Add nd.power and sym.pow

python/mxnet/symbol/symbol.py

@@ -47,7 +47,7 @@
 from ._internal import SymbolBase, _set_symbol_class
 
 __all__ = ["Symbol", "var", "Variable", "Group", "load", "load_json",
-           "pow", "maximum", "minimum", "hypot", "eye", "zeros", "ones", "full", "arange",
+           "pow", "power", "maximum", "minimum", "hypot", "eye", "zeros", "ones", "full", "arange",
            "histogram", "split_v2"]
 
 
@@ -2740,6 +2740,8 @@ def pow(base, exp):
     Both inputs can be Symbol or scalar number.
     Broadcasting is not supported. Use `broadcast_pow` instead.
 
+    `sym.pow` is being deprecated, please use `sym.power` instead.
+
     Parameters
     ---------
     base : Symbol or scalar
@@ -2780,6 +2782,43 @@ def pow(base, exp):
         raise TypeError('types (%s, %s) not supported' % (str(type(base)), str(type(exp))))
 
 
+def power(base, exp):
+    """Returns element-wise result of base element raised to powers from exp element.
+
+    Both inputs can be Symbol or scalar number.
+    Broadcasting is not supported. Use `broadcast_pow` instead.
+
+    Parameters
+    ---------
+    base : Symbol or scalar
+        The base symbol
+    exp : Symbol or scalar
+        The exponent symbol
+
+    Returns
+    -------
+    Symbol or scalar
+        The bases in x raised to the exponents in y.
+
+    Examples
+    --------
+    >>> mx.sym.power(2, 3)
+    8
+    >>> x = mx.sym.Variable('x')
+    >>> y = mx.sym.Variable('y')
+    >>> z = mx.sym.power(x, 2)
+    >>> z.eval(x=mx.nd.array([1,2]))[0].asnumpy()
+    array([ 1.,  4.], dtype=float32)
+    >>> z = mx.sym.power(3, y)
+    >>> z.eval(y=mx.nd.array([2,3]))[0].asnumpy()
+    array([  9.,  27.], dtype=float32)
+    >>> z = mx.sym.power(x, y)
+    >>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([2,3]))[0].asnumpy()
+    array([  9.,  64.], dtype=float32)
+    """
+    return pow(base, exp)
+
+
 # pylint: disable=no-member
 # pylint: disable=redefined-builtin
 def maximum(left, right):

tests/python/unittest/test_operator.py

@@ -698,11 +698,11 @@ def test_symbol_pow():
 def test_pow_fn():
     shape = (3, 4)
     exp = mx.symbol.Variable("exp")
-    y = mx.sym.pow(2, exp)
     x = np.ones(shape)*3
-    check_numeric_gradient(y, [x], numeric_eps=1E-3)
-    check_symbolic_forward(y, [x], [2**x])
-    check_symbolic_backward(y, [x], [np.ones(shape)], [np.log(2) * 2**x])
+    for y in [mx.sym.pow(2, exp), mx.sym.power(2, exp)]:
+        check_numeric_gradient(y, [x], numeric_eps=1E-3)
+        check_symbolic_forward(y, [x], [2**x])
+        check_symbolic_backward(y, [x], [np.ones(shape)], [np.log(2) * 2**x])
 
 
 @with_seed()
@@ -6675,7 +6675,12 @@ def test_binary_math_operators():
                 lambda x, y: np.power(x, y),
                 lambda x, y: np.power(x, y - 1.) * y,
                 lambda x, y: np.power(x, y) * np.log(x),
-                0.2, 5.0, -4.0, 4.0]
+                0.2, 5.0, -4.0, 4.0],
+        'power': [lambda x, y: mx.sym.power(x, y),
+                  lambda x, y: np.power(x, y),
+                  lambda x, y: np.power(x, y - 1.) * y,
+                  lambda x, y: np.power(x, y) * np.log(x),
+                  0.2, 5.0, -4.0, 4.0]
     }
     # Loop over operators
     for name, op in binary_ops.items():