TensorFlow实现手写数字识别

矫情吗；* 2022-09-08 00:09 216阅读 0赞

# TensorFlow实现手写数字识别 #

import  tensorflow as tf
    from    tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
    import matplotlib.pyplot as plt

# 设置GPU使用方式
    # 获取GPU列表
    gpus = tf.config.experimental.list_physical_devices('GPU')
    print(gpus)
    if gpus:
      try:
        # 设置GPU为增长式占用
        for gpu in gpus:
          tf.config.experimental.set_memory_growth(gpu, True) 
      except RuntimeError as e:
        # 打印异常
        print(e)

[]

加载数据集

(xs, ys),_ = datasets.mnist.load_data() #自动下载
    print('datasets:', xs.shape, ys.shape, xs.min(), xs.max())
    
    batch_size = 32
    
    xs = tf.convert_to_tensor(xs, dtype=tf.float32) / 255.
    db = tf.data.Dataset.from_tensor_slices((xs,ys))  
    db = db.batch(batch_size).repeat(30) #将数据集分隔成batch_size个batch repeat(30)代表训练30轮

datasets: (60000, 28, 28) (60000,) 0 255

模型构建

# Dense 全连接层
    model = Sequential([layers.Dense(256, activation='relu'), 
                         layers.Dense(128, activation='relu'),
                         layers.Dense(10)])
    model.build(input_shape=(4, 28*28))
    model.summary() #打印模型信息
    
    optimizer = optimizers.SGD(lr=0.01)
    acc_meter = metrics.Accuracy()

Model: "sequential_3"
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    dense_9 (Dense)              multiple                  200960    
    _________________________________________________________________
    dense_10 (Dense)             multiple                  32896     
    _________________________________________________________________
    dense_11 (Dense)             multiple                  1290      
    =================================================================
    Total params: 235,146
    Trainable params: 235,146
    Non-trainable params: 0
    _________________________________________________________________

模型训练

total_acc = []
    total_loss = []
    for step, (x,y) in enumerate(db):
    
        with tf.GradientTape() as tape:
            # 打平操作，[b, 28, 28] => [b, 784]
            x = tf.reshape(x, (-1, 28*28))
            # Step1. 得到模型输出output [b, 784] => [b, 10]
            out = model(x)
            # [b] => [b, 10]
            y_onehot = tf.one_hot(y, depth=10)
            # 计算差的平方和，[b, 10]
            loss = tf.square(out-y_onehot)
            # 计算每个样本的平均误差，[b]
            loss = tf.reduce_sum(loss) / x.shape[0]
    
    
        acc_meter.update_state(tf.argmax(out, axis=1), y)
    
        # 更新梯度
        grads = tape.gradient(loss, model.trainable_variables)
        optimizer.apply_gradients(zip(grads, model.trainable_variables))
    
    
        if step % 200==0:
            total_acc.append(acc_meter.result().numpy())
            total_loss.append(float(loss))
            print(step, 'loss:', float(loss), 'acc:', acc_meter.result().numpy())
            acc_meter.reset_states()

