fix: add the momentum parameter's implemention of SGD

src/TensorFlowNET.Core/Keras/IOptimizerApi.cs

@@ -63,6 +63,6 @@ namespace Tensorflow.Keras
                 bool centered = false,
                 string name = "RMSprop");
 
-        IOptimizer SGD(float learning_rate);
+        IOptimizer SGD(float learning_rate, float momentum);
     }
 }

src/TensorFlowNET.Core/Training/gen_training_ops.cs

@@ -51,5 +51,9 @@ namespace Tensorflow
         public static Tensor resource_apply_gradient_descent(Tensor var, Tensor alpha, Tensor delta, bool use_locking = false, string name = null)
             => tf.Context.ExecuteOp("ResourceApplyGradientDescent", name, 
                 new ExecuteOpArgs(var, alpha, delta).SetAttributes(new { use_locking }));
+
+        public static Tensor resource_apply_keras_momentum(Tensor var, Tensor accum, Tensor lr, Tensor grad, Tensor momentum, bool use_locking = false, bool use_nesterov = false, string name = null)
+            => tf.Context.ExecuteOp("ResourceApplyKerasMomentum", name, 
+                new ExecuteOpArgs(var, accum, lr, grad, momentum).SetAttributes(new { use_locking, use_nesterov }));
     }
 }

src/TensorFlowNET.Keras/Optimizers/OptimizerApi.cs

@@ -71,7 +71,7 @@ namespace Tensorflow.Keras.Optimizers
                 Name = name
             });
 
-        public IOptimizer SGD(float learning_rate)
-            => new SGD(learning_rate);
+        public IOptimizer SGD(float learning_rate, float momentum)
+            => new SGD(learning_rate, momentum);
     }
 }

src/TensorFlowNET.Keras/Optimizers/SGD.cs

@@ -22,6 +22,8 @@ namespace Tensorflow.Keras.Optimizers
             _set_hyper("decay", decay);
 
             _momentum = momentum > 0;
+            if (momentum < 0 || momentum > 1)
+                throw new ValueError($"momentum must be a number between 0 and 1, got {momentum}.");
 
             _set_hyper("momentum", momentum);
 
@@ -30,6 +32,13 @@ namespace Tensorflow.Keras.Optimizers
 #pragma warning restore CS1717 // Assignment made to same variable
         }
 
+        protected override void _create_slots(IVariableV1[] var_list)
+        {
+            if (_momentum)
+                foreach (var var in var_list)
+                    add_slot(var, "momentum");
+        }
+
         protected override void _prepare_local(DeviceDType device_dtype,
             Dictionary<DeviceDType, Dictionary<string, Tensor>> _apply_state)
         {
@@ -43,7 +52,15 @@ namespace Tensorflow.Keras.Optimizers
         {
             if (_momentum)
             {
-                throw new NotImplementedException("_resource_apply_dense");
+                var momentum_var = get_slot(var, "momentum");
+                return gen_training_ops.resource_apply_keras_momentum(
+                    var.Handle,
+                    momentum_var.Handle,
+                    _get_hyper("learning_rate", var.dtype),
+                    grad,
+                    _get_hyper("momentum", var.dtype),
+                    use_locking: _use_locking,
+                    use_nesterov: nesterov);
             }
             var device_dtype = _apply_state.Keys.FirstOrDefault(x => x.Device == var.Device && x.DType == var.dtype.as_base_dtype());