DL之DNN：利用MultiLayerNet模型【6*100+ReLU+SGD】對Mnist數據集訓練來理解過擬合現象

導讀
自定義少量的Mnist數據集，利用全連接神經網絡MultiLayerNet模型【6*100+ReLU+SGD】進行訓練，觀察過擬合現象。

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目錄

輸出結果

設計思路

核心代碼

更多輸出

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輸出結果

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設計思路

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核心代碼

for i in range(1000000):batch_mask = np.random.choice(train_size, batch_size)x_batch = x_train[batch_mask]t_batch = t_train[batch_mask]grads = network.gradient(x_batch, t_batch) optimizer.update(network.params, grads) if i % iter_per_epoch == 0: train_acc = network.accuracy(x_train, t_train) test_acc = network.accuracy(x_test, t_test) train_acc_list.append(train_acc)test_acc_list.append(test_acc)print("epoch:" + str(epoch_cnt) + ", train_acc:" + str(float('%.4f' % train_acc)) + ", test_acc:" + str(float('%.4f' % test_acc))) epoch_cnt += 1if epoch_cnt >= max_epochs: #break

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更多輸出

epoch:0, train_acc:0.0733, test_acc:0.0792 epoch:1, train_acc:0.0767, test_acc:0.0878 epoch:2, train_acc:0.0967, test_acc:0.0966 epoch:3, train_acc:0.1, test_acc:0.1016 epoch:4, train_acc:0.1133, test_acc:0.1065 epoch:5, train_acc:0.1167, test_acc:0.1166 epoch:6, train_acc:0.13, test_acc:0.1249 epoch:7, train_acc:0.1567, test_acc:0.1348 epoch:8, train_acc:0.1867, test_acc:0.1441 epoch:9, train_acc:0.2067, test_acc:0.1602 epoch:10, train_acc:0.2333, test_acc:0.1759 epoch:11, train_acc:0.24, test_acc:0.1812 epoch:12, train_acc:0.2567, test_acc:0.1963 epoch:13, train_acc:0.2867, test_acc:0.2161 epoch:14, train_acc:0.31, test_acc:0.2292 epoch:15, train_acc:0.35, test_acc:0.2452 epoch:16, train_acc:0.3567, test_acc:0.2609 epoch:17, train_acc:0.3867, test_acc:0.2678 epoch:18, train_acc:0.4, test_acc:0.2796 epoch:19, train_acc:0.41, test_acc:0.291 epoch:20, train_acc:0.42, test_acc:0.2978 epoch:21, train_acc:0.4267, test_acc:0.3039 epoch:22, train_acc:0.4433, test_acc:0.3122 epoch:23, train_acc:0.4533, test_acc:0.3199 epoch:24, train_acc:0.4633, test_acc:0.3252 epoch:25, train_acc:0.47, test_acc:0.3326 epoch:26, train_acc:0.4733, test_acc:0.3406 epoch:27, train_acc:0.4733, test_acc:0.3506 epoch:28, train_acc:0.4733, test_acc:0.3537 epoch:29, train_acc:0.4867, test_acc:0.3582 epoch:30, train_acc:0.4933, test_acc:0.3583 epoch:31, train_acc:0.4967, test_acc:0.3655 epoch:32, train_acc:0.4933, test_acc:0.3707 epoch:33, train_acc:0.4967, test_acc:0.3722 epoch:34, train_acc:0.5033, test_acc:0.3806 epoch:35, train_acc:0.5133, test_acc:0.3776 epoch:36, train_acc:0.51, test_acc:0.3804 epoch:37, train_acc:0.5167, test_acc:0.3837 epoch:38, train_acc:0.52, test_acc:0.3838 epoch:39, train_acc:0.5167, test_acc:0.3844 epoch:40, train_acc:0.5167, test_acc:0.3933 epoch:41, train_acc:0.5233, test_acc:0.397 epoch:42, train_acc:0.5267, test_acc:0.3967 epoch:43, train_acc:0.5333, test_acc:0.4021 epoch:44, train_acc:0.5267, test_acc:0.3961 epoch:45, train_acc:0.5367, test_acc:0.3997 epoch:46, train_acc:0.54, test_acc:0.4126 epoch:47, train_acc:0.5533, test_acc:0.421 epoch:48, train_acc:0.5533, test_acc:0.4274 epoch:49, train_acc:0.5533, test_acc:0.4246 epoch:50, train_acc:0.5633, test_acc:0.4322 epoch:51, train_acc:0.5667, test_acc:0.4372 