《Web安全之机器学习入门》笔记:第十五章 16.2 循环神经网络RNN识别验证码

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本小节通过tflearn库的RNN算法来识别验证码,对比了DNN与RNN两种算法的准确率。

使用MNIST图集,加载方法如下

X, Y, testX, testY = mnist.load_data(one_hot=True)
def do_DNN(X, Y, testX, testY):
    # Building deep neural network
    input_layer = tflearn.input_data(shape=[None, 784])
    dense1 = tflearn.fully_connected(input_layer, 64, activation='tanh',
                                     regularizer='L2', weight_decay=0.001)
    dropout1 = tflearn.dropout(dense1, 0.8)
    dense2 = tflearn.fully_connected(dropout1, 64, activation='tanh',
                                     regularizer='L2', weight_decay=0.001)
    dropout2 = tflearn.dropout(dense2, 0.8)
    softmax = tflearn.fully_connected(dropout2, 10, activation='softmax')

    # Regression using SGD with learning rate decay and Top-3 accuracy
    sgd = tflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=1000)
    top_k = tflearn.metrics.Top_k(3)
    net = tflearn.regression(softmax, optimizer=sgd, metric=top_k,
                             loss='categorical_crossentropy')

    # Training
    model = tflearn.DNN(net, tensorboard_verbose=0)
    model.fit(X, Y, n_epoch=20, validation_set=(testX, testY),
              show_metric=True, run_id="dense_model")

do_DNN(X, Y, testX, testY)
Training Step: 17200  | total loss: 0.38923 | time: 3.821s
| SGD | epoch: 020 | loss: 0.38923 - top3: 0.9759 | val_loss: 0.11003 - val_top3: 0.9957 -- iter: 55000/55000
def do_rnn(X, Y, testX, testY):
    X = np.reshape(X, (-1, 28, 28))
    testX = np.reshape(testX, (-1, 28, 28))

    net = tflearn.input_data(shape=[None, 28, 28])
    net = tflearn.lstm(net, 128, return_seq=True)
    net = tflearn.lstm(net, 128)
    net = tflearn.fully_connected(net, 10, activation='softmax')
    net = tflearn.regression(net, optimizer='adam',
                         loss='categorical_crossentropy', name="output1")
    model = tflearn.DNN(net, tensorboard_verbose=2)
    model.fit(X, Y, n_epoch=1, validation_set=(testX,testY), show_metric=True,
          snapshot_step=100)