0 loss: 0.15352573990821838 acc: 0.95457846
    200 loss: 0.1597624123096466 acc: 0.95765626
    400 loss: 0.1545962393283844 acc: 0.9475
    600 loss: 0.1637483835220337 acc: 0.94578123
    800 loss: 0.15958964824676514 acc: 0.9476563
    1000 loss: 0.20374062657356262 acc: 0.94078124
    1200 loss: 0.18700677156448364 acc: 0.94921875
    1400 loss: 0.15020117163658142 acc: 0.95140624
    1600 loss: 0.14052757620811462 acc: 0.9421875
    1800 loss: 0.15347686409950256 acc: 0.95109373
    2000 loss: 0.15199950337409973 acc: 0.965
    2200 loss: 0.08358028531074524 acc: 0.95390624
    2400 loss: 0.16466480493545532 acc: 0.95484376
    2600 loss: 0.14589539170265198 acc: 0.954375
    2800 loss: 0.10357476770877838 acc: 0.95203125
    3000 loss: 0.1552669256925583 acc: 0.95109373
    3200 loss: 0.13997772336006165 acc: 0.95640624
    3400 loss: 0.10184890031814575 acc: 0.9504688
    3600 loss: 0.0966319739818573 acc: 0.950625
    3800 loss: 0.14316204190254211 acc: 0.9660938
    4000 loss: 0.14158621430397034 acc: 0.9660938
    4200 loss: 0.11101004481315613 acc: 0.9559375
    4400 loss: 0.10630815476179123 acc: 0.96046877
    4600 loss: 0.1794758141040802 acc: 0.95484376
    4800 loss: 0.10882129520177841 acc: 0.9557812
    5000 loss: 0.11497728526592255 acc: 0.96125
    5200 loss: 0.20665663480758667 acc: 0.9559375
    5400 loss: 0.22054731845855713 acc: 0.95390624
    5600 loss: 0.08281977474689484 acc: 0.96828127
    5800 loss: 0.1415213942527771 acc: 0.9696875
    6000 loss: 0.10287574678659439 acc: 0.96234375
    6200 loss: 0.14125148952007294 acc: 0.959375
    6400 loss: 0.0872105211019516 acc: 0.96296877
    6600 loss: 0.09639552235603333 acc: 0.95890623
    6800 loss: 0.09451914578676224 acc: 0.96203125
    7000 loss: 0.09597232937812805 acc: 0.96515626
    7200 loss: 0.27553442120552063 acc: 0.95390624
    7400 loss: 0.11118196696043015 acc: 0.9635937
    7600 loss: 0.13853389024734497 acc: 0.9742187
    7800 loss: 0.0864826887845993 acc: 0.96765625
    8000 loss: 0.12683367729187012 acc: 0.9657813
    8200 loss: 0.0835743099451065 acc: 0.96671873
    8400 loss: 0.07526201754808426 acc: 0.96203125
    8600 loss: 0.08792373538017273 acc: 0.9634375
    8800 loss: 0.1303400695323944 acc: 0.96625
    9000 loss: 0.11500948667526245 acc: 0.9615625
    9200 loss: 0.07716777920722961 acc: 0.96203125
    9400 loss: 0.06136278063058853 acc: 0.9740625
    9600 loss: 0.16090892255306244 acc: 0.97375
    9800 loss: 0.050481319427490234 acc: 0.96796876
    10000 loss: 0.12725922465324402 acc: 0.9678125
    10200 loss: 0.10010596364736557 acc: 0.9653125
    10400 loss: 0.12236626446247101 acc: 0.964375
    10600 loss: 0.06595691293478012 acc: 0.97015625
    10800 loss: 0.16140709817409515 acc: 0.96734375
    11000 loss: 0.07903225719928741 acc: 0.96140623
    11200 loss: 0.09545521438121796 acc: 0.97078127
    11400 loss: 0.08132430166006088 acc: 0.97578126
    11600 loss: 0.14214324951171875 acc: 0.9703125
    11800 loss: 0.0826168805360794 acc: 0.96921873
    12000 loss: 0.07449790835380554 acc: 0.96875
    12200 loss: 0.052427180111408234 acc: 0.9684375
    12400 loss: 0.10587689280509949 acc: 0.96984375
    12600 loss: 0.14333687722682953 acc: 0.96921873
    12800 loss: 0.1134927049279213 acc: 0.9664062
    13000 loss: 0.07688809186220169 acc: 0.96875
    13200 loss: 0.1413525640964508 acc: 0.9767187
    13400 loss: 0.09582379460334778 acc: 0.97484374
    13600 loss: 0.10331477224826813 acc: 0.97046876
    13800 loss: 0.1101207584142685 acc: 0.971875
    14000 loss: 0.05487038940191269 acc: 0.96875
    14200 loss: 0.1763731837272644 acc: 0.9715625
    14400 loss: 0.08923567831516266 acc: 0.970625
