mxnet.ndarray.subtract¶

mxnet.ndarray.subtract(lhs, rhs)[source]¶

Returns element-wise difference of the input arrays with broadcasting.

Equivalent to lhs - rhs, mx.nd.broadcast_sub(lhs, rhs) and mx.nd.broadcast_minus(lhs, rhs).

Note

If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape.

Parameters

lhs (scalar or mxnet.ndarray.array) – First array to be subtracted.
rhs (scalar or mxnet.ndarray.array) – Second array to be subtracted. If lhs.shape != rhs.shape, they must be broadcastable to a common shape.

Returns

The element-wise difference of the input arrays.

Return type

NDArray

Examples

>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1.,  1.,  1.],
       [ 1.,  1.,  1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
       [ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0.,  1.]], dtype=float32)
>>> (x-2).asnumpy()
array([[-1., -1., -1.],
       [-1., -1., -1.]], dtype=float32)
>>> (x-y).asnumpy()
array([[ 1.,  1.,  1.],
       [ 0.,  0.,  0.]], dtype=float32)
>>> mx.nd.subtract(x,y).asnumpy()
array([[ 1.,  1.,  1.],
       [ 0.,  0.,  0.]], dtype=float32)
>>> (z-y).asnumpy()
array([[ 0.,  1.],
       [-1.,  0.]], dtype=float32)