do_rnn(X, Y, testX, testY)
---------------------------------
Run id: NGI5YO
Log directory: /tmp/tflearn_logs/
---------------------------------
Training samples: 55000
Validation samples: 10000
--
Training Step: 1  | time: 2.087s
| Adam | epoch: 001 | loss: 0.00000 - acc: 0.0000 -- iter: 00064/55000
Training Step: 2  | total loss: 2.07202 | time: 2.170s
| Adam | epoch: 001 | loss: 2.07202 - acc: 0.1828 -- iter: 00128/55000
Training Step: 3  | total loss: 2.25508 | time: 2.265s
| Adam | epoch: 001 | loss: 2.25508 - acc: 0.2250 -- iter: 00192/55000
Training Step: 4  | total loss: 2.28581 | time: 2.349s
| Adam | epoch: 001 | loss: 2.28581 - acc: 0.1500 -- iter: 00256/55000
Training Step: 5  | total loss: 2.28283 | time: 2.431s
| Adam | epoch: 001 | loss: 2.28283 - acc: 0.1868 -- iter: 00320/55000
Training Step: 6  | total loss: 2.27338 | time: 2.507s
| Adam | epoch: 001 | loss: 2.27338 - acc: 0.2375 -- iter: 00384/55000
Training Step: 7  | total loss: 2.27075 | time: 2.583s
| Adam | epoch: 001 | loss: 2.27075 - acc: 0.2450 -- iter: 00448/55000
Training Step: 8  | total loss: 2.27322 | time: 2.687s
| Adam | epoch: 001 | loss: 2.27322 - acc: 0.2302 -- iter: 00512/55000
Training Step: 9  | total loss: 2.27180 | time: 2.768s
| Adam | epoch: 001 | loss: 2.27180 - acc: 0.1745 -- iter: 00576/55000
Training Step: 10  | total loss: 2.24697 | time: 2.846s
| Adam | epoch: 001 | loss: 2.24697 - acc: 0.2748 -- iter: 00640/55000
Training Step: 11  | total loss: 2.21987 | time: 2.925s
| Adam | epoch: 001 | loss: 2.21987 - acc: 0.2334 -- iter: 00704/55000
Training Step: 12  | total loss: 2.22415 | time: 3.009s
| Adam | epoch: 001 | loss: 2.22415 - acc: 0.2198 -- iter: 00768/55000
Training Step: 13  | total loss: 2.21162 | time: 3.099s
| Adam | epoch: 001 | loss: 2.21162 - acc: 0.2126 -- iter: 00832/55000
Training Step: 14  | total loss: 2.20106 | time: 3.176s
| Adam | epoch: 001 | loss: 2.20106 - acc: 0.1896 -- iter: 00896/55000
Training Step: 15  | total loss: 2.17609 | time: 3.248s
| Adam | epoch: 001 | loss: 2.17609 - acc: 0.2438 -- iter: 00960/55000
Training Step: 16  | total loss: 2.19087 | time: 3.329s
| Adam | epoch: 001 | loss: 2.19087 - acc: 0.2168 -- iter: 01024/55000
Training Step: 17  | total loss: 2.17489 | time: 3.406s
| Adam | epoch: 001 | loss: 2.17489 - acc: 0.2231 -- iter: 01088/55000
Training Step: 18  | total loss: 2.13640 | time: 3.475s
| Adam | epoch: 001 | loss: 2.13640 - acc: 0.2649 -- iter: 01152/55000
Training Step: 19  | total loss: 2.12010 | time: 3.549s
| Adam | epoch: 001 | loss: 2.12010 - acc: 0.2547 -- iter: 01216/55000
Training Step: 20  | total loss: 2.09065 | time: 3.625s
| Adam | epoch: 001 | loss: 2.09065 - acc: 0.2632 -- iter: 01280/55000
Training Step: 21  | total loss: 2.05633 | time: 3.696s
| Adam | epoch: 001 | loss: 2.05633 - acc: 0.2979 -- iter: 01344/55000
Training Step: 22  | total loss: 2.02618 | time: 3.772s
| Adam | epoch: 001 | loss: 2.02618 - acc: 0.3164 -- iter: 01408/55000
Training Step: 23  | total loss: 2.01173 | time: 3.846s
| Adam | epoch: 001 | loss: 2.01173 - acc: 0.3289 -- iter: 01472/55000
Training Step: 24  | total loss: 2.01153 | time: 3.923s
| Adam | epoch: 001 | loss: 2.01153 - acc: 0.3155 -- iter: 01536/55000
Training Step: 25  | total loss: 1.99772 | time: 3.999s
| Adam | epoch: 001 | loss: 1.99772 - acc: 0.3317 -- iter: 01600/55000
Training Step: 26  | total loss: 1.94960 | time: 4.069s
| Adam | epoch: 001 | loss: 1.94960 - acc: 0.3597 -- iter: 01664/55000
Training Step: 27  | total loss: 1.94797 | time: 4.140s
| Adam | epoch: 001 | loss: 1.94797 - acc: 0.3516 -- iter: 01728/55000
Training Step: 28  | total loss: 1.90517 | time: 4.218s
| Adam | epoch: 001 | loss: 1.90517 - acc: 0.3887 -- iter: 01792/55000
Training Step: 29  | total loss: 1.88715 | time: 4.291s
| Adam | epoch: 001 | loss: 1.88715 - acc: 0.3892 -- iter: 01856/55000
Training Step: 30  | total loss: 1.87837 | time: 4.365s
| Adam | epoch: 001 | loss: 1.87837 - acc: 0.3895 -- iter: 01920/55000
Training Step: 31  | total loss: 1.86977 | time: 4.435s
| Adam | epoch: 001 | loss: 1.86977 - acc: 0.3898 -- iter: 01984/55000
Training Step: 32  | total loss: 1.84277 | time: 4.512s
| Adam | epoch: 001 | loss: 1.84277 - acc: 0.4005 -- iter: 02048/55000
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| Adam | epoch: 001 | loss: 1.80892 - acc: 0.4086 -- iter: 02112/55000
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| Adam | epoch: 001 | loss: 1.74824 - acc: 0.4516 -- iter: 02176/55000
