DL之CNN：自定义SimpleConvNet【3层 im2col优化】利用mnist数据集实现手写数字识别多

DL之CNN：自定义SimpleConvNet【3层,im2col优化】利用mnist数据集实现手写数字识别多分类训练来评估模型

目录

输出结果

设计思路

核心代码

更多输出

输出结果

设计思路

核心代码

class Convolution:def __init__(self, W, b, stride=1, pad=0): ……def forward(self, x): FN, C, FH, FW = self.W.shape N, C, H, W = x.shapeout_h = 1 + int((H + 2*self.pad - FH) / self.stride)out_w = 1 + int((W + 2*self.pad - FW) / self.stride)col = im2col(x, FH, FW, self.stride, self.pad)col_W = self.W.reshape(FN, -1).T out = np.dot(col, col_W) + self.b out = out.reshape(N, out_h, out_w, -1).transpose(0, 3, 1, 2)self.x = xself.col = colself.col_W = col_Wreturn out def backward(self, dout): FN, C, FH, FW = self.W.shape dout = dout.transpose(0,2,3,1).reshape(-1, FN) self.db = np.sum(dout, axis=0) self.dW = np.dot(self.col.T, dout)self.dW = self.dW.transpose(1, 0).reshape(FN, C, FH, FW)dcol = np.dot(dout, self.col_W.T) return dx class Pooling:def __init__(self, pool_h, pool_w, stride=1, pad=0): self.pool_h = pool_hself.pool_w = pool_wself.stride = strideself.pad = padself.x = Noneself.arg_max = None……class SimpleConvNet: #def __init__(self, input_dim=(1, 28, 28), conv_param={'filter_num':30, 'filter_size':5, 'pad':0, 'stride':1},hidden_size=100, output_size=10, weight_init_std=0.01):filter_num = conv_param['filter_num']filter_size = conv_param['filter_size']filter_pad = conv_param['pad']filter_stride = conv_param['stride']input_size = input_dim[1]conv_output_size = (input_size - filter_size + 2*filter_pad) / filter_stride + 1pool_output_size = int(filter_num * (conv_output_size/2) * (conv_output_size/2))self.params = {}self.params['W1'] = weight_init_std * \np.random.randn(filter_num, input_dim[0], filter_size, filter_size)self.params['b1'] = np.zeros(filter_num)self.params['W2'] = weight_init_std * \np.random.randn(pool_output_size, hidden_size)self.params['b2'] = np.zeros(hidden_size)self.params['W3'] = weight_init_std * \np.random.randn(hidden_size, output_size)self.params['b3'] = np.zeros(output_size)self.layers = OrderedDict()self.layers['Conv1'] = Convolution(self.params['W1'], self.params['b1'],conv_param['stride'], conv_param['pad']) self.layers['Relu1'] = Relu() self.layers['Pool1'] = Pooling(pool_h=2, pool_w=2, stride=2) self.layers['Affine1'] = Affine(self.params['W2'], self.params['b2']) self.layers['Relu2'] = Relu()self.layers['Affine2'] = Affine(self.params['W3'], self.params['b3'])self.last_layer = SoftmaxWithLoss() ……def save_params(self, file_name="params.pkl"): params = {} for key, val in self.params.items(): params[key] = valwith open(file_name, 'wb') as f:pickle.dump(params, f)def load_params(self, file_name="params.pkl"): with open(file_name, 'rb') as f:params = pickle.load(f)for key, val in params.items(): self.params[key] = valfor i, key in enumerate(['Conv1', 'Affine1', 'Affine2']): self.layers[key].W = self.params['W' + str(i+1)]self.layers[key].b = self.params['b' + str(i+1)]

