TensorFlow tf.nn.conv2d实现卷积的方式


Posted in Python onJanuary 03, 2020

实验环境:tensorflow版本1.2.0,python2.7

介绍

惯例先展示函数:

tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, name=None)

除去name参数用以指定该操作的name,与方法有关的一共五个参数:

input:

指需要做卷积的输入图像,它要求是一个Tensor,具有[batch, in_height, in_width, in_channels]这样的shape,具体含义是[训练时一个batch的图片数量, 图片高度, 图片宽度, 图像通道数],注意这是一个4维的Tensor,要求类型为float32和float64其中之一

filter:

相当于CNN中的卷积核,它要求是一个Tensor,具有[filter_height, filter_width, in_channels, out_channels]这样的shape,具体含义是[卷积核的高度,卷积核的宽度,图像通道数,卷积核个数],要求类型与参数input相同,有一个地方需要注意,第三维in_channels,就是参数input的第四维

strides:卷积时在图像每一维的步长,这是一个一维的向量,长度4

padding:

string类型的量,只能是”SAME”,”VALID”其中之一,这个值决定了不同的卷积方式(后面会介绍)

use_cudnn_on_gpu:

bool类型,是否使用cudnn加速,默认为true

结果返回一个Tensor,这个输出,就是我们常说的feature map

实验

那么TensorFlow的卷积具体是怎样实现的呢,用一些例子去解释它:

1.考虑一种最简单的情况,现在有一张3×3单通道的图像(对应的shape:[1,3,3,1]),用一个1×1的卷积核(对应的shape:[1,1,1,1])去做卷积,最后会得到一张3×3的feature map

2.增加图片的通道数,使用一张3×3五通道的图像(对应的shape:[1,3,3,5]),用一个1×1的卷积核(对应的shape:[1,1,1,1])去做卷积,仍然是一张3×3的feature map,这就相当于每一个像素点,卷积核都与该像素点的每一个通道做点积

input = tf.Variable(tf.random_normal([1,3,3,5]))
filter = tf.Variable(tf.random_normal([1,1,5,1]))

op = tf.nn.conv2d(input, filter, strides=[1, 1, 1, 1], padding='VALID')

3.把卷积核扩大,现在用3×3的卷积核做卷积,最后的输出是一个值,相当于情况2的feature map所有像素点的值求和

input = tf.Variable(tf.random_normal([1,3,3,5]))
filter = tf.Variable(tf.random_normal([3,3,5,1]))

op = tf.nn.conv2d(input, filter, strides=[1, 1, 1, 1], padding='VALID')

4.使用更大的图片将情况2的图片扩大到5×5,仍然是3×3的卷积核,令步长为1,输出3×3的feature map

.....
.xxx.
.xxx.
.xxx.
.....

5.上面我们一直令参数padding的值为‘VALID',当其为‘SAME'时,表示卷积核可以停留在图像边缘,如下,输出5×5的feature map

input = tf.Variable(tf.random_normal([1,5,5,5]))
filter = tf.Variable(tf.random_normal([3,3,5,1]))

op = tf.nn.conv2d(input, filter, strides=[1, 1, 1, 1], padding='SAME')
xxxxx
xxxxx
xxxxx
xxxxx
xxxxx

6.如果卷积核有多个

input = tf.Variable(tf.random_normal([1,5,5,5]))
filter = tf.Variable(tf.random_normal([3,3,5,7]))

op = tf.nn.conv2d(input, filter, strides=[1, 1, 1, 1], padding='SAME')

此时输出7张5×5的feature map

7.步长不为1的情况,文档里说了对于图片,因为只有两维,通常strides取[1,stride,stride,1]

input = tf.Variable(tf.random_normal([1,5,5,5]))
filter = tf.Variable(tf.random_normal([3,3,5,7]))

op = tf.nn.conv2d(input, filter, strides=[1, 2, 2, 1], padding='SAME')

此时,输出7张3×3的feature map

x.x.x
.....
x.x.x
.....
x.x.x

8.如果batch值不为1,同时输入10张图

input = tf.Variable(tf.random_normal([10,5,5,5]))
filter = tf.Variable(tf.random_normal([3,3,5,7]))

op = tf.nn.conv2d(input, filter, strides=[1, 2, 2, 1], padding='SAME')

