**************input**************

[[[[-0.36166722 0.04847232 1.20818889 -0.1794038 -0.53244466]

[-0.67821187 -1.81838071 0.59005165 -1.17246294 0.33203208]

[-0.18631086 -0.68608224 0.07464688 0.28875718 -0.86492658]]

[[ 1.63322294 0.99059737 0.5923292 -0.80913633 -2.2539773 ]

[ 0.14436921 -0.45454684 -0.61321616 -1.01231539 1.54901564]

[ 0.38690856 1.84936357 0.55067211 0.3163861 -0.62082398]]

[[ 0.3655189 1.96013069 0.91159737 1.89106071 2.04635859]

[-1.13240027 -1.64421642 -1.23379624 -0.18057458 -0.37131071]

[-0.55824232 0.5738467 -1.02291656 0.8829596 -2.15986562]]]]

(1, 3, 3, 5)

*****************filter*************

[[[[ 0.43657559 1.01129627]

[ 0.30303505 1.57386982]

[ 0.63144618 -0.38221657]

[ 1.03055692 0.27556673]

[ 0.14717487 -0.47002205]]]]

(1, 1, 5, 2)

***************result************

[[[[ 0.35645172 -0.55043042]

[-1.63396096 -4.25244951]

[-0.07182495 -0.81064451]]

[[ 0.22164512 3.82079363]

[-1.27720094 -1.34204817]

[ 1.31174088 3.47044706]]

[[ 3.57920766 2.66549063]

[-2.0124495 -3.1366334 ]

[-0.12367389 1.98808599]]]]

(1, 3, 3, 2)

import tensorflow as tf
input = tf.Variable(tf.random_normal([1,3,3,5]));
filter = tf.Variable(tf.random_normal([1,1,5,2]));
op = tf.nn.conv2d(input,filter,strides=[1,1,1,1],padding=‘VALID‘);
with tf.Session() as sess:
sess.run(tf.initialize_all_variables());
result=sess.run(op);
print(‘**************input**************‘);
print(sess.run(input));
print(input.shape);
print(‘*****************filter*************‘);
print(sess.run(filter));
print(filter.shape);
print(‘***************result************‘);
print(result);
print(result.shape);

關於tensorflow conv2d卷積備忘的一點理解