SoFunction
Updated on 2024-11-19

The TensorFlow .conv2d implementation of convolution

Experimental environment: tensorflow version 1.2.0, python2.7

present (sb for a job etc)

The convention is to show the function first:

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

Excluding the name parameter, which specifies the name of the operation, there are five parameters associated with the method:

input：

refers to the input image to be convolved, it is required to be a Tensor with a shape like [batch, in_height, in_width, in_channels], which means [number of images in a batch during training, image height, image width, number of image channels], note that this is a 4-dimensional Tensor. Requirement type is one of float32 and float64.

filter：

Equivalent to the convolution kernel in CNN, it is required to be a Tensor with a shape like [filter_height, filter_width, in_channels, out_channels], which means [height of the convolution kernel, width of the convolution kernel, the number of image channels, the number of convolution kernels], and the type of the requirement is the same as the parameter input, there is a place to note that the third dimension, in_channels, is the fourth dimension of input.

strides：The step size in each dimension of the image at the time of convolution, which is a one-dimensional vector of length 4

padding：

A string type quantity, which can only be one of "SAME", "VALID", this value determines the different convolution methods (described later).

use_cudnn_on_gpu：

bool type, whether to use cudnn acceleration, default is true

The result returns a Tensor, and this output, which is often referred to as a feature map

test

So how exactly is TensorFlow's convolution implemented, using some examples to explain it:

1. Consider one of the simplest cases, now there is a 3×3 single-channel image (corresponding to shape: [1, 3, 3, 1]), and a 1×1 convolution kernel (corresponding to shape: [1, 1, 1, 1]) is used to do the convolution, and we will end up with a 3×3 feature map

2. Increase the number of channels of the image, using a 3×3 five-channel image (corresponding to the shape: [1, 3, 3, 5]), with a 1×1 convolution kernel (corresponding to the shape: [1, 1, 1, 1, 1]) to do the convolution, is still a 3×3 feature map, which is equivalent to each pixel, the convolution kernel with the pixel of each channel to do the convolution point product

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

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

3. Expand the convolution kernel, now use a 3×3 convolution kernel to do the convolution, the final output is a value, equivalent to case 2 of the feature map of all the pixel points of the value of summation

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

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

4. Use a larger image to expand the case 2 image to 5×5, still with a 3×3 convolution kernel, so that the step size is 1, and output a 3×3 feature map

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

5. Above we have been making the value of the parameter padding 'VALID', when it is 'SAME', it means that the convolution kernel can stay at the edge of the image, as follows, output 5×5 feature map

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

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

6. If the convolution kernel has more than one

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

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

At this point the output is seven 5×5 feature maps

7. The step size is not 1, the document said for the picture, because there are only two dimensions, usually strides to take [1, stride, stride, 1]

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

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

At this point, the output is seven 3×3 feature maps


.....

.....

8. If the batch value is not 1, enter 10 images at the same time.

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

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

For each graph, there are seven 3×3 feature maps, and the output shape is [10, 3, 3, 7]

Code List

Finally, summarize the procedure:

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

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

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

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

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

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

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

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

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

Since it was randomly initialized, here's what I got:

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]]]]

This TensorFlow .conv2d implementation of convolution above is all I have to share with you, I hope it will give you a reference, and I hope you will support me more.