DL之DNN:利用MultiLayerNet模型【6*100+ReLU+SGD】对Mnist数据集训练来理解过拟合现象
导读
自定义少量的Mnist数据集,利用全连接神经网络MultiLayerNet模型【6*100+ReLU+SGD】进行训练,观察过拟合现象。
目录
- 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 += 1
- if epoch_cnt >= max_epochs:
- break
- 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:
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