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(16)+1
#调整为合法的维度张量
x=tf.reshape(x,[1,4,4,1])
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=1,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=1,padding='VALID',output_shape=[1,4,4,1])
print(outs)

tf.Tensor(
[[[[-56.][-61.]][[-76.][-81.]]]], shape=(1, 2, 2, 1), dtype=float32)
tf.Tensor(
[[[[  56.][ -51.][  46.][ 183.]][[-148.][ -35.][  35.][-123.]][[  88.][  35.][ -35.][  63.]][[ 532.][ -41.][  36.][ 729.]]]], shape=(1, 4, 4, 1), dtype=float32)