    14600 loss: 0.14577150344848633 acc: 0.96953124
    14800 loss: 0.07163411378860474 acc: 0.966875
    15000 loss: 0.09502746164798737 acc: 0.97828126
    15200 loss: 0.08452443033456802 acc: 0.97625
    15400 loss: 0.09686124324798584 acc: 0.97328126
    15600 loss: 0.0802001953125 acc: 0.97296876
    15800 loss: 0.10131652653217316 acc: 0.9728125
    16000 loss: 0.12712924182415009 acc: 0.97015625
    16200 loss: 0.10623563826084137 acc: 0.973125
    16400 loss: 0.07512909919023514 acc: 0.97484374
    16600 loss: 0.08629828691482544 acc: 0.9675
    16800 loss: 0.09165250509977341 acc: 0.97328126
    17000 loss: 0.08541638404130936 acc: 0.9796875
    17200 loss: 0.03573321923613548 acc: 0.9739063
    17400 loss: 0.08292931318283081 acc: 0.97515625
    17600 loss: 0.09117152541875839 acc: 0.9746875
    17800 loss: 0.055078715085983276 acc: 0.97265625
    18000 loss: 0.07413016259670258 acc: 0.9742187
    18200 loss: 0.10115832090377808 acc: 0.9742187
    18400 loss: 0.06299661844968796 acc: 0.9728125
    18600 loss: 0.056077420711517334 acc: 0.97046876
    18800 loss: 0.08558100461959839 acc: 0.97859377
    19000 loss: 0.0742747038602829 acc: 0.9803125
    19200 loss: 0.06931197643280029 acc: 0.9734375
    19400 loss: 0.07148833572864532 acc: 0.9767187
    19600 loss: 0.11700235307216644 acc: 0.9739063
    19800 loss: 0.06992246210575104 acc: 0.9735938
    20000 loss: 0.05687614157795906 acc: 0.9759375
    20200 loss: 0.14907342195510864 acc: 0.97453123
    20400 loss: 0.1653566211462021 acc: 0.97265625
    20600 loss: 0.03767940402030945 acc: 0.9795312
    20800 loss: 0.07523676753044128 acc: 0.9792187
    21000 loss: 0.07417837530374527 acc: 0.9765625
    21200 loss: 0.08299359679222107 acc: 0.9764063
    21400 loss: 0.04976201429963112 acc: 0.97765625
    21600 loss: 0.04690690338611603 acc: 0.975
    21800 loss: 0.04981674998998642 acc: 0.9746875
    22000 loss: 0.0513470321893692 acc: 0.97828126
    22200 loss: 0.22548101842403412 acc: 0.971875
    22400 loss: 0.07231388241052628 acc: 0.97625
    22600 loss: 0.09100791811943054 acc: 0.9828125
    22800 loss: 0.06141120195388794 acc: 0.97796875
    23000 loss: 0.0918603241443634 acc: 0.9795312
    23200 loss: 0.04934592917561531 acc: 0.97796875
    23400 loss: 0.046293482184410095 acc: 0.97578126
    23600 loss: 0.055974338203668594 acc: 0.97546875
    23800 loss: 0.10047663748264313 acc: 0.97703123
    24000 loss: 0.0761006623506546 acc: 0.9765625
    24200 loss: 0.03693201392889023 acc: 0.9742187
    24400 loss: 0.03466993570327759 acc: 0.9821875
    24600 loss: 0.10374853014945984 acc: 0.981875
    24800 loss: 0.026225175708532333 acc: 0.98
    25000 loss: 0.10136633366346359 acc: 0.9784375
    25200 loss: 0.07362035661935806 acc: 0.978125
    25400 loss: 0.10292142629623413 acc: 0.97453123
    25600 loss: 0.03838391602039337 acc: 0.9784375
    25800 loss: 0.1306307017803192 acc: 0.9790625
    26000 loss: 0.056037187576293945 acc: 0.97546875
    26200 loss: 0.06480588018894196 acc: 0.9803125
    26400 loss: 0.0460970476269722 acc: 0.98265624
    26600 loss: 0.11328999698162079 acc: 0.98078126
    26800 loss: 0.052961524575948715 acc: 0.9809375
    27000 loss: 0.046149395406246185 acc: 0.9784375
    27200 loss: 0.03660104423761368 acc: 0.97859377
    27400 loss: 0.07231131196022034 acc: 0.97796875
    27600 loss: 0.0969608873128891 acc: 0.97859377
    27800 loss: 0.07953917980194092 acc: 0.978125
    28000 loss: 0.05938175320625305 acc: 0.97765625
    28200 loss: 0.10404374450445175 acc: 0.984375
    28400 loss: 0.07486425340175629 acc: 0.98328125
    28600 loss: 0.06746675819158554 acc: 0.98078126
    28800 loss: 0.10037437081336975 acc: 0.98078126
