tf.reshape(inputs, [-1,dim])的理解

柔情只为你懂 2023-10-04 14:24 4阅读 0赞

示例：

import tensorflow as tf
    lenth = tf.reshape(30,shape=[-1])  #变成一维张量
    lenth2 = tf.reshape(30,shape=[])  #变成标量了
    lenth3 = tf.reshape([30],shape=[])  #变成标量了
    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，但一维与标量确理解错了；标量可以理解为0维；

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
        ```