tensorflow中如何进行卷积计算

代码实现

import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim
x1=np.array([0,0,0,2,1,1,0,2,0])
x1=x1.reshape(3,3)
x1= tf.constant(x1, shape=[1, 3, 3, 1],dtype=tf.float32)
kernel_0=np.array([0,0,-1,1,0,0,0,0,1])
kernel_0=kernel_0.reshape(3,3)
kernel_0= tf.constant(kernel_0, shape=[3, 3, 1, 1],dtype=tf.float32)
conv2d = tf.nn.conv2d(x1, kernel_0, strides=[1, 1, 1, 1], padding='SAME')  # 卷积
# yy=slim.conv2d(x1,kernel_0,3)#这个如何ｙｏｇａ暂时还不知道
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    # x1, w,conv2d,yy= sess.run([x1,kernel_0,conv2d,yy])
    x1, w, conv2d= sess.run([x1, kernel_0, conv2d])
    print(conv2d,np.shape(conv2d)) #[1 2 -1
                                    # 1 2 -1
                                    # 0 1 2]. (1,3,3,1)
    # print(yy)