import tensorflow as tf
import numpy as np
import tensorflow as keras
from tensorflow.keras import layers,optimizers,losses
#创建X矩阵，高宽各位5*5
x=tf.range(25)+1
#调整为合法的维度张量
x=tf.reshape(x,[1,5,5,1])
x=tf.cast(x,dtype=tf.float32)
#创建固定内容的卷积核矩阵
w=tf.constant([[-1,2,-3],[4,-5,6],[-7,8,-9]],dtype=tf.float32)
#调整为合法维度的张量
w=tf.expand_dims(w,axis=2)
w=tf.expand_dims(w,axis=3)
#运行普通卷积运算
out=tf.nn.conv2d(x,w,strides=2,padding='VALID')
print(out)
#普通卷积运算的输出作为转置卷积的输入，进行转置卷积运算
outs=tf.nn.conv2d_transpose(out,w,strides=2,padding='VALID',output_shape=[1,5,5,1])
print(outs)

tf.Tensor(
[[[[ -67.][ -77.]][[-117.][-127.]]]], shape=(1, 2, 2, 1), dtype=float32)
tf.Tensor(
[[[[   67.][ -134.][  278.][ -154.][  231.]][[ -268.][  335.][ -710.][  385.][ -462.]][[  586.][ -770.][ 1620.][ -870.][ 1074.]][[ -468.][  585.][-1210.][  635.][ -762.]][[  819.][ -936.][ 1942.][-1016.][ 1143.]]]], shape=(1, 5, 5, 1), dtype=float32)