+                        Filter = op.inputs[1],
+                        Strides = strides,
+                        Padding = padding,
+                        DataFormat = data_format,
+                        Dilations = dilations,
+                        ExplicitPaddings = explicit_paddings,
+                        UseCudnnOnGpu = use_cudnn_on_gpu
+                    })
+            };
+        }
+
         /// <summary>
         /// Gradient function for Conv2D.
         /// </summary>

src/TensorFlowNET.Core/Graphs/Graph.cs

@@ -283,7 +283,7 @@ public virtual Operation create_op(string op_type, Tensor[] inputs, TF_DataType[
             // This was causing duplicate graph node name errors, when testing a conv2d autoencoder
             // https://keras.io/guides/functional_api/#:~:text=keras.,graph%20(DAG)%20of%20layers.
             // name = name.EndsWith("/") ? ops.name_from_scope_name(name) : unique_name(name);
-            name = name.EndsWith("/") ? unique_name(ops.name_from_scope_name(name)) : unique_name(name);
+            name = name.EndsWith("/") ? ops.name_from_scope_name(name) : unique_name(name);
             var node_def = ops._NodeDef(op_type, name, attrs: attrs);
 
             var input_ops = inputs.Select(x => x.op).ToArray();
@@ -386,10 +386,6 @@ public string name_scope(string name)
         /// to name the operation being created.</returns>
         public string unique_name(string name, bool mark_as_used = true)
         {
-            if (name.EndsWith("basic_r_n_n_cell"))
-            {
-
-            }
             if (!String.IsNullOrEmpty(_name_stack))
                 name = _name_stack + "/" + name;
             // For the sake of checking for names in use, we treat names as case

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

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

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

@@ -36,7 +36,7 @@ public ConvolutionInternal(ConvolutionalArgs args)
             name = args.Name;
         }
 
-        public Tensor Apply(Tensors input, IVariableV1 filters)
+        public Tensor Apply(Tensors input, Tensor filters)
         {
             var filters_rank = filters.shape.ndim;
             var inputs_rank = input.shape.ndim;