tf.reshape的-1的错误理解

一直以为reshape(-1,)会变成一维的，然后变成一个标量；

import tensorflow as tf
lenth = tf.reshape(30,shape=[-1])
lenth2 = tf.reshape(30,shape=[])
lenth3 = tf.reshape([30],shape=[])
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    a = sess.run(lenth)
    b = sess.run(lenth2)
    c = sess.run(lenth3)
print(a,b,c)

[30] 30 30

变成一维的，理解没有错，但一维与标量确理解错了；标量可以理解为0维；

想用标量可以用shape=[]参数定义

If one component of `shape` is the special value -1, the size of that dimension
    is computed so that the total size remains constant.  In particular, a `shape`
    of `[-1]` flattens into 1-D.  At most one component of `shape` can be -1.
    
    If `shape` is 1-D or higher, then the operation returns a tensor with shape
    `shape` filled with the values of `tensor`. In this case, the number of elements
    implied by `shape` must be the same as the number of elements in `tensor`.
    
    For example:
    
    ```
    # 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
    ```