TensorFlow tf.nn.conv2d实现卷积的方式

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]
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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我们。