reshape()的括号中所包含的参数有哪些呢？常见的写法有tf.reshape((28,28)):
tf.reshape(tensor,shape,name=None)
将矩阵t变换为一维矩阵，然后再对矩阵的形式进行更改就好了，具体的流程如下：
reshape(t,shape) =>reshape(t,[-1]) =>reshape(t,shape)实际操作中，有如下效果：我创建了一个一维的数组
>>>import numpy as np >>>a= np.array([1,2,3,4,5,6,7,8]) >>>a array([1,2,3,4,5,6,7,8]) >>>使用reshape()方法来更改数组的形状，使得数组成为一个二维的数组：（数组中元素的个数是2×4=8）
>>>d = a.reshape((2,4)) >>>d array([[1, 2, 3, 4], [5, 6, 7, 8]])进一步提升，可以得到一个三维的数组f:（注意数组中元素的个数时2×2×2=8）
>>>f = a.reshape((2,2,2)) >>>f array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
（元素的个数是2×2=4，所以会报错）
>>> e = a.shape((2,2)) Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: 'tuple' object is not callable-1 的应用:-1 表示不知道该填什么数字合适的情况下，可以选择，由python通过a和其他的值3推测出来，比如，这里的a 是二维的数组，数组中共有6个元素，当使用reshape()时，6/3=2，所以形成的是3行2列的二维数组，可以看出，利用reshape进行数组形状的转换时，一定要满足（x,y）中x×y=数组的个数。
>>>a = np.array([[1,2,3],[4,5,6]]) >>>np.reshape(a,(3,-1)) array([[1, 2], [3, 4], [5, 6]]) >>> np.reshape(a,(1,-1)) array([[1, 2, 3, 4, 5, 6]]) >>> np.reshape(a,(6,-1)) array([[1], [2], [3], [4], [5], [6]]) >>> np.reshape(a,(-1,1)) array([[1], [2], [3], [4], [5], [6]])下面是两张2×3大小的图片（不知道有几张图片可以用-1代替），如何把所有二维照片给转换成一维的，请看以下三维的数组：
>>>image = np.array([[[1,2,3], [4,5,6]], [[1,1,1], [1,1,1]]]) >>>image.shape (2,2,3) >>>image.reshape((-1,6)) array([[1, 2, 3, 4, 5, 6], [1, 1, 1, 1, 1, 1]]) >>> a = image.reshape((-1,6)) >>> a.reshape((-1,12)) array([[1, 2, 3, 4, 5, 6, 1, 1, 1, 1, 1, 1]]) a.reshape((12,-1)) array([[1], [2], [3], [4], [5], [6], [1], [1], [1], [1], [1], [1]]) >>> a.reshape([-1]) array([1, 2, 3, 4, 5, 6, 1, 1, 1, 1, 1, 1])通过reshape生成的新的形状的数组和原始数组共用一个内存，所以一旦更改一个数组的元素，另一个数组也将会发生改变。
>>>a[1] = 100 >>>a array([ 1, 100, 3, 4, 5, 6, 7, 8]) >>> d array([[ 1, 100, 3, 4], [ 5, 6, 7, 8]])最后再给大家呈现一下官方给出的例子：
# 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