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| Adam | epoch: 001 | loss: 1.76893 - acc: 0.4225 -- iter: 02240/55000
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| Adam | epoch: 001 | loss: 1.73516 - acc: 0.4288 -- iter: 02304/55000
Training Step: 37  | total loss: 1.71662 | time: 4.871s
| Adam | epoch: 001 | loss: 1.71662 - acc: 0.4336 -- iter: 02368/55000
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| Adam | epoch: 001 | loss: 1.71414 - acc: 0.4161 -- iter: 02432/55000
Training Step: 39  | total loss: 1.69260 | time: 5.011s
| Adam | epoch: 001 | loss: 1.69260 - acc: 0.4261 -- iter: 02496/55000
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| Adam | epoch: 001 | loss: 1.67391 - acc: 0.4341 -- iter: 02560/55000
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| Adam | epoch: 001 | loss: 1.66346 - acc: 0.4434 -- iter: 02624/55000
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| Adam | epoch: 001 | loss: 1.66268 - acc: 0.4451 -- iter: 02688/55000
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| Adam | epoch: 001 | loss: 1.66776 - acc: 0.4410 -- iter: 02752/55000
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| Adam | epoch: 001 | loss: 1.61481 - acc: 0.4756 -- iter: 02816/55000
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| Adam | epoch: 001 | loss: 1.59265 - acc: 0.4850 -- iter: 02880/55000
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| Adam | epoch: 001 | loss: 1.59723 - acc: 0.4771 -- iter: 02944/55000
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| Adam | epoch: 001 | loss: 1.57226 - acc: 0.4655 -- iter: 03008/55000
Training Step: 48  | total loss: 1.58012 | time: 5.665s
| Adam | epoch: 001 | loss: 1.58012 - acc: 0.4585 -- iter: 03072/55000
Training Step: 49  | total loss: 1.55036 | time: 5.740s
| Adam | epoch: 001 | loss: 1.55036 - acc: 0.4749 -- iter: 03136/55000
Training Step: 50  | total loss: 1.51742 | time: 5.807s
| Adam | epoch: 001 | loss: 1.51742 - acc: 0.4812 -- iter: 03200/55000
Training Step: 51  | total loss: 1.49791 | time: 5.877s
| Adam | epoch: 001 | loss: 1.49791 - acc: 0.4889 -- iter: 03264/55000
Training Step: 52  | total loss: 1.48200 | time: 5.947s
| Adam | epoch: 001 | loss: 1.48200 - acc: 0.4976 -- iter: 03328/55000
Training Step: 53  | total loss: 1.46454 | time: 6.019s
| Adam | epoch: 001 | loss: 1.46454 - acc: 0.5025 -- iter: 03392/55000
Training Step: 54  | total loss: 1.46626 | time: 6.090s
| Adam | epoch: 001 | loss: 1.46626 - acc: 0.5090 -- iter: 03456/55000
Training Step: 55  | total loss: 1.45979 | time: 6.159s
| Adam | epoch: 001 | loss: 1.45979 - acc: 0.5211 -- iter: 03520/55000
Training Step: 56  | total loss: 1.47702 | time: 6.235s
| Adam | epoch: 001 | loss: 1.47702 - acc: 0.5269 -- iter: 03584/55000
Training Step: 57  | total loss: 1.45760 | time: 6.305s
| Adam | epoch: 001 | loss: 1.45760 - acc: 0.5340 -- iter: 03648/55000
Training Step: 58  | total loss: 1.42167 | time: 6.373s
| Adam | epoch: 001 | loss: 1.42167 - acc: 0.5507 -- iter: 03712/55000
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| Adam | epoch: 001 | loss: 1.42636 - acc: 0.5523 -- iter: 03776/55000
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| Adam | epoch: 001 | loss: 1.39890 - acc: 0.5640 -- iter: 03840/55000
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| Adam | epoch: 001 | loss: 1.40002 - acc: 0.5638 -- iter: 03904/55000
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| Adam | epoch: 001 | loss: 1.40043 - acc: 0.5455 -- iter: 03968/55000
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| Adam | epoch: 001 | loss: 1.38852 - acc: 0.5437 -- iter: 04032/55000
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| Adam | epoch: 001 | loss: 1.37556 - acc: 0.5295 -- iter: 04160/55000
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| Adam | epoch: 001 | loss: 1.12002 - acc: 0.6138 -- iter: 05824/55000
Training Step: 92  | total loss: 1.10262 | time: 9.080s
| Adam | epoch: 001 | loss: 1.10262 - acc: 0.6196 -- iter: 05888/55000
Training Step: 93  | total loss: 1.09626 | time: 9.158s
| Adam | epoch: 001 | loss: 1.09626 - acc: 0.6217 -- iter: 05952/55000
Training Step: 94  | total loss: 1.07897 | time: 9.233s
| Adam | epoch: 001 | loss: 1.07897 - acc: 0.6330 -- iter: 06016/55000
Training Step: 95  | total loss: 1.07810 | time: 9.304s
| Adam | epoch: 001 | loss: 1.07810 - acc: 0.6322 -- iter: 06080/55000
Training Step: 96  | total loss: 1.06633 | time: 9.373s
