1. fix adamOptimizer no init dtype.

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

@@ -41,6 +41,9 @@ namespace Tensorflow
             public Optimizer AdamOptimizer(float learning_rate, string name = "Adam") 
                 => new AdamOptimizer(learning_rate, name: name);
 
+            public Optimizer AdamOptimizer(float learning_rate, TF_DataType dtype, string name = "Adam")
+                => new AdamOptimizer(learning_rate, name: name, dtype: dtype);
+
             public Optimizer AdamOptimizer(Tensor learning_rate, string name = "Adam")
                 => new AdamOptimizer(learning_rate, name: name);
 

src/TensorFlowNET.Core/Training/AdamOptimizer.cs

@@ -32,21 +32,24 @@ namespace Tensorflow.Train
         float _beta2;
         float _epsilon;
         Tensor _beta1_t, _beta2_t, _epsilon_t;
+        TF_DataType _dtype;
 
-        public AdamOptimizer(float learning_rate, float beta1 = 0.9f, float beta2 = 0.999f, float epsilon = 1e-8f, bool use_locking = false, string name = "Adam")
+        public AdamOptimizer(float learning_rate, float beta1 = 0.9f, float beta2 = 0.999f, float epsilon = 1e-8f, bool use_locking = false, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = "Adam")
             : base(learning_rate, use_locking, name)
         {
             _beta1 = beta1;
             _beta2 = beta2;
             _epsilon = epsilon;
+            _dtype = dtype;
         }
 
-        public AdamOptimizer(Tensor learning_rate, float beta1 = 0.9f, float beta2 = 0.999f, float epsilon = 1e-8f, bool use_locking = false, string name = "Adam")
+        public AdamOptimizer(Tensor learning_rate, float beta1 = 0.9f, float beta2 = 0.999f, float epsilon = 1e-8f, bool use_locking = false, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = "Adam")
             : base(learning_rate, use_locking, name)
         {
             _beta1 = beta1;
             _beta2 = beta2;
             _epsilon = epsilon;
+            _dtype = dtype;
         }
 
         public override Operation _apply_sparse(IndexedSlices grad, RefVariable var)
@@ -154,10 +157,10 @@ namespace Tensorflow.Train
             var beta2 = _call_if_callable(_beta2);
             var epsilon = _call_if_callable(_epsilon);
 
-            _lr_t = _lr_t ?? ops.convert_to_tensor(lr, name: "learning_rate");
-            _beta1_t = _beta1_t ?? ops.convert_to_tensor(beta1, name: "beta1");
-            _beta2_t = _beta2_t ?? ops.convert_to_tensor(beta2, name: "beta2");
-            _epsilon_t = _epsilon_t ?? ops.convert_to_tensor(epsilon, name: "epsilon");
+            _lr_t = _lr_t ?? ops.convert_to_tensor(lr, name: "learning_rate", dtype: _dtype);
+            _beta1_t = _beta1_t ?? ops.convert_to_tensor(beta1, name: "beta1", dtype: _dtype);
+            _beta2_t = _beta2_t ?? ops.convert_to_tensor(beta2, name: "beta2", dtype: _dtype);
+            _epsilon_t = _epsilon_t ?? ops.convert_to_tensor(epsilon, name: "epsilon", dtype: _dtype);
         }
     }
 }

src/TensorFlowNET.Core/Training/Optimizer.cs

@@ -253,6 +253,7 @@ namespace Tensorflow
                 v = variable_scope.default_variable_creator(
                     initial_value, 
                     name: name, 
+                    dtype: colocate_with.dtype.as_base_dtype(),
                     trainable: false,
                     use_resource: resource_variable_ops.is_resource_variable(
                         colocate_with));