每张图,都有7张3×3的feature map,输出的shape就是[10,3,3,7]

代码清单

最后,把程序总结一下:

import tensorflow as tf
#case 2
input = tf.Variable(tf.random_normal([1,3,3,5]))
filter = tf.Variable(tf.random_normal([1,1,5,1]))

op2 = tf.nn.conv2d(input, filter, strides=[1, 1, 1, 1], padding='VALID')
#case 3
input = tf.Variable(tf.random_normal([1,3,3,5]))
filter = tf.Variable(tf.random_normal([3,3,5,1]))

op3 = tf.nn.conv2d(input, filter, strides=[1, 1, 1, 1], padding='VALID')
#case 4
input = tf.Variable(tf.random_normal([1,5,5,5]))
filter = tf.Variable(tf.random_normal([3,3,5,1]))

op4 = tf.nn.conv2d(input, filter, strides=[1, 1, 1, 1], padding='VALID')
#case 5
input = tf.Variable(tf.random_normal([1,5,5,5]))
filter = tf.Variable(tf.random_normal([3,3,5,1]))

op5 = tf.nn.conv2d(input, filter, strides=[1, 1, 1, 1], padding='SAME')
#case 6
input = tf.Variable(tf.random_normal([1,5,5,5]))
filter = tf.Variable(tf.random_normal([3,3,5,7]))

op6 = tf.nn.conv2d(input, filter, strides=[1, 1, 1, 1], padding='SAME')
#case 7
input = tf.Variable(tf.random_normal([1,5,5,5]))
filter = tf.Variable(tf.random_normal([3,3,5,7]))

op7 = tf.nn.conv2d(input, filter, strides=[1, 2, 2, 1], padding='SAME')
#case 8
input = tf.Variable(tf.random_normal([10,5,5,5]))
filter = tf.Variable(tf.random_normal([3,3,5,7]))

op8 = tf.nn.conv2d(input, filter, strides=[1, 2, 2, 1], padding='SAME')

init = tf.initialize_all_variables()
with tf.Session() as sess:
  sess.run(init)
  print("case 2")
  print(sess.run(op2))
  print("case 3")
  print(sess.run(op3))
  print("case 4")
  print(sess.run(op4))
  print("case 5")
  print(sess.run(op5))
  print("case 6")
  print(sess.run(op6))
  print("case 7")
  print(sess.run(op7))
  print("case 8")
  print(sess.run(op8))

因为是随机初始化,我的结果是这样的:

case 2
[[[[-0.64064658]
  [-1.82183945]
  [-2.63191342]]

 [[ 8.05008984]
  [ 1.66023612]
  [ 2.53465152]]

 [[-3.51703644]
  [-5.92647743]
  [ 0.55595356]]]]
case 3
[[[[ 10.53139973]]]]
case 4
[[[[ 10.45460224]
  [ 6.23760509]
  [ 4.97157574]]

 [[ 3.05653667]
  [-11.43907833]
  [ -2.05077457]]

 [[ -7.48340607]
  [ -0.90697062]
  [ 3.27171206]]]]
case 5
[[[[ 5.30279875]
  [ -2.75329947]
  [ 5.62432575]
  [-10.24609661]
  [ 0.12603235]]

 [[ 0.2113893 ]
  [ 1.73748684]
  [ -3.04372549]
  [ -7.2625494 ]
  [-12.76445198]]

 [[ -1.57414591]
  [ -3.39802694]
  [ -6.01582575]
  [ -1.73042905]
  [ -3.07183361]]

 [[ 1.41795194]
  [ -2.02815866]
  [-17.08983231]
  [ 11.98958111]
  [ 2.44879103]]

 [[ 0.29902667]
  [ -3.19712877]
  [ -2.84978414]
  [ -2.71143317]
  [ 5.99366283]]]]
case 6
[[[[ 12.02504349  4.35077286  2.67207813  5.77893162  6.98221684
   -0.96858567 -8.1147871 ]
  [ -0.02988982 -2.52141953 15.24755192  6.39476395 -4.36355495
   -2.34515095  5.55743504]
  [ -2.74448752 -1.62703776 -6.84849405 10.12248802  3.7408421
   4.71439075  6.13722801]
  [ 0.82365227 -1.00546622 -3.29460764  5.12690163 -0.75699937
   -2.60097408 -8.33882809]
  [ 0.76171923 -0.86230004 -6.30558443 -5.58426857  2.70478535
   8.98232937 -2.45504045]]

