TensorFlow的reshape操作 tf.reshape的实现

初学tensorflow,如果写的不对的,请更正,谢谢!

tf.reshape(tensor, shape, name=None)

函数的作用是将tensor变换为参数shape的形式。

其中shape为一个列表形式,特殊的一点是列表中可以存在-1。-1代表的含义是不用我们自己指定这一维的大小,函数会自动计算,但列表中只能存在一个-1。(当然如果存在多个-1,就是一个存在多解的方程了)

好了我想说的重点还有一个就是根据shape如何变换矩阵。其实简单的想就是,

reshape(t, shape) => reshape(t, [-1]) => reshape(t, shape)

首先将矩阵t变为一维矩阵,然后再对矩阵的形式更改就可以了。

官方的例子:

# tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]

# tensor 't' has shape [9]

reshape(t, [3, 3]) ==> [[1, 2, 3],

[4, 5, 6],

[7, 8, 9]]

# tensor 't' is [[[1, 1], [2, 2]],

# [[3, 3], [4, 4]]]

# tensor 't' has shape [2, 2, 2]

reshape(t, [2, 4]) ==> [[1, 1, 2, 2],

[3, 3, 4, 4]]

# tensor 't' is [[[1, 1, 1],

# [2, 2, 2]],

# [[3, 3, 3],

# [4, 4, 4]],

# [[5, 5, 5],

# [6, 6, 6]]]

# tensor 't' has shape [3, 2, 3]

# pass '[-1]' to flatten 't'

reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]

# -1 can also be used to infer the shape

# -1 is inferred to be 9:

reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],

[4, 4, 4, 5, 5, 5, 6, 6, 6]]

# -1 is inferred to be 2:

reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],

[4, 4, 4, 5, 5, 5, 6, 6, 6]]

# -1 is inferred to be 3:

reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],

[2, 2, 2],

[3, 3, 3]],

[[4, 4, 4],

[5, 5, 5],

[6, 6, 6]]]

# tensor 't' is [7]

# shape `[]` reshapes to a scalar

reshape(t, []) ==> 7

在举几个例子或许就清楚了,有一个数组z,它的shape属性是(4, 4)

z = np.array([[1, 2, 3, 4],

[5, 6, 7, 8],

[9, 10, 11, 12],

[13, 14, 15, 16]])

z.shape

(4, 4)

z.reshape(-1)

z.reshape(-1)

array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16])

z.reshape(-1, 1)

也就是说,先前我们不知道z的shape属性是多少,但是想让z变成只有一列,行数不知道多少,通过`z.reshape(-1,1)`,Numpy自动计算出有12行,新的数组shape属性为(16, 1),与原来的(4, 4)配套。

z.reshape(-1,1)

array([[ 1],

[ 2],

[ 3],

[ 4],

[ 5],

[ 6],

[ 7],

[ 8],

[ 9],

[10],

[11],

[12],

[13],

[14],

[15],

[16]])

z.reshape(-1, 2)

newshape等于-1,列数等于2,行数未知,reshape后的shape等于(8, 2)

z.reshape(-1, 2)

array([[ 1, 2],

[ 3, 4],

[ 5, 6],

[ 7, 8],

[ 9, 10],

[11, 12],

[13, 14],

[15, 16]])

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