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数组填充Numpy

max-pooling multidimensional-array matrix numpy python

-4

Alk · 技术社区 · 7 年前

我有以下矩阵:

x = \
    np.array([[[[0.99256822,  0.63019905],
                [0.77484078,  0.27471319]],

               [[0.94722451,  0.95948516],
                [0.81838252,  0.48979609]],

               [[0.81673764,  0.9388614],
                [0.57575844,  0.82265243]]],


              [[[0.95485566,  0.94870753],
                [0.92680463,  0.90044481]],

               [[0.90128127,  0.98683992],
                  [0.9115591,  0.85900321]],

               [[0.949711,    0.85709163],
                  [0.70392261,  0.91043368]]]])

其尺寸: 2,3,2,2 . 我要做的是将其与以下矩阵相乘:

y = \
    np.array([[[[ 0.,          0.,          0.63019905,  0.        ],
   [ 0.,          0.99256822,  0.,          0.        ],
   [ 0.77484078,  0.,          0.,          0.27471319],
   [ 0.,          0.,          0.,          0.        ]],
  [[ 0.,          0.,          0.,          0.        ],
   [ 0.94722451,  0.,          0.,          0.95948516],
   [ 0.81838252, 0.,          0.,          0.        ],
   [ 0.,          0.,          0.48979609,  0.        ]],

  [[ 0.,          0.,          0.,          0.        ],
   [ 0.,          0.81673764,  0.,          0.9388614 ],
   [ 0.,          0.,          0.,          0.82265243],
   [ 0.57575844,  0.,          0.,          0.        ]]],
 [[[ 0.,          0.95485566,  0.,          0.        ],
   [ 0.,          0.,          0.,          0.94870753],
   [ 0.,          0.92680463,  0.,          0.        ],
   [ 0.,          0.,          0.,          0.90044481]],

  [[ 0.,          0.90128127,  0.,          0.        ],
   [ 0.,          0.,          0.,          0.98683992],
   [ 0.,          0.9115591,   0.,          0.        ],
   [ 0.,          0.,          0.,          0.85900321]],

  [[ 0.,          0.,          0.,          0.85709163],
   [ 0.,          0.949711,    0.,          0.        ],
   [ 0.,          0.70392261,  0.,          0.91043368],
   [ 0.,          0.,          0.,          0.        ]]]])

这有维度 2,3,4,4 . 所以我需要做的是填充第一个矩阵,我们将每个条目复制4次,这样就可以进行乘法(其中3个结果将细化为0,最终结果将是我想要的乘法)。因此,我需要将第一个矩阵转换为如下所示:

    [[[[ 0.99256822          0.99256822          0.63019905  0.63019905        ]
       [ 0.99256822          0.99256822          0.63019905  0.63019905        ]
        [ 0.77484078         0.77484078          0.27471319  0.27471319]
        [ 0.77484078         0.77484078          0.27471319  0.27471319        ]]

等等

更新:

 def bprop(self, inputs, outputs, grads_wrt_outputs):

        m,n = grads_wrt_outputs.shape[:2]
        o = inputs.shape[2]
        p = inputs.shape[3]
        return (self.mask.reshape(m,n,2,2,2,2)*grads_wrt_outputs[:,:,:,None,:,None]).reshape(m,n,o,p)

这就是我正在使用它的场景。

2 回复 | 直到 7 年前

Community T.Woody 4 年前

我假设 a 和 b 分别作为两个阵列。

方法#1

为了得到重复的版本,我们可以扩展 一 到 6D 具有 np.broadcast_to 然后重塑为 4D -

a6D = a[:,:,:,None,:,None]
m,n,p,q = a.shape
r,s = b.shape[-2:]
a_repeated = np.broadcast_to(a6D, (m,n,p,r//p,q,s//q)).reshape(b.shape)

然后,使用 a_repeated 对于元素乘法 b .

方法#2(内存高效方法)

您可以扩展 一 6D 通过添加新轴,从而避免任何实际重复或平铺以提高内存效率,使用 6D 重塑 b 最后重塑为 4D 输出因此,对于 4D 阵列 一 和 b

m,n,p,q = a.shape
r,s = b.shape[-2:]
out = (b.reshape(m,n,p,r//p,q,s//q)*a[:,:,:,None,:,None]).reshape(b.shape)

Adeel Ahmad 7 年前

你要找的功能是 np.repeat . 我重复了 x 矩阵沿最后两个维度两次,如下所示:

>>> x = np.repeat(x, 2, axis=(2))
>>> x = np.repeat(x, 2, axis=(3))
>>> x
  [[[[ 0.99256822  0.99256822  0.63019905  0.63019905]
   [ 0.99256822  0.99256822  0.63019905  0.63019905]
   [ 0.77484078  0.77484078  0.27471319  0.27471319]
   [ 0.77484078  0.77484078  0.27471319  0.27471319]]

  [[ 0.94722451  0.94722451  0.95948516  0.95948516]
   [ 0.94722451  0.94722451  0.95948516  0.95948516]
   [ 0.81838252  0.81838252  0.48979609  0.48979609]
   [ 0.81838252  0.81838252  0.48979609  0.48979609]]
   ...
>>> x.shape
(2, 3, 4, 4)
>>> y.shape
(2, 3, 4, 4)

既然这两个矩阵的形状相似,就可以将它们相乘:

>>> x * y
[[[[ 0.          0.          0.39715084  0.        ]
   [ 0.          0.98519167  0.          0.        ]
   [ 0.60037823  0.          0.          0.07546734]
   [ 0.          0.          0.          0.        ]]

  [[ 0.          0.          0.          0.        ]
   [ 0.89723427  0.          0.          0.92061177]
   [ 0.66974995  0.          0.          0.        ]
   [ 0.          0.          0.23990021  0.        ]]

  [[ 0.          0.          0.          0.        ]
   [ 0.          0.66706037  0.          0.88146073]
   [ 0.          0.          0.          0.67675702]
   [ 0.33149778  0.          0.          0.        ]]]


 [[[ 0.          0.91174933  0.          0.        ]
   [ 0.          0.          0.          0.90004598]
   [ 0.          0.85896682  0.          0.        ]
   [ 0.          0.          0.          0.81080086]]

  [[ 0.          0.81230793  0.          0.        ]
   [ 0.          0.          0.          0.97385303]
   [ 0.          0.83093999  0.          0.        ]
   [ 0.          0.          0.          0.73788651]]

  [[ 0.          0.          0.          0.73460606]
   [ 0.          0.90195098  0.          0.        ]
   [ 0.          0.49550704  0.          0.82888949]
   [ 0.          0.          0.          0.        ]]]]