 [[ 3.13419819 -13.96483231  0.42031103  2.97559547  6.86646557
   -3.44916964 -0.10199898]
  [ 11.65359879 -5.2145977  4.28352737  2.68335319  3.21993709
   -6.77338028  8.08918095]
  [ 0.91533852 -0.31835344 -1.06122255 -9.11237717  5.05267143
   5.6913228  -5.23855162]
  [ -0.58775592 -5.03531456 14.70254898  9.78966522 -11.00562763
   -4.08925819 -3.29650426]
  [ -2.23447251 -0.18028721 -4.80610704 11.2093544  -6.72472
   -2.67547607  1.68422937]]

 [[ -3.40548897 -9.70355129 -1.05640507 -2.55293012 -2.78455877
  -15.05377483 -4.16571808]
  [ 13.66925812  2.87588191  8.29056358  6.71941566  2.56558466
   10.10329056  2.88392687]
  [ -6.30473804 -3.3073864  12.43273926 -0.66088223  2.94875336
   0.06056046 -2.78857946]
  [ -7.14735603 -1.44281793  3.3629775  -7.87305021  2.00383091
   -2.50426936 -6.93097973]
  [ -3.15817571  1.85821593  0.60049552 -0.43315536 -4.43284273
   0.54264796  1.54882073]]

 [[ 2.19440389 -0.21308756 -4.35629082 -3.62100363 -0.08513772
   -0.80940366  7.57606506]
  [ -2.65713739  0.45524287 -16.04298019 -5.19629049 -0.63200498
   1.13256514 -6.70045137]
  [ 8.00792599  4.09538221 -6.16250181  8.35843849 -4.25959206
   -1.5945878  -7.60996151]
  [ 8.56787586  5.85663748 -4.38656425  0.12728286 -6.53928804
   2.3200655  9.47253895]
  [ -6.62967777  2.88872099 -2.76913023 -0.86287498 -1.4262073
   -6.59967232  5.97229099]]

 [[ -3.59423327  4.60458899 -5.08300591  1.32078576  3.27156973
   0.5302844  -5.27635145]
  [ -0.87793881  1.79624665  1.66793108 -4.70763969 -2.87593603
   -1.26820421 -7.72825718]
  [ -1.49699068 -3.40959787 -1.21225107 -1.11641395 -8.50123024
   -0.59399474  3.18010235]
  [ -4.4249506  -0.73349547 -1.49064219 -6.09967899  5.18624878
   -3.80284953 -0.55285597]
  [ -1.42934585  2.76053572 -5.19795799  0.83952439 -0.15203482
   0.28564462  2.66513705]]]]
case 7
[[[[ 2.66223097  2.64498258 -2.93302107  3.50935125  4.62247562
   2.04241085 -2.65325522]
  [ -0.03272867 -1.00103927 -4.3691597  2.16724801  7.75251007
   -4.6788125  -0.89318085]
  [ 4.74175072 -0.80443329 -1.02710629 -6.68772554  4.57605314
   -3.72993755  4.79951382]]

 [[ 5.249547   8.92288399  7.10703182 -9.10498428 -7.43814278
   -8.69616318  1.78862095]
  [ 7.53669024 -14.52316284 -2.55870199 -1.11976743  3.81035042
   2.45559502 -2.35436153]
  [ 3.93275881  5.11939669 -4.7114296 -11.96386623  2.11866689
   0.57433248 -7.19815397]]

