Merge pull request #1202 from Wanglongzhi2001/master

fix: add the implementation of the tile's and GatherND's grad and add OptionalArgs

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

@@ -140,6 +140,16 @@ public Tensor identity(Tensor input, string name = null)
         public Tensor gather(Tensor @params, Tensor indices, string name = null, int axis = 0)
             => array_ops.gather(@params, indices, name: name, axis: ops.convert_to_tensor(axis));
 
+        /// <summary>
+        /// Gather slices from `params` into a Tensor with shape specified by `indices`.
+        /// </summary>
+        /// <param name="params"></param>
+        /// <param name="indices"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public Tensor gather_nd(Tensor @params, Tensor indices, string name = null)
+            => gen_array_ops.gather_nd(@params, indices, name: name);
+
         /// <summary>
         /// Return the elements, either from `x` or `y`, depending on the `condition`.
         /// </summary>

src/TensorFlowNET.Core/Gradients/array_grad.cs

@@ -381,5 +381,48 @@ public static Tensor[] _ReverseV2Grad(Operation op, Tensor[] grads)
             var axis = op.inputs[1];
             return new Tensor[] { array_ops.reverse(grad, axis), null };
         }
+
+        [RegisterGradient("Tile")]
+        public static Tensor[] _TileGrad(Operation op, Tensor[] grads)
+        {
+            var grad = grads[0];
+            var input_shape = array_ops.shape(op.inputs[0], out_type: op.inputs[1].dtype);
+            var split_shape = array_ops.reshape(array_ops.transpose(array_ops.stack(new Tensor[] { op.inputs[1], input_shape })), new Shape(-1));
+            var axes = math_ops.range(0, array_ops.size(split_shape), 2);
+
+            //# Sum reduces grad along the first dimension for IndexedSlices
+            //if isinstance(grad, indexed_slices_lib.IndexedSlices):
+            //input_shape_0 = math_ops.cast(input_shape[0], grad.indices.dtype)
+            //grad = math_ops.unsorted_segment_sum(
+            //    grad.values, math_ops.mod(grad.indices, input_shape_0), input_shape_0)
+            //split_shape = array_ops.concat([[1], split_shape[1:]], axis = 0)
+
+            var input_grad = math_ops.reduce_sum(array_ops.reshape(grad, split_shape), axes);
+            if (!tf.Context.executing_eagerly())
+            {
+                input_grad.set_shape(op.inputs[0].GetShape());
+            }
+            return new Tensor[] { input_grad, null };
+        }
+
+        [RegisterGradient("GatherNd")]
+        public static Tensor[] _GatherNdGrad(Operation op, Tensor[] grads)
+        {
+            var @ref = op.inputs[0];
+            var indices = op.inputs[1];
+            var grad = grads[0];
+            var ref_shape = array_ops.shape(@ref, out_type: indices.dtype);
+            Tensor ref_grad = null;
+            if (indices.shape.ndim == 2 && indices.shape.dims[indices.shape.Length - 1] == 1)
+            {
+                ref_grad = (Tensor)new IndexedSlices(grad, array_ops.squeeze(indices, axis: -1), ref_shape);
+            }
+            else
+            {
+                ref_grad = gen_array_ops.scatter_nd(indices, grad, ref_shape);
+            }
+            return new Tensor[] { ref_grad, null };
+        }
+
     }
 }

src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/GRUOptionalArgs.cs

@@ -4,10 +4,8 @@
 
 namespace Tensorflow.Keras.ArgsDefinition
 {
-    public class GRUOptionalArgs
+    public class GRUOptionalArgs : RnnOptionalArgs
     {
         public string Identifier => "GRU";
-
-        public Tensor Mask { get; set; } = null;
     }
 }

src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/LSTMOptionalArgs.cs

@@ -0,0 +1,11 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.ArgsDefinition.Rnn
+{
+    public class LSTMOptionalArgs : RnnOptionalArgs
+    {
+        public string Identifier => "LSTM";
+    }
+}

src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/SimpleRNNOptionalArgs.cs

@@ -0,0 +1,11 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.ArgsDefinition.Rnn
+{
+    public class SimpleRNNOptionalArgs : RnnOptionalArgs
+    {
+        public string Identifier => "SimpleRNN";
+    }
+}

src/TensorFlowNET.Core/Operations/array_ops.cs

@@ -829,7 +829,7 @@ public static Tensor stop_gradient(Tensor input, string name = null)
         /// <returns>A `Tensor`. Has the same type as `input`.
         /// Contains the same data as `input`, but has one or more dimensions of
         /// size 1 removed.</returns>
-        public static Tensor squeeze(Tensor input, int[] axis = null, string name = null)
+        public static Tensor squeeze(Tensor input, Axis axis = null, string name = null)
             => gen_array_ops.squeeze(input, axis, name);
 
         public static Tensor identity(Tensor input, string name = null)
@@ -990,7 +990,7 @@ public static Tensor gather(ResourceVariable @params, Tensor indices, string nam
             return @params.sparse_read(indices, name);
         }
 
-        public static Tensor transpose<T1>(T1 a, Axis perm, string name = "transpose", bool conjugate = false)
+        public static Tensor transpose<T1>(T1 a, Axis perm = null, string name = "transpose", bool conjugate = false)
         {
             return tf_with(ops.name_scope(name, "transpose", new { a }), scope =>
             {

test/TensorFlowNET.UnitTest/GradientTest/GradientEagerTest.cs

@@ -62,7 +62,7 @@ public void SquaredDifference_1D()
             // Calcute the gradient of (x1-x2)^2 
             // by Automatic Differentiation in Eager mode
             // Expected is 2*(abs(x1-x2))
-            Tensor x1 = new NDArray( new float[] { 1, 3, 5, 21, 19, 17 });
+            Tensor x1 = new NDArray(new float[] { 1, 3, 5, 21, 19, 17 });
             Tensor x2 = new NDArray(new float[] { 29, 27, 23, 7, 11, 13 });
             float[] expected = new float[]
             {
@@ -173,5 +173,34 @@ public void ConditionalMultiply()
             var result = grad(x, 4);
             Assert.AreEqual((float)result, 4.0f);
         }
+
+        [TestMethod]
+        public void Tile()
+        {
+            var a = tf.constant(new int[] { 1 }, TF_DataType.TF_FLOAT);
+            var b = tf.constant(new int[] { 2 });
+            using (var tape = tf.GradientTape())
+            {
+                tape.watch(a);
+                var y = tf.tile(a, b);
+                var grad = tape.gradient(y, a);
+                Assert.AreEqual((float)grad.numpy(), 2.0f);
+            }
+        }
+
+        [TestMethod]
+        public void GatherNdTest()
+        {
+            var x = tf.constant(new float[,] { { 1.0f, 2.0f, 3.0f }, { 1.0f, 2.0f, 3.0f }, { 1.0f, 2.0f, 3.0f } }, dtype: TF_DataType.TF_FLOAT);
+            var indices = tf.constant(new int[,] { { 0, 1 }, { 1, 1 }, { 2, 1 } }, dtype: TF_DataType.TF_INT32);
+            using (var tape = tf.GradientTape())
+            {
+                tape.watch(x);
+                var res = tf.gather_nd(x, indices);
+                var grad = tape.gradient(res, x);
+                var expected = np.array(new float[,] { { 0f, 1f, 0f }, { 0f, 1f, 0f }, { 0f, 1f, 0f } });
+                Assert.IsTrue(Enumerable.SequenceEqual(grad.ToArray<float>(), expected.ToArray<float>()));
+            }
+        }
     }
 }