add tf.clip_by_global_norm #857

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

@@ -27,6 +27,9 @@ namespace Tensorflow
         public void add_to_collections<T>(List<string> names, T value)
             => get_default_graph().add_to_collections(names, value);
 
+        public (Tensors, Tensor) clip_by_global_norm(Tensor[] t_list, float clip_norm, Tensor use_norm = null, string name = null)
+            => clip_ops.clip_by_global_norm(t_list, clip_norm, use_norm: use_norm, name: name);
+
         public Tensor assign(IVariableV1 @ref, object value, bool validate_shape = true, bool use_locking = true, string name = null)
             => state_ops.assign(@ref, value, validate_shape, use_locking, name);
 

src/TensorFlowNET.Core/Operations/clip_ops.cs

@@ -21,6 +21,27 @@ namespace Tensorflow
 {
     public class clip_ops
     {
+        public static (Tensors, Tensor) clip_by_global_norm(Tensor[] t_list, float clip_norm, Tensor use_norm = null, string name = null)
+        {
+            use_norm = global_norm(t_list, name);
+            return tf_with(ops.name_scope(name, "clip_by_global_norm", t_list), delegate
+            {
+                // Calculate L2-norm, clip elements by ratio of clip_norm to L2-norm
+                var scale_for_finite = clip_norm * math_ops.minimum(
+                    1.0f / use_norm,
+                    constant_op.constant(1.0, dtype: use_norm.dtype) / clip_norm);
+
+                // If use_norm is any finite number, this is a no-op. For inf/-inf/NaN,
+                // this will make scale NaN.
+                var scale = scale_for_finite + (use_norm - use_norm);
+
+                Tensors values_clipped = new Tensors();
+                foreach (var (i, v) in enumerate(t_list))
+                    values_clipped.Add(array_ops.identity(v * scale, name: $"{name}_{i}"));
+                return (values_clipped, use_norm);
+            });
+        }
+
         public static Tensor clip_by_value<T1, T2>(Tensor t, T1 clip_value_min, T2 clip_value_max, string name = null)
         {
             return tf_with(ops.name_scope(name, "clip_by_value", new { t, clip_value_min, clip_value_max }), delegate

test/TensorFlowNET.UnitTest/ManagedAPI/ClipTest.cs

@@ -0,0 +1,21 @@
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using static Tensorflow.Binding;
+using Tensorflow;
+
+namespace TensorFlowNET.UnitTest.ClipOps
+{
+    [TestClass]
+    public class ClipTest : EagerModeTestBase
+    {
+        [TestMethod]
+        public void clip_by_global_norm()
+        {
+            var t_list = new Tensors(tf.constant(new float[] { 1, 2, 3, 4 }), tf.constant(new float[] { 5, 6, 7, 8 }));
+            var clip_norm = .8f;
+            var (res, norm) = tf.clip_by_global_norm(t_list, clip_norm);
+            Equal(res[0].ToArray<float>(), new[] { 0.0560112074f, 0.112022415f, 0.16803363f, 0.22404483f });
+            Equal(res[1].ToArray<float>(), new[] { 0.28005603f, 0.336067259f, 0.392078459f, 0.448089659f });
+            Assert.AreEqual(norm.numpy(), 14.282857f);
+        }
+    }
+}