 [[ 0.25111672  1.40801668  1.28818977 -2.64093828  0.98182392
   3.69512987  4.78833389]
  [ 0.30391204 -10.26406097  6.05877018 -6.04775047  8.95922089
   0.80235004 -5.4520669 ]
  [ -7.24697018 -2.33498096 -10.20039558 -1.24307609  3.99351597
   -8.1029129  2.44411373]]]]
case 8
[[[[ -6.84037447e+00  1.33321762e-01 -5.09891272e+00  5.55682087e+00
   8.22002888e+00 -4.94586229e-02  4.19012117e+00]
  [ 6.79884481e+00  1.21652853e+00 -5.69557810e+00 -1.33555794e+00
   3.24849486e-01  4.88868570e+00 -3.90220714e+00]
  [ -3.53190374e+00 -4.11765718e+00  4.54340839e+00  1.85549557e+00
   -3.38682461e+00  2.62719369e+00 -4.98658371e+00]]

 [[ -9.86354351e+00 -6.76713943e+00  3.62617874e+00 -6.16720629e+00
   1.96754158e+00 -4.54203081e+00 -1.37485743e+00]
  [ -1.76783955e+00  2.35163045e+00 -2.21175838e+00  3.83091879e+00
   3.16964531e+00 -7.58307219e+00  4.71943617e+00]
  [ 1.20776439e+00  4.86006308e+00  1.04233503e+01 -7.82327271e+00
   5.39195156e+00 -6.31672382e+00  1.35577369e+00]]

 [[ -3.65947580e+00 -1.98961139e+00  7.53771305e+00  2.79224634e-01
   -2.90050888e+00 -3.57466817e+00 -6.33232594e-01]
  [ 5.89931488e-01  2.83219159e-01 -1.65850735e+00 -6.45545387e+00
   -1.17044592e+00  1.40343285e+00  5.74970901e-01]
  [ -8.58810043e+00 -1.25172977e+01  6.84177876e-01  3.80004168e+00
   -1.54420209e+00 -3.32161427e+00 -1.05423713e+00]]]


 [[[ -4.82677078e+00  3.11167526e+00 -4.32694483e+00 -4.77198696e+00
   2.32186103e+00  1.65402293e-01 -5.32707453e+00]
  [ 3.91779566e+00  6.27949667e+00  2.32975650e+00 -1.06336937e+01
   4.44044876e+00  8.08288479e+00 -5.83346319e+00]
  [ -2.82141399e+00 -9.16103745e+00  6.98908520e+00 -5.66505909e+00
   -2.11039782e+00  2.27499461e+00 -5.74120235e+00]]

 [[ 6.71680808e-01 -4.01104212e+00 -4.61760712e+00  1.02667952e+01
   -8.21200657e+00 -8.57054043e+00  1.71461976e+00]
  [ 2.40794683e+00 -2.63071585e+00  9.68963623e+00 -4.51778412e+00
   -3.91073084e+00 -5.91874409e+00  9.96273613e+00]
  [ 2.67705870e+00  2.85607010e-01  2.45853162e+00  4.44810390e+00
   -2.11300468e+00 -5.77583075e+00  2.83322239e+00]]

 [[ -8.21949577e+00 -7.57754421e+00  3.93484974e+00  2.26189137e+00
   -3.49395227e+00 -6.40283823e+00 -6.00450039e-01]
  [ 2.95964479e-02 -1.19976890e+00  5.38537979e+00  4.62369967e+00
   3.89780998e+00 -6.36872959e+00  7.12107182e+00]
  [ -8.85006547e-01  1.92706418e+00  3.26668215e+00  2.03566647e+00
   1.44209075e+00 -6.48463774e+00 -8.33671093e-02]]]


 [[[ -2.64583921e+00  3.86011934e+00  4.18198538e+00  3.50338411e+00
   6.35944796e+00 -4.28423309e+00  4.87355423e+00]
  [ 4.42271233e+00  3.92883778e+00 -5.59371090e+00  4.98251200e+00
   -3.45068884e+00  2.91921115e+00  1.03779554e+00]
  [ 1.36162388e+00 -1.06808968e+01 -3.92534947e+00  1.85111761e-01
   -4.87255526e+00  1.66666222e+01 -1.04918976e+01]]

 [[ -4.34632540e+00  1.74614882e+00 -2.89012527e+00 -8.74067783e+00
   5.06610107e+00  1.24989772e+00 -3.06433105e+00]
  [ 2.49973416e+00  2.14041996e+00 -4.71008825e+00  7.39326143e+00
   3.94770741e+00  8.23049164e+00 -1.67046225e+00]
  [ -2.94665837e+00 -4.58543825e+00  7.21219683e+00  1.09780006e+01
   5.17258358e+00  7.90257788e+00 -2.13929534e+00]]

