diff --git a/src/TensorFlowNET.Core/Keras/Activations/Activations.cs b/src/TensorFlowNET.Core/Keras/Activations/Activations.cs
index ea01b319..e24e42ff 100644
--- a/src/TensorFlowNET.Core/Keras/Activations/Activations.cs
+++ b/src/TensorFlowNET.Core/Keras/Activations/Activations.cs
@@ -28,10 +28,10 @@ namespace Tensorflow.Keras
     }
 
     /// <summary>
-    /// The ActivationAdaptor is used to store string, Activation, and Func for Laysers Api to accept different types of activation parameters.
+    /// The ActivationAdapter is used to store string, Activation, and Func for Laysers Api to accept different types of activation parameters.
     /// One of the properties must be specified while initializing.
     /// </summary>
-    public class ActivationAdaptor
+    public class ActivationAdapter
     {
         /// <summary>
         /// The name of activaiton function, such as `tanh`, `sigmoid`.
@@ -48,34 +48,34 @@ namespace Tensorflow.Keras
         /// </summary>
         public Func<Tensor, string, Tensor>? Func { get; set; } = null;
 
-        public ActivationAdaptor(string name)
+        public ActivationAdapter(string name)
         {
             Name = name;
         }
 
-        public ActivationAdaptor(Activation activation)
+        public ActivationAdapter(Activation activation)
         {
             Activation = activation;
         }
 
-        public ActivationAdaptor(Func<Tensor, string, Tensor> func)
+        public ActivationAdapter(Func<Tensor, string, Tensor> func)
         {
             Func = func;
         }
 
-        public static implicit operator ActivationAdaptor(string name)
+        public static implicit operator ActivationAdapter(string name)
         {
-            return new ActivationAdaptor(name);
+            return new ActivationAdapter(name);
         }
 
-        public static implicit operator ActivationAdaptor(Activation activation)
+        public static implicit operator ActivationAdapter(Activation activation)
         {
-            return new ActivationAdaptor(activation);
+            return new ActivationAdapter(activation);
         }
 
-        public static implicit operator ActivationAdaptor(Func<Tensor, string, Tensor> func)
+        public static implicit operator ActivationAdapter(Func<Tensor, string, Tensor> func)
         {
-            return new ActivationAdaptor(func);
+            return new ActivationAdapter(func);
         }
     }
 
@@ -84,7 +84,7 @@ namespace Tensorflow.Keras
     {
         Activation GetActivationFromName(string name);
         
-        Activation GetActivationFromAdaptor(ActivationAdaptor adaptor);
+        Activation GetActivationFromAdapter(ActivationAdapter adapter);
 
         Activation Linear { get; }
 
diff --git a/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.cs b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.cs
index abeac4e5..b17f635b 100644
--- a/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.cs
+++ b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.cs
@@ -48,7 +48,7 @@ namespace Tensorflow.Keras.Layers
             string data_format = "channels_last",
             int dilation_rate = 1,
             int groups = 1,
-            ActivationAdaptor activation = null,
+            ActivationAdapter activation = null,
             bool use_bias = true,
             string kernel_initializer = "glorot_uniform",
             string bias_initializer = "zeros");
@@ -71,7 +71,7 @@ namespace Tensorflow.Keras.Layers
             string data_format = null,
             Shape dilation_rate = null,
             int groups = 1,
-            ActivationAdaptor activation = null,
+            ActivationAdapter activation = null,
             bool use_bias = true,
             IInitializer kernel_initializer = null,
             IInitializer bias_initializer = null,
@@ -99,7 +99,7 @@ namespace Tensorflow.Keras.Layers
             string output_padding = "valid",
             string data_format = null,
             Shape dilation_rate = null,
-            ActivationAdaptor activation = null,
+            ActivationAdapter activation = null,
             bool use_bias = true,
             string kernel_initializer = null,
             string bias_initializer = null,
@@ -121,7 +121,7 @@ namespace Tensorflow.Keras.Layers
             string activity_regularizer = null);
 