epoch:52, train_acc:0.5867, test_acc:0.4544 epoch:53, train_acc:0.6133, test_acc:0.4631 epoch:54, train_acc:0.6167, test_acc:0.475 epoch:55, train_acc:0.6167, test_acc:0.4756 epoch:56, train_acc:0.6267, test_acc:0.4801 epoch:57, train_acc:0.6333, test_acc:0.4822 epoch:58, train_acc:0.62, test_acc:0.4809 epoch:59, train_acc:0.63, test_acc:0.491 epoch:60, train_acc:0.6233, test_acc:0.4939 epoch:61, train_acc:0.6367, test_acc:0.501 epoch:62, train_acc:0.65, test_acc:0.5156 epoch:63, train_acc:0.65, test_acc:0.5192 epoch:64, train_acc:0.65, test_acc:0.518 epoch:65, train_acc:0.6367, test_acc:0.5204 epoch:66, train_acc:0.6667, test_acc:0.527 epoch:67, train_acc:0.6567, test_acc:0.533 epoch:68, train_acc:0.6633, test_acc:0.5384 epoch:69, train_acc:0.6733, test_acc:0.5374 epoch:70, train_acc:0.67, test_acc:0.5365 epoch:71, train_acc:0.69, test_acc:0.5454 epoch:72, train_acc:0.68, test_acc:0.5479 epoch:73, train_acc:0.6833, test_acc:0.553 epoch:74, train_acc:0.6967, test_acc:0.5568 epoch:75, train_acc:0.68, test_acc:0.55 epoch:76, train_acc:0.7, test_acc:0.5567 epoch:77, train_acc:0.71, test_acc:0.5617 epoch:78, train_acc:0.7167, test_acc:0.5705 epoch:79, train_acc:0.73, test_acc:0.5722 epoch:80, train_acc:0.74, test_acc:0.5831 epoch:81, train_acc:0.73, test_acc:0.5778 epoch:82, train_acc:0.7567, test_acc:0.5845 epoch:83, train_acc:0.7533, test_acc:0.587 epoch:84, train_acc:0.75, test_acc:0.5809 epoch:85, train_acc:0.7433, test_acc:0.5869 epoch:86, train_acc:0.7533, test_acc:0.5996 epoch:87, train_acc:0.75, test_acc:0.5963 epoch:88, train_acc:0.7667, test_acc:0.6079 epoch:89, train_acc:0.7733, test_acc:0.6247 epoch:90, train_acc:0.7633, test_acc:0.6152 epoch:91, train_acc:0.79, test_acc:0.6307 epoch:92, train_acc:0.7967, test_acc:0.637 epoch:93, train_acc:0.8033, test_acc:0.6351 epoch:94, train_acc:0.8, test_acc:0.6464 epoch:95, train_acc:0.7967, test_acc:0.6308 epoch:96, train_acc:0.8067, test_acc:0.6406 epoch:97, train_acc:0.8033, test_acc:0.6432 epoch:98, train_acc:0.81, test_acc:0.657 epoch:99, train_acc:0.81, test_acc:0.6523 epoch:100, train_acc:0.8167, test_acc:0.6487 epoch:101, train_acc:0.8033, test_acc:0.6532 epoch:102, train_acc:0.8133, test_acc:0.672 epoch:103, train_acc:0.8233, test_acc:0.6738 epoch:104, train_acc:0.82, test_acc:0.6588 epoch:105, train_acc:0.8167, test_acc:0.659 epoch:106, train_acc:0.82, test_acc:0.6643 epoch:107, train_acc:0.8233, test_acc:0.6696 epoch:108, train_acc:0.8167, test_acc:0.6665 epoch:109, train_acc:0.8133, test_acc:0.6523 epoch:110, train_acc:0.83, test_acc:0.6744 epoch:111, train_acc:0.8267, test_acc:0.6746 epoch:112, train_acc:0.83, test_acc:0.6757 epoch:113, train_acc:0.8267, test_acc:0.6749 epoch:114, train_acc:0.8167, test_acc:0.668 epoch:115, train_acc:0.8267, test_acc:0.6726 epoch:116, train_acc:0.83, test_acc:0.6794 epoch:117, train_acc:0.8167, test_acc:0.6632 epoch:118, train_acc:0.8233, test_acc:0.6599 epoch:119, train_acc:0.8267, test_acc:0.6692 epoch:120, train_acc:0.83, test_acc:0.6695 epoch:121, train_acc:0.8367, test_acc:0.6781 epoch:122, train_acc:0.8333, test_acc:0.6689 epoch:123, train_acc:0.8367, test_acc:0.6789 epoch:124, train_acc:0.8333, test_acc:0.6821 epoch:125, train_acc:0.8367, test_acc:0.6821 epoch:126, train_acc:0.8267, test_acc:0.6742 epoch:127, train_acc:0.8433, test_acc:0.6823 