tf.nn.conv2d

【TensorFlow】理解tf.nn.conv2d方法 ( 附代碼詳解註釋 )

2018年06月06日 14:31:32 左理想fisher 閱讀數 24046更多

分類專欄： tensorflow

 

版權聲明：本文爲博主原創文章，遵循 CC 4.0 BY-SA 版權協議，轉載請附上原文出處連接和本聲明。

本文連接： https://blog.csdn.net/zuolixiangfisher/article/details/80528989

最近在研究學習TensorFlow，在作識別手寫數字的demo時，遇到了tf.nn.conv2d這個方法，查閱了官網的API 發現講得比較簡略，仍是沒理解。google了一下，參考了網上一些朋友寫得博客，結合本身的理解，差很少整明白了。

方法定義

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

參數：

• input : 輸入的要作卷積的圖片，要求爲一個張量，shape爲 [ batch, in_height, in_weight, in_channel ]，其中batch爲圖片的數量，in_height 爲圖片高度，in_weight 爲圖片寬度，in_channel 爲圖片的通道數，灰度圖該值爲1，彩色圖爲3。（也能夠用其它值，可是具體含義不是很理解）
• filter： 卷積核，要求也是一個張量，shape爲 [ filter_height, filter_weight, in_channel, out_channels ]，其中 filter_height 爲卷積核高度，filter_weight 爲卷積核寬度，in_channel 是圖像通道數 ，和 input 的 in_channel 要保持一致，out_channel 是卷積核數量。
• strides： 卷積時在圖像每一維的步長，這是一個一維的向量，[ 1, strides, strides, 1]，第一位和最後一位固定必須是1
• padding： string類型，值爲「SAME」 和 「VALID」，表示的是卷積的形式，是否考慮邊界。"SAME"是考慮邊界，不足的時候用0去填充周圍，"VALID"則不考慮
• use_cudnn_on_gpu： bool類型，是否使用cudnn加速，默認爲true

具體實現

import tensorflow as tf # case 1 # 輸入是1張 3*3 大小的圖片，圖像通道數是5，卷積核是 1*1 大小，數量是1 # 步長是[1,1,1,1]最後獲得一個 3*3 的feature map # 1張圖最後輸出就是一個 shape爲[1,3,3,1] 的張量 input = tf.Variable(tf.random_normal([1,3,3,5])) filter = tf.Variable(tf.random_normal([1,1,5,1])) op1 = tf.nn.conv2d(input, filter, strides=[1,1,1,1], padding='SAME') # case 2 # 輸入是1張 3*3 大小的圖片，圖像通道數是5，卷積核是 2*2 大小，數量是1 # 步長是[1,1,1,1]最後獲得一個 3*3 的feature map # 1張圖最後輸出就是一個 shape爲[1,3,3,1] 的張量 input = tf.Variable(tf.random_normal([1,3,3,5])) filter = tf.Variable(tf.random_normal([2,2,5,1])) op2 = tf.nn.conv2d(input, filter, strides=[1,1,1,1], padding='SAME') # case 3 # 輸入是1張 3*3 大小的圖片，圖像通道數是5，卷積核是 3*3 大小，數量是1 # 步長是[1,1,1,1]最後獲得一個 1*1 的feature map (不考慮邊界) # 1張圖最後輸出就是一個 shape爲[1,1,1,1] 的張量 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 # 輸入是1張 5*5 大小的圖片，圖像通道數是5，卷積核是 3*3 大小，數量是1 # 步長是[1,1,1,1]最後獲得一個 3*3 的feature map (不考慮邊界) # 1張圖最後輸出就是一個 shape爲[1,3,3,1] 的張量 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 # 輸入是1張 5*5 大小的圖片，圖像通道數是5，卷積核是 3*3 大小，數量是1 # 步長是[1,1,1,1]最後獲得一個 5*5 的feature map (考慮邊界) # 1張圖最後輸出就是一個 shape爲[1,5,5,1] 的張量 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 # 輸入是1張 5*5 大小的圖片，圖像通道數是5，卷積核是 3*3 大小，數量是7 # 步長是[1,1,1,1]最後獲得一個 5*5 的feature map (考慮邊界) # 1張圖最後輸出就是一個 shape爲[1,5,5,7] 的張量 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 # 輸入是1張 5*5 大小的圖片，圖像通道數是5，卷積核是 3*3 大小，數量是7 # 步長是[1,2,2,1]最後獲得7個 3*3 的feature map (考慮邊界) # 1張圖最後輸出就是一個 shape爲[1,3,3,7] 的張量 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 # 輸入是10 張 5*5 大小的圖片，圖像通道數是5，卷積核是 3*3 大小，數量是7 # 步長是[1,2,2,1]最後每張圖獲得7個 3*3 的feature map (考慮邊界) # 10張圖最後輸出就是一個 shape爲[10,3,3,7] 的張量 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('*' * 20 + ' op1 ' + '*' * 20) print(sess.run(op1)) print('*' * 20 + ' op2 ' + '*' * 20) print(sess.run(op2)) print('*' * 20 + ' op3 ' + '*' * 20) print(sess.run(op3)) print('*' * 20 + ' op4 ' + '*' * 20) print(sess.run(op4)) print('*' * 20 + ' op5 ' + '*' * 20) print(sess.run(op5)) print('*' * 20 + ' op6 ' + '*' * 20) print(sess.run(op6)) print('*' * 20 + ' op7 ' + '*' * 20) print(sess.run(op7)) print('*' * 20 + ' op8 ' + '*' * 20) print(sess.run(op8)) 