 [[ 4.20402241e+00 -2.98926830e+00 -3.89006615e-01 -8.16001511e+00
   -2.38355541e+00  1.42584383e+00 -5.46632290e+00]
  [ 5.52395058e+00  5.09255171e+00 -1.08742390e+01 -4.96262169e+00
   -1.35298109e+00  3.65663052e-01 -3.40589857e+00]
  [ -6.95647061e-01 -4.12855625e+00  2.66609401e-01 -9.39565372e+00
   -3.85058141e+00  2.51248240e-01 -5.77149725e+00]]]


 [[[ 1.22103825e+01  5.72040796e+00 -3.56989503e+00 -1.02248180e+00
   -5.20942688e-01  7.15008640e+00  3.43482435e-01]
  [ 6.01409674e+00 -1.59511256e+00 -6.48080063e+00 -1.82889538e+01
   -1.03537569e+01 -1.48270035e+01 -5.26662111e+00]
  [ 5.51758146e+00 -2.91831636e+00  3.75461340e-01 -9.23893452e-02
   -9.22101116e+00  7.16952372e+00 -6.86479330e-01]]

 [[ -3.03645611e+00  6.68620300e+00 -3.31973934e+00 -4.91346550e+00
   9.20719814e+00 -2.55552864e+00 -2.16087699e-02]
  [ -3.02986956e+00 -1.29726543e+01  1.53023469e+00 -8.19733238e+00
   5.68085670e+00 -1.72856820e+00 -4.69369221e+00]
  [ -6.67176056e+00  8.76355553e+00  2.18996063e-01 -4.38777208e+00
   -6.35764122e-01 -1.37812555e+00 -4.41474581e+00]]

 [[ 2.25345469e+00  1.02142305e+01 -1.71714854e+00 -5.29060185e-01
   2.27982092e+00 -8.75302982e+00  7.13998675e-02]
  [ -6.67547846e+00  3.67722750e+00 -3.44172812e+00  5.69674826e+00
   -2.28723526e+00  5.92991543e+00  5.53608060e-01]
  [ -1.01174891e-01 -2.73731589e+00 -4.06187654e-01  6.54158068e+00
   2.59603882e+00  2.99202776e+00 -2.22350287e+00]]]


 [[[ -1.81271315e+00  2.47674489e+00 -2.90284491e+00  1.34291325e+01
   7.69864845e+00 -1.27134466e+00  3.02233839e+00]
  [ -2.08135307e-01  1.03206539e+00  1.90775347e+00  9.01517391e+00
   -3.52140331e+00  9.05393791e+00 -9.12732124e-01]
  [ 1.12128162e+00  5.98179293e+00 -2.27206993e+00 -5.21281779e-01
   6.20835352e+00  3.73474598e+00  1.18961644e+00]]

 [[ 3.17242837e+00 -6.00571585e+00  2.37661076e+00 -5.64483738e+00
   -6.45412731e+00  8.75251675e+00  7.33790398e-02]
  [ 3.08957529e+00 -1.06855690e-01 -5.16810894e-01 -9.41085911e+00
   8.23878098e+00  6.79738426e+00 -1.23478663e+00]
  [ -9.20640087e+00 -6.82801771e+00 -5.96975613e+00  7.61030674e-01
   -4.35995817e+00 -3.54818010e+00 -2.56281614e+00]]

 [[ 4.69872713e-01  8.36402321e+00  5.37103415e-01 -1.68033957e-01
   -3.21731424e+00 -7.34270859e+00 -3.14253521e+00]
  [ 6.69656086e+00 -5.27954197e+00 -8.57314682e+00  4.84328842e+00
   -2.96387672e+00  2.47114658e+00  2.85376692e+00]
  [ -7.86032295e+00 -7.18845367e+00 -3.27161223e-01  9.27330971e+00
   -6.14093494e+00 -4.49041557e+00  3.47160912e+00]]]