         public ILayer Dense(int units,
-            ActivationAdaptor activation = null,
+            ActivationAdapter activation = null,
             IInitializer kernel_initializer = null,
             bool use_bias = true,
             IInitializer bias_initializer = null,
@@ -155,7 +155,7 @@ namespace Tensorflow.Keras.Layers
         public ILayer EinsumDense(string equation,
                 Shape output_shape,
                 string bias_axes,
-                ActivationAdaptor activation = null,
+                ActivationAdapter activation = null,
                 IInitializer kernel_initializer = null,
                 IInitializer bias_initializer = null,
                 IRegularizer kernel_regularizer = null,
@@ -209,8 +209,8 @@ namespace Tensorflow.Keras.Layers
         public ILayer LeakyReLU(float alpha = 0.3f);
 
         public ILayer LSTM(int units,
-            ActivationAdaptor activation = null,
-            ActivationAdaptor recurrent_activation = null,
+            ActivationAdapter activation = null,
+            ActivationAdapter recurrent_activation = null,
             bool use_bias = true,
             IInitializer kernel_initializer = null,
             IInitializer recurrent_initializer = null,
@@ -259,7 +259,7 @@ namespace Tensorflow.Keras.Layers
             Shape input_shape = null);
 
         public ILayer SimpleRNN(int units,
-            ActivationAdaptor activation = null,
+            ActivationAdapter activation = null,
             string kernel_initializer = "glorot_uniform",
             string recurrent_initializer = "orthogonal",
             string bias_initializer = "zeros",
diff --git a/src/TensorFlowNET.Keras/Activations.cs b/src/TensorFlowNET.Keras/Activations.cs
index 4d08c77e..89988667 100644
--- a/src/TensorFlowNET.Keras/Activations.cs
+++ b/src/TensorFlowNET.Keras/Activations.cs
@@ -94,37 +94,37 @@ namespace Tensorflow.Keras
         }
 
