import numpy as np
import tensorflow as tf
x_image = tf.placeholder(tf.float32,shape=[5,5])
x = tf.reshape(x_image,[1,5,5,1])
#Filter: W
W_cpu = np.array([[3,1,1],[0,-2,0],[0,-1,1]],dtype=np.float32)
W = tf.Variable(W_cpu)
W = tf.reshape(W, [3,3,1,1])
strides=[1, 1, 1, 1]#没用到
padding='VALID'
y = tf.nn.atrous_conv2d(x, W, 2, padding)
x_data = np.array([[1,0,0,0,0],[2,1,1,2,1],[1,1,2,2,0],[2,2,1,0,0],[2,1,2,1,1]],dtype=np.float32)
with tf.Session() as sess:
init = tf.initialize_all_variables()
sess.run(init)
x = (sess.run(x, feed_dict={x_image: x_data}))
W = (sess.run(W, feed_dict={x_image: x_data}))
y = (sess.run(y, feed_dict={x_image: x_data}))
print "The shape of x:\t", x.shape, ",\t and the x.reshape(5,5) is :"
print x.reshape(5,5)
print ""
print "The shape of x:\t", W.shape, ",\t and the W.reshape(3,3) is :"
print W.reshape(3,3)
print ""
print "The shape of y:\t", y.shape, ",\t and the y.reshape(1) is :"
print y.reshape(1)
print ""