 [[[ -1.89188433e+00  5.43082857e+00  6.04252160e-01  6.92894220e+00
   8.59178162e+00  1.02003086e+00  5.31300211e+00]
  [ -8.97491455e-01  6.52438164e+00 -4.43710327e+00  7.10509634e+00
   8.84234428e+00  3.08552694e+00  2.78152227e+00]
  [ -9.40537453e-02  2.34666920e+00 -5.57496691e+00 -8.62346458e+00
   -1.32807600e+00 -8.12027454e-02 -9.00946975e-01]]

 [[ -3.53673506e+00  8.93675327e+00  3.27456236e-01 -3.41519475e+00
   7.69804525e+00 -5.18698692e+00 -3.96991730e+00]
  [ 1.99988627e+00 -9.16149998e+00 -7.49944544e+00  5.02162695e-01
   3.57059622e+00  9.17566013e+00 -1.77589107e+00]
  [ -1.18147678e+01 -7.68992901e+00  1.88449645e+00  2.77643538e+00
   -1.11342735e+01 -3.12916255e+00 -3.34161663e+00]]

 [[ -3.62668943e+00 -3.10993242e+00  3.60834384e+00  4.69678783e+00
   -1.73794723e+00 -1.27035933e+01  3.65882218e-01]
  [ -8.97550106e+00 -4.33533072e-01  4.41743970e-01 -5.83433771e+00
   -4.85818958e+00  9.56629372e+00  3.56375504e+00]
  [ -6.87092066e+00  1.96412420e+00  5.14182663e+00 -8.97769547e+00
   3.61136627e+00  5.91387987e-01 -2.95224571e+00]]]


 [[[ -1.11802626e+00  3.24175072e+00  5.94067669e+00  9.29727936e+00
   9.28199863e+00 -4.80889034e+00  6.96202660e+00]
  [ 7.23959684e+00  3.11182523e+00  1.84116721e+00  5.12095928e-01
   -7.65049171e+00 -4.05325556e+00  5.38544941e+00]
  [ 4.66621685e+00 -1.61665392e+00  9.76448345e+00  2.38519001e+00
   -2.06760812e+00 -6.03633642e-01  3.66192675e+00]]

 [[ 1.52149725e+00 -1.84441996e+00  4.87877655e+00  2.96750760e+00
   2.37311172e+00 -2.98487616e+00  9.98114228e-01]
  [ 9.20035839e+00  5.24396753e+00 -2.57312679e+00 -7.26040459e+00
   -1.17509928e+01  6.85688591e+00  3.37383580e+00]
  [ 6.17629957e+00 -5.15294194e-01 -1.64212489e+00 -5.70274448e+00
   -2.36294913e+00  2.60432816e+00  2.63957453e+00]]

 [[ 7.91168213e-03 -1.15018034e+00  3.05471039e+00  3.31086922e+00
   5.35744762e+00  1.14832592e+00  9.56500292e-01]
  [ 4.86464739e+00  5.37348413e+00  1.42920148e+00  1.62809372e+00
   2.61656570e+00  7.88479471e+00 -6.09324336e-01]
  [ 7.71319962e+00 -1.73930550e+00 -2.99925613e+00 -3.14857435e+00
   3.19194889e+00  1.70928288e+00  4.90955710e-01]]]


 [[[ -1.79046512e+00  8.54369068e+00  1.85044312e+00 -9.88471413e+00
   9.52995300e-01 -1.34820042e+01 -1.13713551e+01]
  [ 8.37582207e+00  6.64692163e+00 -3.22429276e+00  3.37997460e+00
   3.91468263e+00  6.96061993e+00 -1.18029404e+00]
  [ -2.13278866e+00  4.36152029e+00 -4.14593410e+00 -2.15160155e+00
   1.90767622e+00  1.16321917e+01 -3.72644544e+00]]

 [[ -5.03508925e-01 -6.33426476e+00 -1.06393566e+01 -6.49301624e+00
   -6.31036520e+00  3.13485146e+00 -5.77433109e-01]
  [ 7.41444230e-01 -4.87326956e+00 -5.98253345e+00 -9.14121056e+00
   -8.64077091e-01  2.06696177e+00 -7.59688473e+00]
  [ 1.38767815e+00  1.84418947e-01  5.72539902e+00 -2.07557893e+00
   9.70911503e-01  1.16765432e+01 -1.40111232e+00]]

