@np_utils.np_doc('bitwise_and') def bitwise_and(x1, x2): return _bitwise_binary_op(bitwise_ops.bitwise_and, x1, x2) @np_utils.np_doc('bitwise_or') def bitwise_or(x1, x2): return _bitwise_binary_op(bitwise_ops.bitwise_or, x1, x2) @np_utils.np_doc('bitwise_xor') def bitwise_xor(x1, x2): return _bitwise_binary_op(bitwise_ops.bitwise_xor, x1, x2) @np_utils.np_doc('bitwise_not', link=np_utils.AliasOf('invert')) def bitwise_not(x): def f(x): if x.dtype == dtypes.bool: return math_ops.logical_not(x) return bitwise_ops.invert(x) return _scalar(f, x) def _scalar(tf_fn, x, promote_to_float=False): """Computes the tf_fn(x) for each element in `x`. Args: tf_fn: function that takes a single Tensor argument. x: array_like. Could be an ndarray, a Tensor or any object that can be
from tensorflow.python.ops.array_ops import newaxis from tensorflow.python.ops.numpy_ops import np_random as random from tensorflow.python.ops.numpy_ops import np_utils # pylint: disable=wildcard-import from tensorflow.python.ops.numpy_ops.np_array_ops import * from tensorflow.python.ops.numpy_ops.np_arrays import ndarray from tensorflow.python.ops.numpy_ops.np_config import * from tensorflow.python.ops.numpy_ops.np_dtypes import * from tensorflow.python.ops.numpy_ops.np_math_ops import * # pylint: enable=wildcard-import from tensorflow.python.ops.numpy_ops.np_utils import finfo from tensorflow.python.ops.numpy_ops.np_utils import promote_types from tensorflow.python.ops.numpy_ops.np_utils import result_type # pylint: disable=redefined-builtin,undefined-variable @np_utils.np_doc("max", link=np_utils.AliasOf("amax")) def max(a, axis=None, keepdims=None): return amax(a, axis=axis, keepdims=keepdims) @np_utils.np_doc("min", link=np_utils.AliasOf("amin")) def min(a, axis=None, keepdims=None): return amin(a, axis=axis, keepdims=keepdims) @np_utils.np_doc("round", link=np_utils.AliasOf("around")) def round(a, decimals=0): return around(a, decimals=decimals) # pylint: enable=redefined-builtin,undefined-variable