Fix CS1572 (XML comment has a param tag, but there is no parameter by…

… that name)

Directory.Build.props

@@ -17,9 +17,8 @@
       Suppress warnings for currently-invalid documentation comments.
 
       CS1570: XML comment has badly formed XML
-      CS1572: XML comment has a param tag, but there is no parameter by that name
     -->
-    <NoWarn>$(NoWarn),1570,1572</NoWarn>
+    <NoWarn>$(NoWarn),1570</NoWarn>
   </PropertyGroup>
 
 </Project>

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

@@ -68,7 +68,7 @@ public IInitializer random_normal_initializer(float mean = 0.0f,
         /// </summary>
         /// <param name="factor"></param>
         /// <param name="mode"></param>
-        /// <param name="distribution"></param>
+        /// <param name="uniform"></param>
         /// <param name="seed"></param>
         /// <param name="dtype"></param>
         /// <returns></returns>

src/TensorFlowNET.Core/Eager/c_api.eager.cs

@@ -18,7 +18,7 @@ public static extern void TFE_RegisterGradientFunction(gradient_function_callbac
         /// <param name="op_name"></param>
         /// <param name="op_inputs"></param>
         /// <param name="op_outputs"></param>
-        /// <param name="num_attrs"></param>
+        /// <param name="attrs_string"></param>
         /// <param name="output_grads">previous node ouput</param>
         /// <param name="skip_input_indices"></param>
         /// <returns></returns>
@@ -355,7 +355,7 @@ public delegate void delete_backward_function_callback(string op_name,
         /// TFE_ExecutorWaitForAllPendingNodes before calling this API if you want to
         /// make sure all nodes are finished.
         /// </summary>
-        /// <param name="e">TFE_Executor*</param>
+        /// <param name="executor">TFE_Executor*</param>
         [DllImport(TensorFlowLibName)]
         public static extern void TFE_DeleteExecutor(IntPtr executor);
 

src/TensorFlowNET.Core/Gradients/nn_grad.cs

@@ -30,7 +30,7 @@ public class nn_grad
         /// Return the gradients for the 2 inputs of bias_op.
         /// </summary>
         /// <param name="op"></param>
-        /// <param name="grad"></param>
+        /// <param name="grads"></param>
         /// <returns></returns>
         [RegisterGradient("BiasAdd")]
         public static Tensor[] _BiasAddGrad(Operation op, Tensor[] grads)
@@ -78,8 +78,7 @@ public static Tensor[] _SoftmaxGrad(Operation op, Tensor[] grads)
         /// Gradient function for SoftmaxCrossEntropyWithLogits.
         /// </summary>
         /// <param name="op"></param>
-        /// <param name="grad_loss"></param>
-        /// <param name="grad_grad"></param>
+        /// <param name="grads"></param>
         /// <returns></returns>
         [RegisterGradient("SoftmaxCrossEntropyWithLogits")]
         public static Tensor[] _SoftmaxCrossEntropyWithLogitsGrad(Operation op, Tensor[] grads)

src/TensorFlowNET.Core/Interfaces/ITensorFlowObject.cs

@@ -23,7 +23,6 @@ public interface ITensorFlowObject : IDisposable
         /// <summary>
         /// Called when the instance is created.
         /// </summary>
-        /// <param name="args"></param>
         void __init__();
 
         void __enter__();

src/TensorFlowNET.Core/Operations/NnOps/gen_nn_ops.cs

@@ -161,15 +161,7 @@ public static Tensor elu(Tensor features, string name = "Elu")
         /// <summary>
         /// Gradient for batch normalization.
         /// </summary>
-        /// <param name="y_backprop"></param>
-        /// <param name="x"></param>
-        /// <param name="scale"></param>
-        /// <param name="reserve_space_1"></param>
-        /// <param name="reserve_space_2"></param>
-        /// <param name="epsilon"></param>
-        /// <param name="data_format"></param>
-        /// <param name="is_training"></param>
-        /// <param name="name"></param>
+        /// <param name="params"></param>
         /// <returns></returns>
         public static Tensor[] fused_batch_norm_grad(FusedBatchNormParams @params)
         {

src/TensorFlowNET.Core/Operations/control_flow_ops.cs

@@ -637,7 +637,7 @@ public static Tensor ZerosLikeOutsideLoop(Operation op, int index)
         /// <param name="cond"></param>
         /// <param name="body"></param>
         /// <param name="loop_vars"></param>
-        /// <param name="i"></param>
+        /// <param name="shape_invariants"></param>
         public static TItem while_loop<TItem>(Func<TItem, Tensor> cond, Func<TItem, TItem> body, TItem loop_vars,
             TensorShape[] shape_invariants = null,
             int parallel_iterations = 10,

src/TensorFlowNET.Core/Operations/random_ops.cs

@@ -158,7 +158,7 @@ public static Tensor multinomial(Tensor logits, int num_samples, int? seed = nul
         /// </summary>
         /// <param name="logits"></param>
         /// <param name="num_samples"></param>
-        /// <param name="output_dtype"></param>
+        /// <param name="dtype"></param>
         /// <param name="seed"></param>
         /// <returns></returns>
         private static Tensor multinomial_categorical_impl(Tensor logits, int num_samples, TF_DataType dtype = TF_DataType.DtInvalid,