 [[ -1.21869087e+00  2.44499159e+00 -1.65706706e+00 -6.19807529e+00
   -5.56950712e+00 -1.72372568e+00  3.62687564e+00]
  [ 2.23708963e+00 -2.87862611e+00  2.71666467e-01  4.35115099e+00
   -8.85548592e-01  2.91860628e+00  8.10848951e-01]
  [ -5.33635712e+00  7.15072036e-01  5.21240902e+00 -3.11152220e+00
   2.01623154e+00 -2.28398323e-01 -3.23233747e+00]]]


 [[[ 3.77991509e+00  5.53513861e+00 -1.82022047e+00  4.22430277e+00
   5.60331726e+00 -4.28308249e+00  4.54524136e+00]
  [ -5.30983162e+00 -3.45605731e+00  2.69374561e+00 -6.16836596e+00
   -9.18601036e+00 -1.58697796e+00 -5.73809910e+00]
  [ 2.18868661e+00  6.96338892e-01  1.88057957e+01 -4.21353197e+00
   1.20818818e+00  2.85108542e+00  6.62180042e+00]]

 [[ 1.01285219e+01 -4.86819077e+00 -2.45067930e+00  7.50106812e-01
   4.37201977e+00  4.78472042e+00  1.19103444e+00]
  [ -3.26395583e+00 -5.59358537e-01  1.52001972e+01 -5.93994498e-01
   -1.49040818e+00 -7.02547312e+00 -1.29268813e+00]
  [ 1.02763653e+01  1.31108007e+01 -2.91605043e+00 -1.37688947e+00
   3.33029580e+00  1.96966705e+01  2.55259371e+00]]

 [[ 4.58397627e+00 -3.19160700e+00 -6.51985502e+00  1.02908373e+01
   -4.17618275e+00 -9.69347239e-01  7.46259832e+00]
  [ 6.09876537e+00  1.33044279e+00  5.04027081e+00 -6.87740147e-01
   4.14770365e+00 -2.26751328e-01  1.54876924e+00]
  [ 2.70127630e+00 -1.59834003e+00 -1.82587504e+00 -5.92888784e+00
   -5.65038967e+00 -6.46078014e+00 -1.80765367e+00]]]


 [[[ -1.57899165e+00  3.39969063e+00  1.02308102e+01 -7.77082300e+00
   -8.02129686e-01 -3.67387819e+00 -1.37204361e+00]
  [ 3.93093729e+00  6.17498016e+00 -1.41695750e+00 -1.26903206e-01
   2.18985319e+00  5.83657503e-01  7.39725351e-01]
  [ 5.53898287e+00  2.22283316e+00 -1.10478985e+00  2.68644023e+00
   -2.59913635e+00  3.74231935e+00  4.85016155e+00]]

 [[ 4.05368614e+00 -3.74058294e+00  7.32348633e+00 -1.17656231e+00
   3.71810269e+00 -1.63957381e+00  9.91670132e-01]
  [ -1.29317007e+01  1.12296543e+01 -1.13844347e+01 -7.13933802e+00
   -8.65884399e+00 -5.56065178e+00 -1.46718264e+00]
  [ -8.08718109e+00 -1.98826480e+00 -4.07488203e+00  2.06440473e+00
   1.13524094e+01  5.68703651e+00 -2.18706942e+00]]

 [[ 1.51166654e+00 -6.84034204e+00  9.33474350e+00 -4.80931902e+00
   -6.24172688e-02 -4.21381521e+00 -5.73313046e+00]
  [ -1.35943902e+00  5.27799511e+00 -3.77813816e+00  6.88291168e+00
   4.35068893e+00 -1.02540245e+01  8.86861205e-01]
  [ -4.49999619e+00 -2.97630525e+00 -6.18604183e-01 -2.49702692e+00
   -6.76169348e+00 -2.55930996e+00 -2.71291423e+00]]]]

以上这篇TensorFlow tf.nn.conv2d实现卷积的方式就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持三水点靠木。

