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
|
1
2
3
4
|
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
|
1
2
3
4
|
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
|
1
2
3
4
5
|
......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
|
1
2
3
4
|
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') |
|
1
2
3
4
5
|
xxxxxxxxxxxxxxxxxxxxxxxxx |
6.如果卷积核有多个
|
1
2
3
4
|
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]
|
1
2
3
4
|
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
|
1
2
3
4
5
|
x.x.x.....x.x.x.....x.x.x |
8.如果batch值不为1，同时输入10张图
|
1
2
3
4
|
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：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
|
import tensorflow as tf#case 2input = 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 3input = 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 4input = 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 5input = 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 6input = 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 7input = 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 8input = 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：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
|
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服务器之家。
原文链接：https://blog.csdn.net/mao_xiao_feng/article/details/78004522