| Adam | epoch: 001 | loss: 1.06633 - acc: 0.6440 -- iter: 06144/55000
Training Step: 97  | total loss: 1.06578 | time: 9.445s
| Adam | epoch: 001 | loss: 1.06578 - acc: 0.6499 -- iter: 06208/55000
Training Step: 98  | total loss: 1.05860 | time: 9.519s
| Adam | epoch: 001 | loss: 1.05860 - acc: 0.6583 -- iter: 06272/55000
Training Step: 99  | total loss: 1.05952 | time: 9.587s
| Adam | epoch: 001 | loss: 1.05952 - acc: 0.6566 -- iter: 06336/55000
Training Step: 100  | total loss: 1.06813 | time: 12.306s
| Adam | epoch: 001 | loss: 1.06813 - acc: 0.6518 | val_loss: 0.92908 - val_acc: 0.6861 -- iter: 06400/55000
--
Training Step: 101  | total loss: 1.05771 | time: 12.383s
| Adam | epoch: 001 | loss: 1.05771 - acc: 0.6507 -- iter: 06464/55000
Training Step: 102  | total loss: 1.04335 | time: 12.463s
| Adam | epoch: 001 | loss: 1.04335 - acc: 0.6528 -- iter: 06528/55000
Training Step: 103  | total loss: 1.02571 | time: 12.539s
| Adam | epoch: 001 | loss: 1.02571 - acc: 0.6547 -- iter: 06592/55000
Training Step: 104  | total loss: 0.99155 | time: 12.659s
| Adam | epoch: 001 | loss: 0.99155 - acc: 0.6643 -- iter: 06656/55000
Training Step: 105  | total loss: 0.98733 | time: 12.761s
| Adam | epoch: 001 | loss: 0.98733 - acc: 0.6682 -- iter: 06720/55000
Training Step: 106  | total loss: 0.97634 | time: 12.846s
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| Adam | epoch: 001 | loss: 0.89521 - acc: 0.7031 -- iter: 07424/55000
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| Adam | epoch: 001 | loss: 0.60992 - acc: 0.7874 | val_loss: 0.58093 - val_acc: 0.8198 -- iter: 12800/55000
--
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Training Step: 300  | total loss: 0.37637 | time: 36.404s
| Adam | epoch: 001 | loss: 0.37637 - acc: 0.8885 | val_loss: 0.34490 - val_acc: 0.9018 -- iter: 19200/55000
--
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Training Step: 400  | total loss: 0.26747 | time: 47.924s
| Adam | epoch: 001 | loss: 0.26747 - acc: 0.9261 | val_loss: 0.27111 - val_acc: 0.9200 -- iter: 25600/55000
--
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| Adam | epoch: 001 | loss: 0.29700 - acc: 0.9132 | val_loss: 0.24866 - val_acc: 0.9278 -- iter: 32000/55000
--
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--
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| Adam | epoch: 001 | loss: 0.20164 - acc: 0.9382 | val_loss: 0.19836 - val_acc: 0.9394 -- iter: 44800/55000
--
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Training Step: 798  | total loss: 0.12740 | time: 91.048s
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Training Step: 800  | total loss: 0.12542 | time: 93.332s
| Adam | epoch: 001 | loss: 0.12542 - acc: 0.9632 | val_loss: 0.14589 - val_acc: 0.9562 -- iter: 51200/55000
--
Training Step: 801  | total loss: 0.11808 | time: 93.401s
| Adam | epoch: 001 | loss: 0.11808 - acc: 0.9669 -- iter: 51264/55000
Training Step: 802  | total loss: 0.12174 | time: 93.474s
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Training Step: 803  | total loss: 0.11769 | time: 93.550s
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Training Step: 807  | total loss: 0.12184 | time: 93.845s
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Training Step: 853  | total loss: 0.23969 | time: 97.921s
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Training Step: 854  | total loss: 0.22799 | time: 98.007s
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Training Step: 855  | total loss: 0.21135 | time: 98.090s
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Training Step: 856  | total loss: 0.22039 | time: 98.178s
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Training Step: 857  | total loss: 0.20773 | time: 98.260s
| Adam | epoch: 001 | loss: 0.20773 - acc: 0.9381 -- iter: 54848/55000
Training Step: 858  | total loss: 0.20429 | time: 98.338s
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Training Step: 859  | total loss: 0.21189 | time: 98.418s
| Adam | epoch: 001 | loss: 0.21189 - acc: 0.9348 -- iter: 54976/55000
Training Step: 860  | total loss: 0.20062 | time: 100.993s
| Adam | epoch: 001 | loss: 0.20062 - acc: 0.9382 | val_loss: 0.15825 - val_acc: 0.9538 -- iter: 55000/55000
--

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本文为互联网自动采集或经作者授权后发布,本文观点不代表立场,若侵权下架请联系我们删帖处理!文章出自:https://blog.csdn.net/mooyuan/article/details/122768412
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