src/TensorFlowNET.Core/Operations/resource_variable_ops.cs

@@ -151,7 +151,7 @@ public static Tensor variable_handle_from_shape_and_dtype(TensorShape shape, TF_
         /// Sets the shape inference result HandleData on tensor.
         /// </summary>
         /// <param name="handle"></param>
-        /// <param name="full_handle_data"></param>
+        /// <param name="handle_data"></param>
         /// <param name="graph_mode"></param>
         private static void _set_handle_shapes_and_types(Tensor handle, HandleData handle_data, bool graph_mode)
         {

src/TensorFlowNET.Core/Tensors/c_api.tensor.cs

@@ -103,8 +103,6 @@ public partial class c_api
         /// <param name="num_dims"></param>
         /// <param name="data"></param>
         /// <param name="len">num_bytes, ex: 6 * sizeof(float)</param>
-        /// <param name="deallocator"></param>
-        /// <param name="deallocator_arg"></param>
         [MethodImpl(MethodImplOptions.AggressiveInlining)]
         public static unsafe IntPtr TF_NewTensor(TF_DataType dataType, long[] dims, int num_dims, IntPtr data, ulong len)
         {
@@ -118,8 +116,6 @@ public static unsafe IntPtr TF_NewTensor(TF_DataType dataType, long[] dims, int
         /// <param name="num_dims"></param>
         /// <param name="data"></param>
         /// <param name="len">num_bytes, ex: 6 * sizeof(float)</param>
-        /// <param name="deallocator"></param>
-        /// <param name="deallocator_arg"></param>
         [MethodImpl(MethodImplOptions.AggressiveInlining)]
         public static unsafe IntPtr TF_NewTensor(TF_DataType dataType, long[] dims, int num_dims, void* data, ulong len)
         {

src/TensorFlowNET.Core/Tensors/constant_op.cs

@@ -35,13 +35,13 @@ public class constant_op
         /// <param name="dtype">The type of the elements of the resulting tensor.</param>
         /// <param name="shape">Optional dimensions of resulting tensor.</param>
         /// <param name="name">Optional name for the tensor.</param>
-        /// <param name="verify_shape">Boolean that enables verification of a shape of values.</param>
         /// <returns></returns>
         public static Tensor constant(object value, TF_DataType dtype = TF_DataType.DtInvalid, int[] shape = null, string name = "Const")
         {
             return _constant_impl(value, dtype, shape, name, verify_shape: false, allow_broadcast: true);
         }
 
+        /// <param name="verify_shape">Boolean that enables verification of a shape of values.</param>
         public static Tensor _constant_impl(object value, 
             TF_DataType dtype, 
             TensorShape shape, 

src/TensorFlowNET.Core/Training/GradientDescentOptimizer.cs

@@ -21,6 +21,8 @@ namespace Tensorflow.Train
     /// </summary>
     public class GradientDescentOptimizer : Optimizer
     {
+        private bool _useTensor;
+
         /// <summary>
         /// Construct a new gradient descent optimizer.
         /// </summary>
@@ -35,7 +37,6 @@ public class GradientDescentOptimizer : Optimizer
         /// for changing these values across different invocations of optimizer
         /// functions.
         /// </remarks>
-        private bool _useTensor;
         public GradientDescentOptimizer(float learning_rate, bool use_locking = false, string name = "GradientDescent") 
             : base(learning_rate, use_locking, name)
         {

src/TensorFlowNET.Core/Util/nest.py.cs

@@ -486,13 +486,7 @@ public static object pack_sequence_as(object structure, IEnumerable<object> flat
         ///  and the return value will contain the results in the same structure.
         /// </summary>
         /// <param name="func"> A callable that accepts as many arguments as there are structures.</param>
-        /// <param name="structures">one or many IEnumerable of object</param>
-        /// <param name="check_types">If set to
-        ///      `True` (default) the types of iterables within the structures have to be
-        ///      same (e.g. `map_structure(func, [1], (1,))` raises a `TypeError`
-        ///     exception). To allow this set this argument to `False`.
-        ///      Note that namedtuples with identical name and fields are always
-        ///      considered to have the same shallow structure.</param>
+        /// <param name="structure">one or many IEnumerable of object</param>
         /// <returns>
         ///    A new structure with the same arity as `structure`, whose values correspond
         ///    to `func(x[0], x[1], ...)` where `x[i]` is a value in the corresponding

src/TensorFlowNET.Core/Variables/gen_state_ops.py.cs

@@ -51,7 +51,7 @@ public static Tensor variable_v2(int[] shape, TF_DataType dtype, string name = n
         /// <summary>
         /// Update 'ref' by assigning 'value' to it
         /// </summary>
-        /// <param name="REF"></param>
+        /// <param name="ref"></param>
         /// <param name="value"></param>
         /// <param name="validate_shape"></param>
         /// <param name="use_locking"></param>

src/TensorFlowNET.Core/ops.cs

@@ -313,7 +313,7 @@ public static void _colocate_with_for_gradient(Operation op, string gradient_uid
         /// <summary>
         /// Uses the default session to evaluate one or more tensors.
         /// </summary>
-        /// <param name="tensors">A single Tensor, or a list of Tensor objects.</param>
+        /// <param name="tensor">A single Tensor, or a list of Tensor objects.</param>
         /// <param name="feed_dict">
         /// A dictionary that maps Tensor objects (or tensor names) to lists,
         /// numpy ndarrays, TensorProtos, or strings.