         /// <summary>
-        /// Convert ActivationAdaptor to Activation.
-        /// If more than one properties of ActivationAdaptor are specified, the order of priority is `Name`, `Activation`, `Func`
+        /// Convert ActivationAdapter to Activation.
+        /// If more than one properties of ActivationAdapter are specified, the order of priority is `Name`, `Activation`, `Func`
         /// </summary>
-        /// <param name="adaptor"></param>
+        /// <param name="adapter"></param>
         /// <returns></returns>
         /// <exception cref="Exception"></exception>
-        public Activation GetActivationFromAdaptor(ActivationAdaptor adaptor)
+        public Activation GetActivationFromAdapter(ActivationAdapter adapter)
         {
-            if(adaptor == null)
+            if(adapter == null)
             {
                 return _linear;
             }
-            if(adaptor.Name != null)
+            if(adapter.Name != null)
             {
-                return GetActivationFromName(adaptor.Name);
+                return GetActivationFromName(adapter.Name);
             }
-            else if(adaptor.Activation != null)
+            else if(adapter.Activation != null)
             {
-                return adaptor.Activation;
+                return adapter.Activation;
             }
-            else if(adaptor.Func != null)
+            else if(adapter.Func != null)
             {
                 return new Activation()
                 {
-                    Name = adaptor.Func.GetMethodInfo().Name,
-                    ActivationFunction = adaptor.Func
+                    Name = adapter.Func.GetMethodInfo().Name,
+                    ActivationFunction = adapter.Func
                 };
             }
             else
             {
-                throw new Exception("Could not interpret activation adaptor");
+                throw new Exception("Could not interpret activation adapter");
             }
         }
     }
diff --git a/src/TensorFlowNET.Keras/Layers/LayersApi.cs b/src/TensorFlowNET.Keras/Layers/LayersApi.cs
index 776feb48..9349f50f 100644
--- a/src/TensorFlowNET.Keras/Layers/LayersApi.cs
+++ b/src/TensorFlowNET.Keras/Layers/LayersApi.cs
@@ -94,7 +94,7 @@ namespace Tensorflow.Keras.Layers
             string data_format = "channels_last",
             int dilation_rate = 1,
             int groups = 1,
-            ActivationAdaptor activation = null,
+            ActivationAdapter activation = null,
             bool use_bias = true,
             string kernel_initializer = "glorot_uniform",
             string bias_initializer = "zeros")
@@ -109,7 +109,7 @@ namespace Tensorflow.Keras.Layers
                 DilationRate = dilation_rate,
                 Groups = groups,
                 UseBias = use_bias,
-                Activation = keras.activations.GetActivationFromAdaptor(activation),
+                Activation = keras.activations.GetActivationFromAdapter(activation),
                 KernelInitializer = GetInitializerByName(kernel_initializer),
                 BiasInitializer = GetInitializerByName(bias_initializer)
             });
@@ -167,7 +167,7 @@ namespace Tensorflow.Keras.Layers
             string data_format = null,
             Shape dilation_rate = null,
             int groups = 1,
-            ActivationAdaptor activation = null,
+            ActivationAdapter activation = null,
             bool use_bias = true,
             IInitializer kernel_initializer = null,
             IInitializer bias_initializer = null,
@@ -190,7 +190,7 @@ namespace Tensorflow.Keras.Layers
                     BiasInitializer = bias_initializer == null ? tf.zeros_initializer : bias_initializer,
                     BiasRegularizer = bias_regularizer,
                     ActivityRegularizer = activity_regularizer,
-                    Activation = keras.activations.GetActivationFromAdaptor(activation),
+                    Activation = keras.activations.GetActivationFromAdapter(activation),
                 });
         public ILayer Conv2D(int filters,
             Shape kernel_size = null,
@@ -248,7 +248,7 @@ namespace Tensorflow.Keras.Layers
             string output_padding = "valid",
             string data_format = null,
             Shape dilation_rate = null,
-            ActivationAdaptor activation = null,
+            ActivationAdapter activation = null,
             bool use_bias = true,
             string kernel_initializer = null,
             string bias_initializer = null,
@@ -267,7 +267,7 @@ namespace Tensorflow.Keras.Layers
                     UseBias = use_bias,
                     KernelInitializer = GetInitializerByName(kernel_initializer),
                     BiasInitializer = GetInitializerByName(bias_initializer),
-                    Activation = keras.activations.GetActivationFromAdaptor(activation)
+                    Activation = keras.activations.GetActivationFromAdapter(activation)
                 });
         public ILayer Conv2DTranspose(int filters,
             Shape kernel_size = null,
@@ -317,7 +317,7 @@ namespace Tensorflow.Keras.Layers
         /// <param name="bias_constraint">Constraint function for the bias.</param>
         /// <returns>N-D tensor with shape: (batch_size, ..., units). For instance, for a 2D input with shape (batch_size, input_dim), the output would have shape (batch_size, units).</returns>
