tensorflow 行转为列_TensorFlow2-维度变换

心已赠人 2023-01-08 08:12 160阅读 0赞

TensorFlow2-维度变换

Outline(大纲)

shape, ndim

reshape

expand\_dims/squeeze

transpose

图片视图

\[b, 28, 28\] \# 保存b张图片，28行，28列(保存数据一般行优先)，图片的数据没有被破坏

\[b, 28\*28\] \# 保存b张图片，不考虑图片的行和列，只保存图片的数据，不关注图片数据的细节

\[b, 2, 14\*28\] \# 保存b张图片，把图片分为上下两个部分，两个部分具体多少行是不清楚的

\[b, 28, 28, 1\] \# 保存b张图片，28行，28列，1个通道

First Reshape(重塑视图)

import tensorflow as tf

a = tf.random.normal(\[4, 28, 28, 3\])

a.shape, a.ndim

(TensorShape(\[4, 28, 28, 3\]), 4)

tf.reshape(a, \[4, 784, 3\]).shape \# 给出一张图片某个通道的数据，丢失行、宽的信息

TensorShape(\[4, 784, 3\])

tf.reshape(a, \[4, -1, 3\]).shape \# 4\*(-1)\*3 = 4\*28\*28\*3

TensorShape(\[4, 784, 3\])

tf.reshape(a, \[4, 784\*3\]).shape \# 给出一张图片的所有数据，丢失行、宽和通道的信息

TensorShape(\[4, 2352\])

tf.reshape(a, \[4, -1\]).shape

TensorShape(\[4, 2352\])

Second Reshape(恢复视图)

tf.reshape(tf.reshape(a, \[4, -1\]), \[4, 28, 28, 3\]).shape

TensorShape(\[4, 28, 28, 3\])

tf.reshape(tf.reshape(a, \[4, -1\]), \[4, 14, 56, 3\]).shape

TensorShape(\[4, 14, 56, 3\])

tf.reshape(tf.reshape(a, \[4, -1\]), \[4, 1, 784, 3\]).shape

TensorShape(\[4, 1, 784, 3\])

first reshape:

images: \[4,28,28,3\]

reshape to: \[4,784,3\]

second reshape:

\[4,784,3\]  height:28,width:28  \[4,28,28,3\] √

\[4,784,3\]  height:14,width:56  \[4,14,56,3\] ×

\[4,784,3\]  width:28,height:28  \[4,28,28,3\] ×

Transpose(转置)

a = tf.random.normal((4, 3, 2, 1))

a.shape

TensorShape(\[4, 3, 2, 1\])

tf.transpose(a).shape

TensorShape(\[1, 2, 3, 4\])

tf.transpose(a, perm=\[0, 1, 3, 2\]).shape \# 按照索引替换维度

TensorShape(\[4, 3, 1, 2\])

a = tf.random.normal(\[4, 28, 28, 3\]) \# b,h,w,c

a.shape

TensorShape(\[4, 28, 28, 3\])

tf.transpose(a, \[0, 2, 1, 3\]).shape \# b,2,h,c

TensorShape(\[4, 28, 28, 3\])

tf.transpose(a, \[0, 3, 2, 1\]).shape \# b,c,w,h

TensorShape(\[4, 3, 28, 28\])

tf.transpose(a, \[0, 3, 1, 2\]).shape \# b,c,h,w

TensorShape(\[4, 3, 28, 28\])

Expand\_dims(增加维度)

a:\[classes, students, classes\]

add school dim(增加学校的维度):

\[1, 4, 35, 8\] + \[1, 4, 35, 8\] = \[2, 4, 35, 8\]

a = tf.random.normal(\[4, 25, 8\])

a.shape

TensorShape(\[4, 25, 8\])

tf.expand\_dims(a, axis=0).shape \# 索引0前

TensorShape(\[1, 4, 25, 8\])

tf.expand\_dims(a, axis=3).shape \# 索引3前

TensorShape(\[4, 25, 8, 1\])

tf.expand\_dims(a,axis=-1).shape \# 索引-1后

TensorShape(\[4, 25, 8, 1\])

tf.expand\_dims(a,axis=-4).shape \# 索引-4后，即左边空白处

TensorShape(\[1, 4, 25, 8\])

Squeeze(挤压维度)

Only squeeze for shape = 1 dim(只删除维度为1的维度)

\[4, 35, 8, 1\] = \[4, 35, 8\]

\[1, 4, 35, 8\] = \[14, 35, 8\]

\[1, 4, 35, 1\] = \[4, 35, 8\]

tf.squeeze(tf.zeros(\[1,2,1,1,3\])).shape

TensorShape(\[2, 3\])

a = tf.zeros(\[1,2,1,3\])

a.shape

TensorShape(\[1, 2, 1, 3\])

tf.squeeze(a,axis=0).shape

TensorShape(\[2, 1, 3\])

tf.squeeze(a,axis=2).shape

TensorShape(\[1, 2, 3\])

tf.squeeze(a,axis=-2).shape

TensorShape(\[1, 2, 3\])

tf.squeeze(a,axis=-4).shape

TensorShape(\[2, 1, 3\])

tensorflow 行转为列_TensorFlow2-维度变换

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