更多输出

train_loss:2.29956519109714=== epoch:1, train_acc:0.216, test_acc:0.218 ===train_loss:2.2975110344641716train_loss:2.291654113382576train_loss:2.2858174689127875train_loss:2.272262093336837train_loss:2.267908303517325train_loss:2.2584119706864336train_loss:2.2258807222804693train_loss:2.2111025085252543train_loss:2.188119055308738train_loss:2.163215575430596train_loss:2.1191887076886724train_loss:2.0542599060672186train_loss:2.0244523646451915train_loss:1.9779786923239808train_loss:1.9248431928319325train_loss:1.7920653808470397train_loss:1.726860911000866train_loss:1.7075144252509131train_loss:1.6875413868425186train_loss:1.6347461097804266train_loss:1.5437112361395253train_loss:1.4987893515035628train_loss:1.3856720782969847train_loss:1.2002110952243676train_loss:1.2731100379603273train_loss:1.117132621224333train_loss:1.0622583460165833train_loss:1.0960592785565957train_loss:0.8692067763172185train_loss:0.854878047317train_loss:0.83872966253374train_loss:0.7819342397053507train_loss:0.7589812430284729train_loss:0.795533991336train_loss:0.8190930469691535train_loss:0.6297212128196131train_loss:0.8279837022068413train_loss:0.6996430264702379train_loss:0.5256550729087258train_loss:0.7288553394002595train_loss:0.7033049908220391train_loss:0.5679669207218877train_loss:0.6344174262581003train_loss:0.7151382401438272train_loss:0.5814593192354963train_loss:0.5736217677325146train_loss:0.5673622947809682train_loss:0.48303413903204395train_loss:0.452267909884157train_loss:0.4009118158839013=== epoch:2, train_acc:0.818, test_acc:0.806 ===train_loss:0.5669686001623327train_loss:0.5358187806595359train_loss:0.3837535143737321train_loss:0.544335563142595train_loss:0.39288485196871803train_loss:0.49770310644457566train_loss:0.4610248131112265train_loss:0.36641463191798196train_loss:0.4874682221372042train_loss:0.38796698110644817train_loss:0.360776259665train_loss:0.4744726274001774train_loss:0.3086952062454927train_loss:0.40012397040718645train_loss:0.3634667070910744train_loss:0.3204093812396573train_loss:0.5063082359543781train_loss:0.5624992123039615train_loss:0.34281562891324663train_loss:0.3415065217065326train_loss:0.4946703009790488train_loss:0.48942997572068253train_loss:0.25416776815225534train_loss:0.3808555005314615train_loss:0.22793380858862108train_loss:0.4709915396804245train_loss:0.25826190862498605train_loss:0.44862426522901516train_loss:0.25519522472564815train_loss:0.5063495442657376train_loss:0.37233317168099206train_loss:0.4027673899570495train_loss:0.4234905061164214train_loss:0.44590221111177714train_loss:0.3846538639824134train_loss:0.3371733857576183train_loss:0.23612786737321756train_loss:0.4814543539448962train_loss:0.38362762929477556train_loss:0.5105811329813293train_loss:0.31729857191880056train_loss:0.43677582454472663train_loss:0.37362647454980324train_loss:0.2696715797445873train_loss:0.26682852302518134train_loss:0.18763432881504752train_loss:0.2886557425885745train_loss:0.23833327847639763train_loss:0.36315802981646train_loss:0.21083779781027828=== epoch:3, train_acc:0.89, test_acc:0.867 ===train_loss:0.34070333399972674train_loss:0.3356587138064409train_loss:0.25919406618960505train_loss:0.31537349840856743train_loss:0.2276928810208216train_loss:0.32171416950979326train_loss:0.22754919179736025train_loss:0.37619164258262944train_loss:0.3221102374023198train_loss:0.36724681541104537train_loss:0.3310213819075522train_loss:0.33583429981768936train_loss:0.36054827740285833train_loss:0.3002031789326344train_loss:0.19480027104864756train_loss:0.3074748184113467train_loss:0.31035699050378train_loss:0.37289594799797554train_loss:0.38054981033442864train_loss:0.2150866558286973train_loss:0.4014488874986493train_loss:0.2643304660197891train_loss:0.31806887985854354train_loss:0.29365139713396693train_loss:0.33212651106203267train_loss:0.29544164636048587train_loss:0.4969991428069569train_loss:0.3348535409949116train_loss:0.18914984777413654train_loss:0.3868380951987871train_loss:0.26857192970788485train_loss:0.373151707743815train_loss:0.3522570704735893train_loss:0.204823140388568train_loss:0.3974239710544049train_loss:0.21753509102652058train_loss:0.26034229667679715train_loss:0.26991319118062235train_loss:0.30959776720795107train_loss:0.2718109180045845train_loss:0.2738413103423023train_loss:0.22209179719364106train_loss:0.5025051167945939train_loss:0.23308114849307443train_loss:0.24989561030033144train_loss:0.4666621160650158train_loss:0.3511547384608582train_loss:0.32856542443039893train_loss:0.29344954251556093train_loss:0.21027623914222787=== epoch:4, train_acc:0.905, test_acc:0.897 ===train_loss:0.3912739685030935train_loss:0.38209838818230624train_loss:0.34743100915819064train_loss:0.2466622246872034train_loss:0.4342299239968299train_loss:0.2691256872383198train_loss:0.33061633649960986train_loss:0.24714178601043train_loss:0.27972544337302246train_loss:0.2594663777039397train_loss:0.3618566656990062train_loss:0.46329147512107755train_loss:0.24382989786183829train_loss:0.30893321320835465train_loss:0.32945962831674774train_loss:0.14512986683598966train_loss:0.18177996995372436train_loss:0.33010123547450865train_loss:0.22821102485978303train_loss:0.13184290288561265train_loss:0.1623416243274031train_loss:0.15789928544006773train_loss:0.28080142395723756train_loss:0.37489571529660976train_loss:0.1401357680735train_loss:0.2721256133343583train_loss:0.3284216941766708train_loss:0.18839612600685815train_loss:0.22950135076005498train_loss:0.3657428746249682train_loss:0.2656377917932745train_loss:0.18838799129016182train_loss:0.2875731634059018train_loss:0.4565329335709001train_loss:0.1894573118304train_loss:0.2305260793504801train_loss:0.2148999949995126train_loss:0.28529427710203675train_loss:0.2819535462668795train_loss:0.2670982521557257train_loss:0.2734307192256681train_loss:0.1388387469300277train_loss:0.2700532055195449train_loss:0.2179124091178431train_loss:0.19658434695884133train_loss:0.2777291934300614train_loss:0.20381437081332332train_loss:0.3290713715455train_loss:0.27254826158873285train_loss:0.22710678143573176=== epoch:5, train_acc:0.913, test_acc:0.912 ===train_loss:0.16794884237909946train_loss:0.22785903063567253train_loss:0.1704819172872827train_loss:0.2525653382920443train_loss:0.21185790294965987train_loss:0.17767717976901584train_loss:0.1889506605539382train_loss:0.17273423199217824train_loss:0.2510078095831616train_loss:0.14205249835249428train_loss:0.3129092704025964train_loss:0.3117928731764807train_loss:0.20503712236242064train_loss:0.20318831742627225train_loss:0.21303909770975452train_loss:0.23190878850961483train_loss:0.17291311185744473train_loss:0.20334851907094717train_loss:0.15855326731614855train_loss:0.21942667459237625train_loss:0.0924354215910217train_loss:0.09567491107181217train_loss:0.19180958792274005train_loss:0.25969731631050624train_loss:0.27574837165425986train_loss:0.24987203428843377train_loss:0.4377410898909417train_loss:0.26026206472975066train_loss:0.27954893992114804train_loss:0.1699281856687059train_loss:0.15934689245821898train_loss:0.3161871226226364train_loss:0.10976032096009508train_loss:0.1763696866686196train_loss:0.18580995761265345train_loss:0.1842207131970236train_loss:0.2443475666901613train_loss:0.18738051698673439train_loss:0.22270658116867303train_loss:0.1662389219099242train_loss:0.209158762880929train_loss:0.22983617951577964train_loss:0.2790296623615454train_loss:0.24788172524111998train_loss:0.1293738188409751train_loss:0.1552172413660744train_loss:0.23018276943562502train_loss:0.16189165875684913train_loss:0.24392025522410113train_loss:0.13403840930108568=== epoch:6, train_acc:0.921, test_acc:0.918 ===train_loss:0.1961216529174243train_loss:0.2924197504956213train_loss:0.19465010122753057train_loss:0.28290935332276435train_loss:0.14427638876873242train_loss:0.2566711475334627train_loss:0.167375730919932train_loss:0.3154511081448441train_loss:0.1578877575967train_loss:0.17910954391766404train_loss:0.23884644581690193train_loss:0.09618189067278102train_loss:0.24388882345961582train_loss:0.08541530798998809train_loss:0.06809986906621876train_loss:0.24638946409490692train_loss:0.18927011798228044train_loss:0.09945981596350358train_loss:0.18495019162631973train_loss:0.15258840338866894train_loss:0.19096173442426728train_loss:0.14569967578533724train_loss:0.1841763707949563train_loss:0.0967340944259887train_loss:0.0970240457283082train_loss:0.15266131436990713train_loss:0.11793802844679865train_loss:0.23125882163453734train_loss:0.1540181533866train_loss:0.11575841101176092train_loss:0.1333871420622398train_loss:0.08651040019662394train_loss:0.216125204224472train_loss:0.16165588422959304train_loss:0.27869245421310007train_loss:0.11198243521614289train_loss:0.17313438972459186train_loss:0.17212043609334862train_loss:0.13791897831064198train_loss:0.2267562895570335train_loss:0.10722405971795468train_loss:0.1149995899103652train_loss:0.09703973400039906train_loss:0.2139958338452train_loss:0.17101299029565184train_loss:0.12963329125364453train_loss:0.1946558983682687train_loss:0.15189507558508436train_loss:0.15603991257676963train_loss:0.1894440989591196=== epoch:7, train_acc:0.944, test_acc:0.921 ===train_loss:0.1949166062126958train_loss:0.16660652708551138train_loss:0.11841422215045073train_loss:0.09924967850906151train_loss:0.3562463811267train_loss:0.15198739956171664train_loss:0.23276767408280194train_loss:0.11995565794860409train_loss:0.2166112047955train_loss:0.17637313795453327train_loss:0.172362454787868train_loss:0.20851418734477065train_loss:0.09537001525763981train_loss:0.14146913793087992train_loss:0.2617576866376055train_loss:0.10500607559534571train_loss:0.3396765217711637train_loss:0.08427796011888775train_loss:0.15303614654098532train_loss:0.132821052254927train_loss:0.1154173668832886train_loss:0.12357953723411788train_loss:0.18706847766652746train_loss:0.2688341936588257train_loss:0.16520252414666456train_loss:0.08039280193318782train_loss:0.1178618737147573train_loss:0.1495808236060719train_loss:0.13937468284703372train_loss:0.09823544010832733train_loss:0.1262785713216828train_loss:0.17823790661433755train_loss:0.08725751897376116train_loss:0.1280730814886477train_loss:0.16139747833498747train_loss:0.13856299791286275train_loss:0.11895206801034919train_loss:0.12937502196848547train_loss:0.10080232388997615train_loss:0.1433918613109576train_loss:0.15192895187892305train_loss:0.1648711640447537train_loss:0.15515860320952918train_loss:0.11577427405176502train_loss:0.04991838139950274train_loss:0.16669192227101182train_loss:0.1887594842527train_loss:0.13278044728094665train_loss:0.14462363902692724train_loss:0.12899222057327978=== epoch:8, train_acc:0.953, test_acc:0.929 ===train_loss:0.11614658829052528train_loss:0.1283181306383869train_loss:0.13602630519082037train_loss:0.08820753814622587train_loss:0.16890325196609468train_loss:0.06370471015340015train_loss:0.1380223598283016train_loss:0.10414267340046371train_loss:0.09350530384194355train_loss:0.12745550967245167train_loss:0.08580615867361312train_loss:0.07332708433862614train_loss:0.14091931565454754train_loss:0.0760411000748177train_loss:0.09505745644205849train_loss:0.06360761624213854train_loss:0.0654150073613train_loss:0.12404314553963294train_loss:0.10167160576295751train_loss:0.10616148380778018train_loss:0.1346644429775604train_loss:0.12441423831964894train_loss:0.3573323396268424train_loss:0.24916186199107485train_loss:0.12530529822852685train_loss:0.08754367015669812train_loss:0.07334443956083914train_loss:0.20917550197781243train_loss:0.1847840883495349train_loss:0.1183049487746507train_loss:0.07881905605438366train_loss:0.15063665903727463train_loss:0.17107469503107173train_loss:0.11236219217021456train_loss:0.09393106092285483train_loss:0.06416538395448765train_loss:0.11236854428092079train_loss:0.20945523787716333train_loss:0.08337149369731861train_loss:0.05732487355325358train_loss:0.1570864506321766train_loss:0.18076648840092233train_loss:0.13745138865307854train_loss:0.08714081091649845train_loss:0.1435806754576637train_loss:0.24435407501635567train_loss:0.12994146376471538train_loss:0.15372389864103003train_loss:0.09813508945397395train_loss:0.12535304105848438=== epoch:9, train_acc:0.949, test_acc:0.929 ===train_loss:0.12884389358627435train_loss:0.07230903284506444train_loss:0.13088479970015968train_loss:0.08134419807781099train_loss:0.13741150483980263train_loss:0.11837091458319343train_loss:0.0360333597933849train_loss:0.10086706481279009train_loss:0.07501685865192625train_loss:0.07863162231090925train_loss:0.13702724499254867train_loss:0.08084087775983821train_loss:0.12343541914233253train_loss:0.07850160249109997train_loss:0.09418802616477617train_loss:0.09552050398868868train_loss:0.07673580117804006train_loss:0.026939052951253605train_loss:0.04395589295983649train_loss:0.038031816812409164train_loss:0.06999557624936044train_loss:0.1655966718000311train_loss:0.06368445153357599train_loss:0.04010530475275284train_loss:0.12382479494357689train_loss:0.1641936287301483train_loss:0.18920478194308601train_loss:0.05733130321010137train_loss:0.17698603597887125train_loss:0.10764127802606108train_loss:0.09413680031262134train_loss:0.08907267445559093train_loss:0.15502890698462124train_loss:0.1533752414611575train_loss:0.1510053939835train_loss:0.09968853683767069train_loss:0.0906986479553312train_loss:0.06981896162587345train_loss:0.125922628245562train_loss:0.08376618287979185train_loss:0.05995160730233552train_loss:0.09389935503195222train_loss:0.13350440149583398train_loss:0.09142311542034161train_loss:0.13335311846237471train_loss:0.11711887232469347train_loss:0.044254101034480256train_loss:0.06471555203906754train_loss:0.14891282539205272train_loss:0.883194756923=== epoch:10, train_acc:0.953, test_acc:0.94 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epoch:20, train_acc:0.988, test_acc:0.955 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Final Test Accuracy ===============test_acc:0.959Saved Network Parameters!

DL之CNN：自定义SimpleConvNet【3层 im2col优化】利用mnist数据集实现手写数字识别多分类训练来评估模型