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# 運行結果 ******************** op1 ******************** [[[[ 0.78366613] [-0.11703026] [ 3.533338 ]] [[ 3.4455981 ] [-2.40102 ] [-1.3336506 ]] [[ 1.9816184 ] [-3.3166158 ] [ 2.0968733 ]]]] ******************** op2 ******************** [[[[-4.429776 ] [ 4.1218996 ] [-4.1383405 ]] [[ 0.4804101 ] [ 1.3983132 ] [ 1.2663789 ]] [[-1.8450742 ] [-0.02915052] [-0.5696235 ]]]] ******************** op3 ******************** [[[[-6.969367]]]] ******************** op4 ******************** [[[[ -2.9217496 ] [ 4.4683943 ] [ 7.5761824 ]] [[-14.627491 ] [ -5.014709 ] [ -3.4593797 ]] [[ 0.45091882] [ 4.8827124 ] [ -9.658895 ]]]] ******************** op5 ******************** [[[[-2.8486536 ] [ 1.3990458 ] [ 2.953944 ] [-6.007198 ] [ 5.089696 ]] [[-0.20283715] [ 2.4726171 ] [ 6.2137847 ] [-0.38609552] [-1.8869443 ]] [[ 7.7240233 ] [10.6962805 ] [-3.1667676 ] [-3.6487846 ] [-2.2908094 ]] [[-9.00223 ] [ 4.5111785 ] [ 2.5615098 ] [-5.8492236 ] [ 1.7734764 ]] [[ 2.3674765 ] [-5.9122458 ] [ 5.867611 ] [-0.50353 ] [-4.890904 ]]]] ******************** op6 ******************** [[[[-4.06957626e+00 5.69651246e-01 2.97890633e-01 -5.08075190e+00 2.76357365e+00 -7.34121323e+00 -2.09436584e+00] [-9.03515625e+00 -8.96854973e+00 -4.40316677e+00 -3.23745847e+00 -3.56242275e+00 3.67262197e+00 2.59603453e+00] [ 1.25131302e+01 1.30267200e+01 2.25630283e+00 3.31285048e+00 -1.00396938e+01 -9.06786323e-01 -7.20120049e+00] [-3.18641067e-01 -7.66135693e+00 5.02029419e+00 -1.65469778e+00 -5.53000355e+00 -4.76842117e+00 4.98133230e+00] [ 3.68885136e+00 2.54145473e-01 -4.17096436e-01 1.20136106e+00 -2.29291725e+00 6.98313904e+00 4.92819786e-01]] [[ 1.22962761e+01 3.85902214e+00 -2.91524696e+00 -6.89016438e+00 3.35520816e+00 -1.85112596e+00 5.59113741e+00] [ 2.99087334e+00 4.42690086e+00 -3.34755349e+00 -7.41521478e-01 3.65099478e+00 -2.84761238e+00 -2.74149513e+00] [-9.65088654e+00 -4.91817188e+00 3.82093906e+00 -5.72443676e+00 1.43630829e+01 5.11133957e+00 -1.18163595e+01] [ 1.69606721e+00 -1.00837049e+01 9.65112305e+00 3.48559356e+00 4.71356201e+00 -2.74463081e+00 -5.76961470e+00] [-5.11555862e+00 1.06215849e+01 1.97274566e+00 -1.66155469e+00 5.40411043e+00 1.64753020e+00 -2.25898552e+00]] [[ 3.20135975e+00 1.16082029e+01 6.35383892e+00 -1.22541785e+00 -7.81781197e-01 -7.39507914e+00 3.02070093e+00] [ 3.37887239e+00 -3.17085648e+00 8.15050030e+00 9.17820644e+00 -5.42563820e+00 -1.06148596e+01 1.44039564e+01] [ 6.06520414e+00 -6.89214110e-01 1.18828654e+00 6.44250536e+00 -3.90648508e+00 -7.45609093e+00 1.70780718e-02] [-5.51369572e+00 -5.99862814e-01 -5.97459745e+00 5.03705800e-01 -4.89957094e-01 4.65023327e+00 6.97832489e+00] [ 5.56566572e+00 3.15251064e+00 4.23309374e+00 4.58887959e+00 1.11150384e+00 1.56815052e-01 -2.64446616e+00]] [[-3.47755957e+00 -2.51347685e+00 5.07092476e+00 -1.79448032e+01 1.23025656e+00 -7.04272604e+00 -3.11969209e+00] [-3.64519453e+00 -2.48672795e+00 1.45192409e+00 -7.42938709e+00 7.32508659e-01 1.73417020e+00 -8.84127915e-01] [ 4.80518007e+00 -1.00521259e+01 -1.47410703e+00 -2.73861027e+00 -6.11766815e+00 5.89801645e+00 7.41809845e+00] [ 1.52897854e+01 3.40052223e+00 -1.17849231e-01 8.11421871e+00 -7.15329647e-02 -8.57025623e+00 -6.36894524e-01] [-1.29184561e+01 -2.07097292e+00 6.51137114e+00 4.45195580e+00 6.51636696e+00 1.94592953e-01 7.76367307e-01]] [[-7.64904690e+00 -4.64357853e+00 -5.09730625e+00 1.46977997e+00 -2.66898251e+00 6.18280554e+00 7.30443239e+00] [ 3.74768376e-02 8.19200230e+00 -2.99126768e+00 -1.25706446e+00 2.82602859e+00 4.79209185e-01 -7.99170971e+00] [-9.31276321e+00 2.71563363e+00 2.68426132e+00 -2.98767281e+00 2.85978794e-01 5.26730251e+00 -6.51313114e+00] [-5.16205406e+00 -3.73660684e+00 -1.25655127e+00 -4.03212357e+00 -2.34876966e+00 3.49581933e+00 3.21578264e-01] [ 4.80592680e+00 -2.01916337e+00 -2.70319057e+00 9.14705086e+00 3.14293051e+00 -5.12257957e+00 1.87513745e+00]]]] ******************** op7 ******************** [[[[ -5.3398733 4.176247 -1.0400615 1.7490227 -2.3762708 -4.43866 -2.9152555 ] [ -6.2849035 2.9156108 2.2420614 3.0133455 2.697643 -1.2664369 2.2018924 ] [ -1.7367094 -2.6707978 -4.823809 -2.9799473 -2.588249 -0.8573512 0.7243177 ]] [[ 9.770168 -6.0919194 -7.755929 0.7116828 4.696847 -1.5403405 -10.603018 ] [ -2.2849545 7.23973 0.06859291 -0.3011052 -7.885673 -4.7223825 -1.2202084 ] [ -1.7584102 -0.9349402 1.8078477 6.8720684 11.548839 -1.3058915 1.785974 ]] [[ 3.8749192 -5.9033284 1.3921509 -2.68101 5.386052 5.2535496 7.804141 ] [ 1.9598813 -6.1589165 0.9447456 0.06089067 -3.7891803 -2.0653834 -2.60965 ] [ -2.1243367 -0.9703847 1.5366316 5.8760977 -3.697129 6.050654 -0.01914603]]]] ******************** op8 ******************** [[[[ 7.6126375 -2.261326 0.32292777 8.602917 -2.9009488 3.3160565 2.1506643 ] [ -3.5364501 -2.1440878 1.354662 5.531647 -1.4339367 5.1957445 -0.9030779 ] [ 7.844642 -6.1276717 7.7938704 -2.23364 -3.4782376 -5.097751 5.285432 ]] [[ -1.6915132 2.2787857 -5.9708385 8.21313 -4.5076394 -0.3270775 -8.479343 ] [ 2.0611243 3.1743298 -0.53598183 -3.0830724 -13.820877 5.3642063 -4.0782714 ] [ -2.2280676 -6.232974 6.031793 6.4705186 1.1858556 -5.012024 -0.12968755]] [[ -2.7237153 -2.0637414 1.4018252 -2.937191 2.572178 3.9408593 2.605546 ] [ -1.607345 5.66703 -4.989913 -6.0507936 -1.9384562 0.61666656 -6.9282484 ] [ -0.03978544 -2.008681 -7.406146 -1.2036608 -3.8769712 -3.0997906 6.066886 ]]] [[[ -0.6766513 -0.16299164 3.2324884 -3.3543284 2.711526 -0.7604065 -2.9422672 ] [-11.477009 6.985447 -7.168281 1.6444209 2.1505005 -2.5210168 1.248457 ] [ -2.5344536 0.78997815 4.921354 0.32946062 -3.4039345 2.3872323 1.0319829 ]] [[ 5.672534 -4.6865053 5.780566 11.394991 1.0943577 1.6653306 -0.93034 ] [ 11.131994 6.8491035 -15.839502 7.006518 3.261397 -0.99962735 10.55006 ] [ 2.6103654 2.7730281 2.3594556 3.5570846 6.1872926 4.217743 -6.4607897 ]] [[ -2.7581267 -0.12229636 1.351732 -4.4823456 2.1730578 -2.828763 -3.0473292 ] [ -2.742803 -5.817521 -4.570032 -7.3254657 3.2537496 -0.6938226 0.6609373 ] [ -3.1279428 -4.922457 2.745709 -4.864913 -3.6143937 2.6719465 -1.1376699 ]]] [[[ -0.7445632 0.45240074 5.131389 -2.8525875 1.3901956 -0.4648465 5.4685025 ] [ 3.1593595 1.2171756 0.1267331 -3.2178001 -2.6123729 -5.186987 4.1898375 ] [ 9.478796 -1.8722348 4.896418 1.301182 -3.6362329 -1.9956454 -1.770525 ]] [[ 4.8301635 -3.8837552 7.0490103 1.2435023 3.4047306 -3.2604568 1.051601 ] [ -2.2003438 0.88552344 -6.8119774 7.017317 -2.9890797 5.8106375 -0.863615 ] [ -0.17809808 -10.802618 3.225249 -2.0419974 5.072168 1.2349106 -4.600774 ]] [[ -3.1843624 -2.5729177 1.191327 -3.0042355 0.97465754 -4.564925 3.9409044 ] [ 1.2322719 14.114404 -0.35690814 2.2237332 0.35432827 -1.9053037 -12.545719 ] [ 0.80399454 -5.358243 -6.344287 3.5417094 -3.9716966 -0.02347088 3.0606985 ]]] [[[ 0.37148464 -3.8297706 -2.0831337 6.29245 2.5057077 0.8506646 1.9863653 ] [ 3.765554 1.4267049 1.0800252 7.7149706 0.44219214 8.109619 3.6685073 ] [ 4.635173 -2.9154918 -6.4538617 -5.448964 6.57819 0.61271524 2.9938192 ]] [[ -3.616211 0.0879938 -6.3440037 1.6937144 0.04956067 2.4064069 -8.493458 ] [ -5.0647597 0.93558145 -1.9845109 -8.771115 4.6100225 1.1144816 -12.28625 ] [ 1.0221918 -7.5176277 -1.8426392 -4.289383 2.2868915 -8.87014 -0.3772235 ]] [[ -1.1132717 2.4524128 -0.365159 4.004697 -1.5730555 0.5331385 -6.8898973 ] [ 3.5391765 2.8012395 0.7159001 7.421248 -3.0292435 3.0187619 -3.9419355 ] [ -5.387392 -6.63677 2.4566684 1.821631 -0.16935372 -0.88219285 2.2688925 ]]] [[[ -3.9313369 -1.8516166 -3.2839324 -6.9028835 8.055535 -1.080044 -1.732337 ] [ -3.1068752 2.6514802 3.7293913 -1.7883471 -5.44104 -4.5572286 -4.829409 ] [ 2.6451612 -3.1832254 3.171578 -4.6448216 -4.001822 -6.899353 -0.6295476 ]] [[ -0.65707624 -1.9670736 6.3386445 2.3041923 -4.439172 -2.9729037 -0.94020796] [ 0.43153757 5.194006 0.45434368 3.0731819 4.0513067 -5.8058457 6.947601 ] [ -4.2653627 0.9031774 -1.6685407 -5.4121113 0.5529208 0.7007126 9.279081 ]] [[ -0.37299162 2.7452188 1.9330034 3.6408103 -5.0701776 1.1965587 0.59263295] [ 4.81972 -1.1006856 7.8824034 5.260598 3.434634 0.04601002 8.869657 ] [ 4.231048 1.5457909 -4.7653384 -3.4977267 3.7780495 -5.872396 12.113913 ]]] [[[ -3.8766992 -0.398234 -1.9723368 1.2132525 0.56892383 1.2515173 3.7913866 ] [ -0.4337333 1.8678297 5.1747704 -0.6080067 -1.3174248 -1.7126535 0.4686459 ] [ -5.754308 -2.4168007 -3.6410232 -4.5670137 1.6215359 -4.580209 -5.5926514 ]] [[ 11.04498 4.4554973 3.8934658 -1.4875691 -11.931008 4.515834 -6.144173 ] [ 3.8855233 -7.6059284 5.552779 -0.4441495 4.6369743 2.3952575 4.981801 ] [ -4.5357304 8.016967 -3.8956852 8.697634 0.7237491 -1.2161034 9.980692 ]] [[ -3.8816683 -6.1477547 6.313223 3.8985054 -2.1990623 2.0681944 -0.53726804] [ 0.9768859 0.2593964 5.1300526 -4.3372006 4.838679 1.2677834 1.0290532 ] [ -2.7676988 6.0724287 4.556395 -2.004102 -0.79856735 2.4891334 -1.8703268 ]]] [[[ -2.4113853 -4.7984595 -0.28992027 1.1324785 5.6149826 3.4891384 -0.2521189 ] [ 11.86079 -2.660718 1.3913785 -9.618228 0.04568058 -2.8031406 1.12844 ] [ -0.08115374 2.8916602 -5.7155695 -5.4544435 2.526495 6.5253263 1.3852744 ]] [[ -1.5733382 -0.08704215 2.6952646 7.385515 -0.7799995 3.1702318 -14.530704 ] [ 0.05908662 -13.9438095 -1.154305 3.4328744 7.0506897 -5.0249805 2.5534477 ] [ 0.61222774 0.14303133 4.685219 -7.0924406 1.7709903 1.0107443 -4.5374393 ]] [[ -5.6678987 0.6903403 2.23693 1.2741803 -6.179094 3.0454116 -5.2941957 ] [ 0.23656422 -2.2511265 3.3220747 2.021302 -3.070989 -3.815312 3.7513428 ] [ 5.048253 5.163742 -3.064779 5.2195883 6.6997313 -2.0612605 2.076776 ]]] [[[ -1.1741709 0.50855964 3.7991686 6.946745 -0.99349356 1.4751754 -1.08081 ] [ 2.1064334 0.3293423 1.8446237 -0.3842956 3.8418627 -2.5760477 -4.709687 ] [ -3.8787804 5.9237094 -3.8139226 3.2697144 -2.5398688 4.3881574 11.573359 ]] [[ -3.1857545 7.100687 -3.9305675 0.6854049 -1.2562029 1.2753329 8.361776 ] [ 2.7635245 -1.649135 -1.3044827 5.9628034 7.0507197 8.040147 -0.5544966 ] [ 6.0894575 1.864697 2.0811782 -8.773295 3.7755995 5.5564737 -3.4745088 ]] [[ 1.3517151 2.8740213 -6.181453 0.21349654 -5.9370227 -1.6817973 3.0836923 ] [ -0.7866033 2.7180645 3.2119308 4.905232 -3.8589058 -3.349786 -1.2415386 ] [ 7.3208423 7.184522 1.8396591 0.25130635 4.5287986 -1.9662986 -5.4157324 ]]] [[[ -1.796482 -0.19289398 0.08456608 9.18009 4.3642817 3.9750414 10.058201 ] [ -3.404979 10.002911 2.6454616 0.09656489 -5.6097493 2.0856397 8.30741 ] [ -6.1940312 0.20053774 7.5518293 1.6553136 -6.075909 1.9946573 -8.276907 ]] [[ 1.5515908 -4.065265 6.201588 -10.958014 2.8450232 1.7398013 6.308612 ] [ 1.3526641 -0.20383507 -0.97939104 -12.001176 6.5776787 7.0159016 -2.6269057 ] [ -3.5487242 -2.0833373 2.128775 8.243093 -1.1012591 3.3278828 0.64393663]] [[ 2.3041837 -1.2524377 3.4256964 3.190121 0.32376206 1.0883296 -3.531728 ] [ -2.393531 0.57050663 -3.172806 7.0572777 -0.7350081 -2.5658474 -6.9233646 ] [ -1.0682559 -0.22647202 10.799706 -5.5458803 -3.2260892 -0.6237745 6.320084 ]]] [[[ 8.890318 1.926058 -5.8980203 3.4635465 2.0711088 -1.0413806 -6.304987 ] [ -7.1290493 -8.781645 -10.162883 3.1751637 2.1062303 -0.04042304 -14.788281 ] [ -1.382834 -7.988844 2.7986026 -1.9692816 0.30068183 -1.4710974 -5.3116736 ]] [[ -7.576119 -3.2894049 0.7375753 -1.3818941 2.9862103 -6.683834 -7.8058653 ] [ 4.9312177 -0.04471028 -0.34124258 8.375692 -8.983649 -2.1781216 -12.752575 ] [ 9.337945 -5.1725883 10.788802 0.9727853 -2.5389743 1.0551623 1.4216776 ]] [[ 1.5142308 4.546703 -2.5327616 4.6643023 -2.0437615 -1.7893765 4.8349857 ] [ 3.843536 8.979685 -5.5770497 12.787272 3.2864804 -9.081071 5.1559086 ] [ -3.7020745 9.714738 -5.7880783 -2.3634226 4.0264153 5.8175054 -7.454776 ]]]] 

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參考：
一、https://blog.csdn.net/mao_xiao_feng/article/details/53444333
二、https://www.tensorflow.org/api_docs/python/tf/nn/conv2d
三、CNN原理介紹 http://www.javashuo.com/article/p-gcwytflw-eo.html