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TensorFlow中,想要維度增加一維,可以使用tf.expand_dims(input, dim, name=None)函數(shù)。當(dāng)然,我們常用tf.reshape(input, shape=[])也可以達(dá)到相同效果,但是有些時(shí)候在構(gòu)建圖的過(guò)程中,placeholder沒有被feed具體的值,這時(shí)就會(huì)包下面的錯(cuò)誤:TypeError: Expected binary or unicode string, got 1
在這種情況下,我們就可以考慮使用expand_dims來(lái)將維度加1。比如我自己代碼中遇到的情況,在對(duì)圖像維度降到二維做特定操作后,要還原成四維[batch, height, width, channels],前后各增加一維。如果用reshape,則因?yàn)樯鲜鲈驁?bào)錯(cuò)
one_img2 = tf.reshape(one_img, shape=[1, one_img.get_shape()[0].value, one_img.get_shape()[1].value, 1])
用下面的方法可以實(shí)現(xiàn):
one_img = tf.expand_dims(one_img, 0) one_img = tf.expand_dims(one_img, -1) #-1表示最后一維
在最后,給出官方的例子和說(shuō)明
# 't' is a tensor of shape [2] shape(expand_dims(t, 0)) ==> [1, 2] shape(expand_dims(t, 1)) ==> [2, 1] shape(expand_dims(t, -1)) ==> [2, 1] # 't2' is a tensor of shape [2, 3, 5] shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5] shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5] shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
Args:
input: A Tensor.
dim: A Tensor. Must be one of the following types: int32, int64. 0-D (scalar). Specifies the dimension index at which to expand the shape of input.
name: A name for the operation (optional).
Returns:
A Tensor. Has the same type as input. Contains the same data as input, but its shape has an additional dimension of size 1 added.
以上這篇TensorFlow用expand_dim()來(lái)增加維度的方法就是小編分享給大家的全部?jī)?nèi)容了,希望能給大家一個(gè)參考,也希望大家多多支持億速云。
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