tensorflowサイクルtensorの値を変更する
1728 ワード
tfを使用する.concat()は4次元tensorのループ付与を実現する
出力:
alist=[[[[1,1,1],[2,2,2],[3,3,3]],[[4,4,4],[5,5,5],[6,6,6]]],[[[7,7,7],[8,8,8],[9,9,9]],[[10,10,10],[11,11,11],[12,12,12]]]] #2,2,3,3-n,c,h,w
kenel=(np.asarray(alist)*2).tolist()
print(kenel)
inputs=tf.constant(alist,dtype=tf.float32)
kenel=tf.constant(kenel,dtype=tf.float32)
inputs=tf.transpose(inputs,[0,2,3,1]) #n,h,w,c
kenel=tf.transpose(kenel,[0,2,3,1]) #n,h,w,c
uints=inputs.get_shape()
h=int(uints[1])
w=int(uints[2])
encoder_output=[]
for b in range(int(uints[0])):
encoder_output_c=[]
for c in range(int(uints[-1])):
one_channel_in = inputs[b, :, :, c]
one_channel_in = tf.reshape(one_channel_in, [1, h, w, 1])
one_channel_kernel = kenel[b, :, :, c]
one_channel_kernel = tf.reshape(one_channel_kernel, [h, w, 1, 1])
encoder_output_cc = tf.nn.conv2d(input=one_channel_in, filter=one_channel_kernel, strides=[1, 1, 1, 1], padding="SAME")
if c==0:
encoder_output_c=encoder_output_cc
else:
encoder_output_c=tf.concat([encoder_output_c,encoder_output_cc],axis=3)
if b==0:
encoder_output=encoder_output_c
else:
encoder_output = tf.concat([encoder_output, encoder_output_c], axis=0)
with tf.Session() as sess:
print(sess.run(tf.transpose(encoder_output,[0,3,1,2])))
print(encoder_output.get_shape())
出力:
[[[[ 32. 48. 32.]
[ 56. 84. 56.]
[ 32. 48. 32.]]
[[ 200. 300. 200.]
[ 308. 462. 308.]
[ 200. 300. 200.]]]
[[[ 512. 768. 512.]
[ 776. 1164. 776.]
[ 512. 768. 512.]]
[[ 968. 1452. 968.]
[1460. 2190. 1460.]
[ 968. 1452. 968.]]]]
(2, 3, 3, 2)