4、维度变换

①简单使用
 1 a = tf.random.normal([4,28,28,3])  #view1
 2 print(a.shape,a.ndim)  #(4, 28, 28, 3)  4
 3 
 4 b1 = tf.reshape(a,[4,784,3])   #输入一种新的理解方式，即view2。这里只关注像素的个数，
 5 print(b1.shape) #(4, 784, 3)
 6 
 7 b2 = tf.reshape(a,[4,-1,3]) # -1会自动计算原图片的像素的个数28*28，等同于b1的[4,784,3]
 8 print(b2.shape) #(4, 784, 3)
 9 
10 b3 = tf.reshape(a,[4,784*3]) #新的方式view3，隐藏了通道的信息
11 print(b3.shape) #(4, 2352)
12 
13 b4 = tf.reshape(a,[4,-1]) # -1自动计算783*3，等同于b3的[4,783*3]
14 print(b4.shape) # (4, 2352)

 ②灵活运用
 1 a = tf.random.normal([4,28,28,3])  #view1
 2 print(a.shape,a.ndim)  #(4, 28, 28, 3)  4
 3 
 4 b1 = tf.reshape(tf.reshape(a,[4,-1]),[4,28,28,3]) #tf.reshape(a,[4,-1])将原图片改成[4,28*28*3],然后继续变成[4,28,28,3]
 5 print(b1.shape) # (4, 28, 28, 3)
 6 
 7 b2 = tf.reshape(tf.reshape(a,[4,-1]),[4,14,56,3])
 8 print(b2.shape) # (4, 14, 56, 3)
 9 
10 b3 = tf.reshape(tf.reshape(a,[4,-1]),[4,1,784,3])
11 print(b3.shape) # (4, 1, 784, 3)

1 a = tf.random.normal([4,3,2,1])  #view1
2 print(a.shape,a.ndim)  #(4, 3, 2, 1) 4
3 
4 b1 = tf.transpose(a) #默认全部转置
5 print(b1.shape) #(1, 2, 3, 4)
6 
7 #如果希望只有某些维度进行转置，则需要指定参数，表示转换后的顺序号
8 b2 = tf.transpose(a,perm = [0,1,3,2])
9 print(b2.shape) #(4, 3, 1, 2)

 1 #增加维度
 2 a = tf.random.normal([4,35,8])
 3 print(a.shape) #(4, 35, 8)
 4 
 5 b1 = tf.expand_dims(a,axis=0)
 6 print(b1.shape) #(1, 4, 35, 8)
 7 
 8 b2 = tf.expand_dims(a,axis=3)
 9 print(b2.shape) #(4, 35, 8, 1)
10 
11 #减少维度，只能减掉shape = 1的维度
12 """
13 如：[2,33,21,1] ——> [2,33,21]
14     [3,1,4,1,1] ——> [3,4]
15 """
16 
17 a = tf.zeros([1,2,1,1,3])
18 print(a.shape)
19 
20 b1 = tf.squeeze(a) #默认减去所有shape为1的维度
21 print(b1.shape) # (2, 3)
22 
23 b2 = tf.squeeze(a,axis=[0,2]) #设定指定的维度
24 print(b2.shape) #(2, 1, 3)

1 # ①将b的维度自动进行扩张
2 a = tf.random.normal([4,32,32,3])
3 print(a.shape) # (4, 32, 32, 3)
4 
5 b = tf.random.normal([3])
6 print(b.shape) #(3,)
7 
8 c = a + b
9 print(c.shape) # (4, 32, 32, 3)

1 # ②维度转换，只能从低维度到高维度转换
2 a = tf.random.normal([4,1,1,1])
3 print(a.shape) # [4,1,1,1]
4 
5 #将b的维度扩张同a一样的维度
6 b = tf.broadcast_to(a,[4,32,32,3])
7 print(b.shape) #(4, 32, 32, 3)