tf.max, tf.min

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

@@ -105,6 +105,39 @@ namespace Tensorflow
         public static Tensor greater_equal<Tx, Ty>(Tx x, Ty y, string name = null)
             => gen_math_ops.greater_equal(x, y, name);
 
+        /// <summary>
+        /// Returns the truth value of (x < y) element-wise.
+        /// </summary>
+        /// <typeparam name="Tx"></typeparam>
+        /// <typeparam name="Ty"></typeparam>
+        /// <param name="x"></param>
+        /// <param name="y"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static Tensor less<Tx, Ty>(Tx x, Ty y, string name = null)
+            => gen_math_ops.less(x, y, name);
+
+        /// <summary>
+        /// Returns the truth value of (x <= y) element-wise.
+        /// </summary>
+        /// <typeparam name="Tx"></typeparam>
+        /// <typeparam name="Ty"></typeparam>
+        /// <param name="x"></param>
+        /// <param name="y"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static Tensor less_equal<Tx, Ty>(Tx x, Ty y, string name = null)
+            => gen_math_ops.less_equal(x, y, name);
+
+        /// <summary>
+        /// Computes natural logarithm of (1 + x) element-wise.
+        /// </summary>
+        /// <param name="x"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static Tensor log1p(Tensor x, string name = null)
+            => gen_math_ops.log1p(x, name);
+
         /// <summary>
         /// Clips tensor values to a specified min and max.
         /// </summary>
@@ -141,6 +174,56 @@ namespace Tensorflow
         public static Tensor atan2(Tensor y, Tensor x, string name = null)
             => gen_math_ops.atan2(y, x, name);
 
+        /// <summary>
+        /// Computes the maximum of elements across dimensions of a tensor.
+        /// </summary>
+        /// <typeparam name="Tx"></typeparam>
+        /// <typeparam name="Ty"></typeparam>
+        /// <param name="input"></param>
+        /// <param name="axis"></param>
+        /// <param name="keep_dims"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static Tensor max<Tx, Ty>(Tx input, Ty axis, bool keep_dims = false, string name = null)
+            => gen_math_ops._max(input, axis, keep_dims: keep_dims, name: name);
+
+        /// <summary>
+        /// Computes the minimum of elements across dimensions of a tensor.
+        /// </summary>
+        /// <typeparam name="Tx"></typeparam>
+        /// <typeparam name="Ty"></typeparam>
+        /// <param name="input"></param>
+        /// <param name="axis"></param>
+        /// <param name="keep_dims"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static Tensor min<Tx, Ty>(Tx input, Ty axis, bool keep_dims = false, string name = null)
+            => gen_math_ops._min(input, axis, keep_dims: keep_dims, name: name);
+
+        /// <summary>
+        /// Returns the max of x and y (i.e. x > y ? x : y) element-wise.
+        /// </summary>
+        /// <typeparam name="T1"></typeparam>
+        /// <typeparam name="T2"></typeparam>
+        /// <param name="x"></param>
+        /// <param name="y"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static Tensor maximum<T1, T2>(T1 x, T2 y, string name = null)
+            => gen_math_ops.maximum(x, y, name: name);
+
+        /// <summary>
+        /// Returns the min of x and y (i.e. x < y ? x : y) element-wise.
+        /// </summary>
+        /// <typeparam name="T1"></typeparam>
+        /// <typeparam name="T2"></typeparam>
+        /// <param name="x"></param>
+        /// <param name="y"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static Tensor minimum<T1, T2>(T1 x, T2 y, string name = null)
+            => gen_math_ops.minimum(x, y, name: name);
+
         public static Tensor multiply(Tensor x, Tensor y) 
             => gen_math_ops.mul(x, y);
 

src/TensorFlowNET.Core/Operations/gen_array_ops.cs

@@ -26,13 +26,6 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
-        public static Tensor less<Tx, Ty>(Tx x, Ty y, string name = null)
-        {
-            var _op = _op_def_lib._apply_op_helper("Less", name: name, args: new { x, y });
-
-            return _op.outputs[0];
-        }
-
         public static Tensor pack(Tensor[] values, int axis = 0, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Pack", name: name, args: new { values, axis });

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -135,6 +135,27 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
+        public static Tensor less<Tx, Ty>(Tx x, Ty y, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("Less", name: name, args: new { x, y });
+
+            return _op.outputs[0];
+        }
+
+        public static Tensor less_equal<Tx, Ty>(Tx x, Ty y, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("LessEqual", name: name, args: new { x, y });
+
+            return _op.outputs[0];
+        }
+
+        public static Tensor log1p(Tensor x, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("Log1p", name, args: new { x });
+
+            return _op.outputs[0];
+        }
+
         public static Tensor squared_difference(Tensor x, Tensor y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("SquaredDifference", name, args: new { x, y, name });
@@ -308,6 +329,13 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
+        public static Tensor minimum<T1, T2>(T1 x, T2 y, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("Minimum", name, args: new { x, y });
+
+            return _op.outputs[0];
+        }
+
         public static Tensor _abs(Tensor x, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Abs", name, new { x });
@@ -322,6 +350,13 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
+        public static Tensor _min<Tx, Ty>(Tx input, Ty axis, bool keep_dims = false, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("Min", name, new { input, reduction_indices = axis, keep_dims });
+
+            return _op.outputs[0];
+        }
+
         public static Tensor pow<Tx, Ty>(Tx x, Ty y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Pow", name, args: new { x, y });

src/TensorFlowNET.Core/Tensors/Tensor.Operators.cs

@@ -27,12 +27,12 @@ namespace Tensorflow
 
         public static Tensor operator %(Tensor x, Tensor y) => BinaryOpWrapper("mod", x, y);
 
-        public static Tensor operator >(Tensor x, int y) => gen_array_ops.greater(x, y);
-        public static Tensor operator >(Tensor x, float y) => gen_array_ops.greater(x, y);
-        public static Tensor operator >(Tensor x, double y) => gen_array_ops.greater(x, y);
-        public static Tensor operator <(Tensor x, int y) => gen_array_ops.less(x, y);
-        public static Tensor operator <(Tensor x, float y) => gen_array_ops.less(x, y);
-        public static Tensor operator <(Tensor x, double y) => gen_array_ops.less(x, y);
+        public static Tensor operator >(Tensor x, int y) => gen_math_ops.greater(x, y);
+        public static Tensor operator >(Tensor x, float y) => gen_math_ops.greater(x, y);
+        public static Tensor operator >(Tensor x, double y) => gen_math_ops.greater(x, y);
+        public static Tensor operator <(Tensor x, int y) => gen_math_ops.less(x, y);
+        public static Tensor operator <(Tensor x, float y) => gen_math_ops.less(x, y);
+        public static Tensor operator <(Tensor x, double y) => gen_math_ops.less(x, y);
 
         private static Tensor BinaryOpWrapper<Tx, Ty>(string name, Tx x, Ty y)
         {