def _argminmax(fn, a, axis=None): a = np_array_ops.array(a) if axis is None: # When axis is None numpy flattens the array. a_t = array_ops.reshape(a.data, [-1]) else: a_t = np_array_ops.atleast_1d(a).data return np_utils.tensor_to_ndarray(fn(input=a_t, axis=axis))
def _argminmax(fn, a, axis=None): a = np_array_ops.array(a) if axis is None: # When axis is None numpy flattens the array. a_t = array_ops.reshape(a, [-1]) else: a_t = np_array_ops.atleast_1d(a) return fn(input=a_t, axis=axis)