    29000 loss: 0.04371890425682068 acc: 0.9790625
    29200 loss: 0.1544964462518692 acc: 0.97984374
    29400 loss: 0.06675610691308975 acc: 0.9795312
    29600 loss: 0.11682221293449402 acc: 0.98046875
    29800 loss: 0.055132970213890076 acc: 0.97765625
    30000 loss: 0.06676920503377914 acc: 0.9845312
    30200 loss: 0.061399221420288086 acc: 0.984375
    30400 loss: 0.0784747451543808 acc: 0.981875
    30600 loss: 0.05704239010810852 acc: 0.981875
    30800 loss: 0.06897450983524323 acc: 0.98078126
    31000 loss: 0.09554265439510345 acc: 0.9792187
    31200 loss: 0.07846721261739731 acc: 0.97984374
    31400 loss: 0.05001729726791382 acc: 0.9828125
    31600 loss: 0.06418764591217041 acc: 0.978125
    31800 loss: 0.06677256524562836 acc: 0.98078126
    32000 loss: 0.06099291145801544 acc: 0.98609376
    32200 loss: 0.02437475323677063 acc: 0.9825
    32400 loss: 0.054472584277391434 acc: 0.98296875
    32600 loss: 0.07025627791881561 acc: 0.9821875
    32800 loss: 0.043403904885053635 acc: 0.980625
    33000 loss: 0.05405097454786301 acc: 0.9817188
    33200 loss: 0.07893810421228409 acc: 0.9814063
    33400 loss: 0.04618637263774872 acc: 0.9815625
    33600 loss: 0.03955160081386566 acc: 0.9784375
    33800 loss: 0.06573085486888885 acc: 0.985
    34000 loss: 0.05302225425839424 acc: 0.9859375
    34200 loss: 0.04989982396364212 acc: 0.9828125
    34400 loss: 0.06038925424218178 acc: 0.9834375
    34600 loss: 0.08686563372612 acc: 0.98234373
    34800 loss: 0.05421433597803116 acc: 0.9809375
    35000 loss: 0.04355776682496071 acc: 0.9828125
    35200 loss: 0.12376898527145386 acc: 0.98125
    35400 loss: 0.12621557712554932 acc: 0.98078126
    35600 loss: 0.025318723171949387 acc: 0.985
    35800 loss: 0.047489866614341736 acc: 0.9842188
    36000 loss: 0.06456907093524933 acc: 0.98390627
    36200 loss: 0.05885958671569824 acc: 0.9842188
    36400 loss: 0.038796693086624146 acc: 0.9834375
    36600 loss: 0.028620203956961632 acc: 0.9820312
    36800 loss: 0.03317582234740257 acc: 0.9814063
    37000 loss: 0.03372523933649063 acc: 0.9834375
    37200 loss: 0.18894273042678833 acc: 0.97859377
    37400 loss: 0.06384888291358948 acc: 0.98234373
    37600 loss: 0.07211901247501373 acc: 0.9870312
    37800 loss: 0.05046132951974869 acc: 0.9846875
    38000 loss: 0.07783858478069305 acc: 0.985625
    38200 loss: 0.03523237258195877 acc: 0.9846875
    38400 loss: 0.03408464789390564 acc: 0.9821875
    38600 loss: 0.044989801943302155 acc: 0.9828125
    38800 loss: 0.07720053195953369 acc: 0.98296875
    39000 loss: 0.05239255353808403 acc: 0.9817188
    39200 loss: 0.026881976053118706 acc: 0.98078126
    39400 loss: 0.022131649777293205 acc: 0.98609376
    39600 loss: 0.0717465877532959 acc: 0.9865625
    39800 loss: 0.020211482420563698 acc: 0.98484373
    40000 loss: 0.09074624627828598 acc: 0.985
    40200 loss: 0.05809881165623665 acc: 0.98515624
    40400 loss: 0.08850902318954468 acc: 0.981875
    40600 loss: 0.02669745683670044 acc: 0.9842188
    40800 loss: 0.11308488249778748 acc: 0.9834375
    41000 loss: 0.0438566729426384 acc: 0.9809375
    41200 loss: 0.04586176574230194 acc: 0.9846875
    41400 loss: 0.036804813891649246 acc: 0.98625
    41600 loss: 0.09279486536979675 acc: 0.98546875
    41800 loss: 0.03883099555969238 acc: 0.9867188
    42000 loss: 0.033931516110897064 acc: 0.985
    42200 loss: 0.03281283751130104 acc: 0.9840625
    42400 loss: 0.05394122377038002 acc: 0.9834375
    42600 loss: 0.0685659870505333 acc: 0.983125
    42800 loss: 0.058388061821460724 acc: 0.98328125
    43000 loss: 0.05259823054075241 acc: 0.9825
    43200 loss: 0.0837898999452591 acc: 0.986875
    43400 loss: 0.06377860903739929 acc: 0.9870312