         public ILayer Dense(int units,
-            ActivationAdaptor activation = null,
+            ActivationAdapter activation = null,
             IInitializer kernel_initializer = null,
             bool use_bias = true,
             IInitializer bias_initializer = null,
@@ -330,7 +330,7 @@ namespace Tensorflow.Keras.Layers
             => new Dense(new DenseArgs
             {
                 Units = units,
-                Activation = keras.activations.GetActivationFromAdaptor(activation),
+                Activation = keras.activations.GetActivationFromAdapter(activation),
                 KernelInitializer = kernel_initializer ?? tf.glorot_uniform_initializer,
                 BiasInitializer = bias_initializer ?? (use_bias ? tf.zeros_initializer : null),
                 InputShape = input_shape,
@@ -386,7 +386,7 @@ namespace Tensorflow.Keras.Layers
         /// <returns></returns>
         public Tensor dense(Tensor inputs,
             int units,
-            ActivationAdaptor activation = null,
+            ActivationAdapter activation = null,
             bool use_bias = true,
             IInitializer kernel_initializer = null,
             IInitializer bias_initializer = null,
@@ -405,7 +405,7 @@ namespace Tensorflow.Keras.Layers
             var layer = new Dense(new DenseArgs
             {
                 Units = units,
-                Activation = keras.activations.GetActivationFromAdaptor(activation),
+                Activation = keras.activations.GetActivationFromAdapter(activation),
                 UseBias = use_bias,
                 BiasInitializer = bias_initializer,
                 KernelInitializer = kernel_initializer,
@@ -460,7 +460,7 @@ namespace Tensorflow.Keras.Layers
         public ILayer EinsumDense(string equation,
                 Shape output_shape,
                 string bias_axes,
-                ActivationAdaptor activation = null,
+                ActivationAdapter activation = null,
                 IInitializer kernel_initializer= null,
                 IInitializer bias_initializer= null,
                 IRegularizer kernel_regularizer= null,
@@ -473,7 +473,7 @@ namespace Tensorflow.Keras.Layers
                 Equation = equation,
                 OutputShape = output_shape,
                 BiasAxes = bias_axes,
-                Activation = keras.activations.GetActivationFromAdaptor(activation),
+                Activation = keras.activations.GetActivationFromAdapter(activation),
                 KernelInitializer = kernel_initializer ?? tf.glorot_uniform_initializer,
                 BiasInitializer = bias_initializer ?? tf.zeros_initializer,
                 KernelRegularizer = kernel_regularizer,
@@ -807,7 +807,7 @@ namespace Tensorflow.Keras.Layers
         /// <param name="activation">The name of the activation function to use. Default: hyperbolic tangent (tanh)..</param>
         /// <returns></returns>
         public ILayer SimpleRNN(int units,
-            ActivationAdaptor activation = null,
+            ActivationAdapter activation = null,
             string kernel_initializer = "glorot_uniform",
             string recurrent_initializer = "orthogonal",
             string bias_initializer = "zeros",
@@ -816,7 +816,7 @@ namespace Tensorflow.Keras.Layers
                 => new SimpleRNN(new SimpleRNNArgs
                 {
                     Units = units,
-                    Activation = activation == null ? keras.activations.GetActivationFromAdaptor(activation): keras.activations.Tanh,
+                    Activation = activation == null ? keras.activations.GetActivationFromAdapter(activation): keras.activations.Tanh,
                     KernelInitializer = GetInitializerByName(kernel_initializer),
                     RecurrentInitializer = GetInitializerByName(recurrent_initializer),
                     BiasInitializer = GetInitializerByName(bias_initializer),
@@ -869,8 +869,8 @@ namespace Tensorflow.Keras.Layers
         /// </param>
         /// <returns></returns>
         public ILayer LSTM(int units,
-            ActivationAdaptor activation = null,
-            ActivationAdaptor recurrent_activation = null,
+            ActivationAdapter activation = null,
+            ActivationAdapter recurrent_activation = null,
             bool use_bias = true,
             IInitializer kernel_initializer = null,
             IInitializer recurrent_initializer = null,
@@ -888,8 +888,8 @@ namespace Tensorflow.Keras.Layers
                 => new LSTM(new LSTMArgs
                 {
                     Units = units,
-                    Activation = activation == null ? keras.activations.GetActivationFromAdaptor(activation) : keras.activations.Tanh,
-                    RecurrentActivation = recurrent_activation == null ? keras.activations.GetActivationFromAdaptor(activation) : keras.activations.Sigmoid,
+                    Activation = activation == null ? keras.activations.GetActivationFromAdapter(activation) : keras.activations.Tanh,
+                    RecurrentActivation = recurrent_activation == null ? keras.activations.GetActivationFromAdapter(activation) : keras.activations.Sigmoid,
                     KernelInitializer = kernel_initializer ?? tf.glorot_uniform_initializer,
                     RecurrentInitializer = recurrent_initializer ?? tf.orthogonal_initializer,
                     BiasInitializer = bias_initializer ?? tf.zeros_initializer,