epoch:128, train_acc:0.8367, test_acc:0.6828 epoch:129, train_acc:0.8367, test_acc:0.6864 epoch:130, train_acc:0.84, test_acc:0.674 epoch:131, train_acc:0.84, test_acc:0.676 epoch:132, train_acc:0.83, test_acc:0.6715 epoch:133, train_acc:0.84, test_acc:0.6938 epoch:134, train_acc:0.8333, test_acc:0.7013 epoch:135, train_acc:0.84, test_acc:0.6979 epoch:136, train_acc:0.84, test_acc:0.6822 epoch:137, train_acc:0.84, test_acc:0.6929 epoch:138, train_acc:0.8433, test_acc:0.6921 epoch:139, train_acc:0.8433, test_acc:0.6963 epoch:140, train_acc:0.83, test_acc:0.6976 epoch:141, train_acc:0.84, test_acc:0.6897 epoch:142, train_acc:0.8433, test_acc:0.6994 epoch:143, train_acc:0.8467, test_acc:0.7042 epoch:144, train_acc:0.8567, test_acc:0.6963 epoch:145, train_acc:0.86, test_acc:0.6966 epoch:146, train_acc:0.8533, test_acc:0.6813 epoch:147, train_acc:0.85, test_acc:0.6891 epoch:148, train_acc:0.8667, test_acc:0.6908 epoch:149, train_acc:0.8467, test_acc:0.6719 epoch:150, train_acc:0.85, test_acc:0.6783 epoch:151, train_acc:0.86, test_acc:0.6969 epoch:152, train_acc:0.86, test_acc:0.7071 epoch:153, train_acc:0.8567, test_acc:0.6974 epoch:154, train_acc:0.86, test_acc:0.7009 epoch:155, train_acc:0.86, test_acc:0.6931 epoch:156, train_acc:0.8567, test_acc:0.6946 epoch:157, train_acc:0.86, test_acc:0.7004 epoch:158, train_acc:0.86, test_acc:0.7023 epoch:159, train_acc:0.85, test_acc:0.7054 epoch:160, train_acc:0.8633, test_acc:0.6933 epoch:161, train_acc:0.8667, test_acc:0.6872 epoch:162, train_acc:0.86, test_acc:0.6844 epoch:163, train_acc:0.8567, test_acc:0.6909 epoch:164, train_acc:0.8633, test_acc:0.6884 epoch:165, train_acc:0.87, test_acc:0.7005 epoch:166, train_acc:0.8667, test_acc:0.6926 epoch:167, train_acc:0.8633, test_acc:0.7131 epoch:168, train_acc:0.86, test_acc:0.7068 epoch:169, train_acc:0.87, test_acc:0.7045 epoch:170, train_acc:0.8633, test_acc:0.7027 epoch:171, train_acc:0.87, test_acc:0.6917 epoch:172, train_acc:0.87, test_acc:0.7046 epoch:173, train_acc:0.87, test_acc:0.71 epoch:174, train_acc:0.8767, test_acc:0.714 epoch:175, train_acc:0.87, test_acc:0.6925 epoch:176, train_acc:0.8633, test_acc:0.7112 epoch:177, train_acc:0.8733, test_acc:0.7149 epoch:178, train_acc:0.8567, test_acc:0.7056 epoch:179, train_acc:0.8633, test_acc:0.7149 epoch:180, train_acc:0.8567, test_acc:0.6962 epoch:181, train_acc:0.87, test_acc:0.7011 epoch:182, train_acc:0.8633, test_acc:0.6964 epoch:183, train_acc:0.8667, test_acc:0.6888 epoch:184, train_acc:0.8633, test_acc:0.7118 epoch:185, train_acc:0.8767, test_acc:0.6966 epoch:186, train_acc:0.86, test_acc:0.7009 epoch:187, train_acc:0.88, test_acc:0.7146 epoch:188, train_acc:0.8667, test_acc:0.7047 epoch:189, train_acc:0.8733, test_acc:0.7049 epoch:190, train_acc:0.8767, test_acc:0.7107 epoch:191, train_acc:0.8667, test_acc:0.6961 epoch:192, train_acc:0.8733, test_acc:0.6946 epoch:193, train_acc:0.87, test_acc:0.6967 epoch:194, train_acc:0.88, test_acc:0.712 epoch:195, train_acc:0.8767, test_acc:0.7098 epoch:196, train_acc:0.8667, test_acc:0.7142 epoch:197, train_acc:0.8733, test_acc:0.7018 epoch:198, train_acc:0.87, test_acc:0.7102 epoch:199, train_acc:0.8767, test_acc:0.7044 epoch:200, train_acc:0.8767, test_acc:0.7013

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