Python 相关文章推荐
Pyramid将models.py文件的内容分布到多个文件的方法
Nov 27 Python
Python网络编程 Python套接字编程
Sep 13 Python
Python使用Matplotlib实现Logos设计代码
Dec 25 Python
Python实现翻转数组功能示例
Jan 12 Python
使用pandas对矢量化数据进行替换处理的方法
Apr 11 Python
python 用正则表达式筛选文本信息的实例
Jun 05 Python
Django管理员账号和密码忘记的完美解决方法
Dec 06 Python
Python爬虫:url中带字典列表参数的编码转换方法
Aug 21 Python
使用python模拟高斯分布例子
Dec 09 Python
python实现对变位词的判断方法
Apr 05 Python
python 带时区的日期格式化操作
Oct 23 Python
python中出现invalid syntax报错的几种原因分析
Feb 12 Python
Python调用钉钉自定义机器人的实现
Jan 03 #Python
pytorch中的上采样以及各种反操作,求逆操作详解
Jan 03 #Python
pytorch 获取tensor维度信息示例
Jan 03 #Python
pytorch中torch.max和Tensor.view函数用法详解
Jan 03 #Python
pytorch逐元素比较tensor大小实例
Jan 03 #Python
pytorch 改变tensor尺寸的实现
Jan 03 #Python
Pytorch Tensor 输出为txt和mat格式方式
Jan 03 #Python
You might like
PHP中几种常见的超时处理全面总结
2012/09/11 PHP
记录mysql性能查询过程的使用方法
2013/05/02 PHP
PHP xpath提取网页数据内容代码解析
2020/07/16 PHP
js几个不错的函数 $$()
2006/10/09 Javascript
jquery实现文本框鼠标右击无效以及不能输入的代码
2010/11/05 Javascript
地址栏传递中文参数乱码在js里用escape转码
2013/08/28 Javascript
FireBug 调试JS入门教程 如何调试JS
2013/12/23 Javascript
一个CSS+jQuery实现的放大缩小动画效果
2014/02/19 Javascript
JavaScript正则表达式之multiline属性的应用
2015/06/16 Javascript
利用jQuery中的ajax分页实现代码
2016/02/25 Javascript
微信小程序实现左滑修改、删除功能
2020/10/19 Javascript
vue+flask实现视频合成功能(拖拽上传)
2021/03/04 Vue.js
[22:20]初生之犊-TI4第5名LGD战队纪录片
2014/08/13 DOTA
[01:54]胎教DOTA2 准妈妈玩家现身中国区预选赛
2016/06/26 DOTA
[07:20]2018DOTA2国际邀请赛寻真——逐梦Mineski
2018/08/10 DOTA
python局域网ip扫描示例分享
2014/04/03 Python
Python切片用法实例教程
2014/09/08 Python
pycharm安装图文教程
2017/05/02 Python
python 把列表转化为字符串的方法
2018/10/23 Python
python多进程使用及线程池的使用方法代码详解
2018/10/24 Python
Python numpy中矩阵的基本用法汇总
2019/02/12 Python
Python实现的爬取百度贴吧图片功能完整示例
2019/05/10 Python
python获取指定日期范围内的每一天,每个月,每季度的方法
2019/08/08 Python
pycharm双击无响应(打不开问题解决办法)
2020/01/10 Python
python Matplotlib数据可视化(2):详解三大容器对象与常用设置
2020/09/30 Python
Python实现微信表情包炸群功能
2021/01/28 Python
Electrolux伊莱克斯巴西商店:家用电器、小家电和配件
2018/05/23 全球购物
英国家居装饰品、户外家具和玻璃器皿购物网站:Rinkit.com
2019/11/04 全球购物
中学教师管理制度
2014/01/14 职场文书
酒店管理求职信范文
2014/04/06 职场文书
园艺专业毕业生求职信
2014/09/02 职场文书
2015年家长学校工作总结
2015/04/22 职场文书
2015年学校图书室工作总结
2015/05/19 职场文书
员工保密协议范本,您一定得收藏!很有用!
2019/08/08 职场文书
船舶调度指挥系统——助力智慧海事
2022/02/18 无线电
CSS中实现动画效果-附案例
2022/02/28 HTML / CSS