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Oceania2018Oceania2018
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tf.max, tf.min
1 parent 4c78760 commit 4c66c0b

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Lines changed: 124 additions & 13 deletions

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

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

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@@ -26,13 +26,6 @@ public static Tensor gather_v2(Tensor @params, Tensor indices, int axis, string
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return _op.outputs[0];
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}
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public static Tensor less<Tx, Ty>(Tx x, Ty y, string name = null)
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{
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var _op = _op_def_lib._apply_op_helper("Less", name: name, args: new { x, y });
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return _op.outputs[0];
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}
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public static Tensor pack(Tensor[] values, int axis = 0, string name = null)
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{
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var _op = _op_def_lib._apply_op_helper("Pack", name: name, args: new { values, axis });

‎src/TensorFlowNET.Core/Operations/gen_math_ops.cs‎

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Original file line numberDiff line numberDiff line change
@@ -135,6 +135,27 @@ public static Tensor greater_equal<Tx, Ty>(Tx x, Ty y, string name = null)
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return _op.outputs[0];
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}
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public static Tensor less<Tx, Ty>(Tx x, Ty y, string name = null)
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{
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var _op = _op_def_lib._apply_op_helper("Less", name: name, args: new { x, y });
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return _op.outputs[0];
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}
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public static Tensor less_equal<Tx, Ty>(Tx x, Ty y, string name = null)
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{
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var _op = _op_def_lib._apply_op_helper("LessEqual", name: name, args: new { x, y });
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return _op.outputs[0];
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}
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public static Tensor log1p(Tensor x, string name = null)
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{
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var _op = _op_def_lib._apply_op_helper("Log1p", name, args: new { x });
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return _op.outputs[0];
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}
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public static Tensor squared_difference(Tensor x, Tensor y, string name = null)
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{
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var _op = _op_def_lib._apply_op_helper("SquaredDifference", name, args: new { x, y, name });
@@ -308,6 +329,13 @@ public static Tensor maximum<T1, T2>(T1 x, T2 y, string name = null)
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return _op.outputs[0];
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}
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public static Tensor minimum<T1, T2>(T1 x, T2 y, string name = null)
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{
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var _op = _op_def_lib._apply_op_helper("Minimum", name, args: new { x, y });
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return _op.outputs[0];
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}
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public static Tensor _abs(Tensor x, string name = null)
312340
{
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var _op = _op_def_lib._apply_op_helper("Abs", name, new { x });
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return _op.outputs[0];
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}
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public static Tensor _min<Tx, Ty>(Tx input, Ty axis, bool keep_dims = false, string name = null)
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{
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var _op = _op_def_lib._apply_op_helper("Min", name, new { input, reduction_indices = axis, keep_dims });
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357+
return _op.outputs[0];
358+
}
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public static Tensor pow<Tx, Ty>(Tx x, Ty y, string name = null)
326361
{
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var _op = _op_def_lib._apply_op_helper("Pow", name, args: new { x, y });

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

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

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