Merge pull request #1136 from Beacontownfc/mybranch

Add a new optimizer

src/TensorFlowNET.Core/Keras/IOptimizerApi.cs

@@ -25,6 +25,27 @@ namespace Tensorflow.Keras
                 bool amsgrad = false,
                 string name = "Adam");
 
+        /// <summary>
+        /// Adam enables L2 weight decay on gradients.
+        /// </summary>
+        /// <param name="learning_rate"></param>
+        /// <param name="weight_decay"></param>
+        /// <param name="beta_1"></param>
+        /// <param name="beta_2"></param>
+        /// <param name="epsilon"></param>
+        /// <param name="amsgrad"></param>
+        /// <param name="decay_params"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        IOptimizer AdamW(float learning_rate = 0.001f,
+                float weight_decay = 0.004f,
+                float beta_1 = 0.9f,
+                float beta_2 = 0.999f,
+                float epsilon = 1e-7f,
+                bool amsgrad = false,
+                List<string> no_decay_params = null,
+                string name = "AdamW");
+
         /// <summary>
         /// Construct a new RMSprop optimizer.
         /// </summary>

src/TensorFlowNET.Keras/Optimizers/AdamW.cs

@@ -0,0 +1,64 @@
+namespace Tensorflow.Keras.Optimizers
+{
+    public class AdamW : Adam
+    {
+        string name;
+        float weight_decay;
+        DeviceDType deType;
+        List<string> no_decay_params = null;
+        public AdamW(float learning_rate= 0.001f,
+                     float weight_decay= 0.004f,
+                     float beta_1= 0.9f,
+                     float beta_2= 0.999f,
+                     float epsilon= 1e-7f,
+                     bool amsgrad = false,
+                     List<string> no_decay_params = null,
+                     string name= "AdamW") : base(learning_rate, beta_1, beta_2, epsilon, amsgrad)
+        {
+            this.name = name;
+            this.weight_decay = weight_decay;
+            this.no_decay_params = no_decay_params;
+        }
+
+        protected Operation _decay_weights_op(IVariableV1 var, float learning_rate, Dictionary<DeviceDType, Dictionary<string, Tensor>> apply_state)
+        {
+            bool do_decay = _do_use_weight_decay(var.Name);
+            if (do_decay) return var.assign_add(
+                -learning_rate * var.AsTensor() * apply_state[deType]["weight_decay"]);
+            return tf.no_op();
+        }
+
+
+        protected bool _do_use_weight_decay(string param_name)
+        {
+            // Whether to use L2 weight decay for `param_name`.
+            if (this.weight_decay == 0)
+                return false;
+
+            if (this.no_decay_params != null)
+            {
+                foreach (var name in no_decay_params)
+                {
+                    if (param_name.Contains(name)) return false;
+                }
+
+            }
+            return true;
+        }
+
+        protected override Operation _resource_apply_dense(IVariableV1 var, Tensor grad, Dictionary<DeviceDType, Dictionary<string, Tensor>> apply_state)
+        {
+            var decay = _decay_weights_op(var, _hyper["learning_rate"], apply_state);
+            tf.control_dependencies(new[] { decay });
+            return base._resource_apply_dense(var, grad, apply_state);
+        }
+
+        protected override void _prepare_local(DeviceDType device_dtype, Dictionary<DeviceDType, Dictionary<string, Tensor>> apply_state)
+        {
+            this.deType = device_dtype;
+            base._prepare_local(device_dtype, apply_state);
+            apply_state[device_dtype]["weight_decay"] = tf.constant(
+                weight_decay, name: "adam_weight_decay_rate");
+        }
+    }
+}

src/TensorFlowNET.Keras/Optimizers/OptimizerApi.cs

@@ -29,6 +29,22 @@ namespace Tensorflow.Keras.Optimizers
                 amsgrad: amsgrad,
                 name: name);
 
+        public IOptimizer AdamW(float learning_rate = 0.001f,
+                float weight_decay = 0.004f,
+                float beta_1 = 0.9f,
+                float beta_2 = 0.999f,
+                float epsilon = 1e-7f,
+                bool amsgrad = false,
+                List<string> no_decay_params = null,
+                string name = "AdamW") => new AdamW(learning_rate: learning_rate,
+                    beta_1: beta_1,
+                    beta_2: beta_2,
+                    epsilon: epsilon,
+                    amsgrad: amsgrad,
+                    name: name,
+                    weight_decay: weight_decay,
+                    no_decay_params: no_decay_params);
+
         /// <summary>
         /// Construct a new RMSprop optimizer.
         /// </summary>