    43600 loss: 0.04784414917230606 acc: 0.98640627
    43800 loss: 0.08819957822561264 acc: 0.98640627
    44000 loss: 0.033343978226184845 acc: 0.98390627
    44200 loss: 0.1391856074333191 acc: 0.98609376
    44400 loss: 0.05300295725464821 acc: 0.98484373
    44600 loss: 0.1040736585855484 acc: 0.98359376
    44800 loss: 0.041021399199962616 acc: 0.9817188
    45000 loss: 0.050329744815826416 acc: 0.9884375
    45200 loss: 0.04664241150021553 acc: 0.9871875
    45400 loss: 0.06599676609039307 acc: 0.98640627
    45600 loss: 0.04718659818172455 acc: 0.98765624
    45800 loss: 0.04680747911334038 acc: 0.98609376
    46000 loss: 0.0784095823764801 acc: 0.9845312
    46200 loss: 0.062429361045360565 acc: 0.98625
    46400 loss: 0.03825797885656357 acc: 0.985
    46600 loss: 0.04770933836698532 acc: 0.983125
    46800 loss: 0.05306899920105934 acc: 0.985625
    47000 loss: 0.0469331368803978 acc: 0.98796874
    47200 loss: 0.018814777955412865 acc: 0.9867188
    47400 loss: 0.04082533344626427 acc: 0.988125
    47600 loss: 0.05882826820015907 acc: 0.98640627
    47800 loss: 0.035880930721759796 acc: 0.98609376
    48000 loss: 0.04324483126401901 acc: 0.98640627
    48200 loss: 0.06581659615039825 acc: 0.98515624
    48400 loss: 0.038081344217061996 acc: 0.9853125
    48600 loss: 0.032461464405059814 acc: 0.98296875
    48800 loss: 0.05362715572118759 acc: 0.98796874
    49000 loss: 0.04097782447934151 acc: 0.989375
    49200 loss: 0.038341302424669266 acc: 0.9867188
    49400 loss: 0.05667734891176224 acc: 0.9871875
    49600 loss: 0.06505107134580612 acc: 0.98625
    49800 loss: 0.048090700060129166 acc: 0.9867188
    50000 loss: 0.038220785558223724 acc: 0.986875
    50200 loss: 0.10568118095397949 acc: 0.985
    50400 loss: 0.10147371888160706 acc: 0.985
    50600 loss: 0.02043035626411438 acc: 0.98890626
    50800 loss: 0.03445787355303764 acc: 0.9871875
    51000 loss: 0.05728784203529358 acc: 0.9871875
    51200 loss: 0.04533722251653671 acc: 0.98796874
    51400 loss: 0.03492813929915428 acc: 0.988125
    51600 loss: 0.020884167402982712 acc: 0.986875
    51800 loss: 0.025441624224185944 acc: 0.985625
    52000 loss: 0.026575148105621338 acc: 0.98640627
    52200 loss: 0.16384227573871613 acc: 0.9842188
    52400 loss: 0.05616907402873039 acc: 0.9867188
    52600 loss: 0.059886958450078964 acc: 0.98890626
    52800 loss: 0.04272538423538208 acc: 0.98796874
    53000 loss: 0.06959083676338196 acc: 0.9884375
    53200 loss: 0.026869554072618484 acc: 0.9878125
    53400 loss: 0.027034994214773178 acc: 0.98640627
    53600 loss: 0.03967958688735962 acc: 0.98796874
    53800 loss: 0.060415420681238174 acc: 0.98546875
    54000 loss: 0.03867259994149208 acc: 0.98640627
    54200 loss: 0.021209895610809326 acc: 0.9845312
    54400 loss: 0.017180444672703743 acc: 0.9892188
    54600 loss: 0.0526532307267189 acc: 0.98890626
    54800 loss: 0.015818050131201744 acc: 0.9878125
    55000 loss: 0.08110405504703522 acc: 0.98859376
    55200 loss: 0.04867621138691902 acc: 0.9884375
    55400 loss: 0.07329011708498001 acc: 0.9867188
    55600 loss: 0.0186185110360384 acc: 0.9875
    55800 loss: 0.09356825053691864 acc: 0.98640627
    56000 loss: 0.034789077937603 acc: 0.98484373
    56200 loss: 0.03775575011968613 acc: 0.9878125

x = [i for i in range(0, len(total_loss))]
    plt.plot(x,total_loss)

[<matplotlib.lines.Line2D at 0x7f8643ca4290>]

![png][]

x = [i for i in range(0, len(total_acc))]
    plt.plot(x,total_acc)

[<matplotlib.lines.Line2D at 0x7f8643e6c2d0>]

![png][png 1]

[png]: /images/20220829/5da74285027a421a8bc253ff136fd1dc.png
[png 1]: /images/20220829/cc8b1fa200dc41d0a3f54745d722236e.png