DL之DNN：利用MultiLayerNet模型【6*100+ReLU+SGD,weight_decay】對(duì)Mnist數(shù)據(jù)集訓(xùn)練來(lái)抑制過(guò)擬合

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目錄

輸出結(jié)果

設(shè)計(jì)思路

核心代碼

更多輸出

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輸出結(jié)果

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設(shè)計(jì)思路

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核心代碼

# weight_decay_lambda = 0 weight_decay_lambda = 0.1for 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

1、MultiLayerNet[6*100+ReLU+SGD]: DIY Overfitting Data Set based on mnist,train_acc VS test_acc

epoch:0, train_acc:0.06, test_acc:0.0834 epoch:1, train_acc:0.1233, test_acc:0.1109 epoch:2, train_acc:0.1467, test_acc:0.1292 epoch:3, train_acc:0.2233, test_acc:0.1717 epoch:4, train_acc:0.2567, test_acc:0.1891 epoch:5, train_acc:0.27, test_acc:0.2181 epoch:6, train_acc:0.31, test_acc:0.229 epoch:7, train_acc:0.32, test_acc:0.24 epoch:8, train_acc:0.3567, test_acc:0.2502 epoch:9, train_acc:0.37, test_acc:0.2651 epoch:10, train_acc:0.3767, test_acc:0.2743 epoch:11, train_acc:0.39, test_acc:0.2833 epoch:12, train_acc:0.3767, test_acc:0.2769 epoch:13, train_acc:0.4067, test_acc:0.295 epoch:14, train_acc:0.4667, test_acc:0.3169 epoch:15, train_acc:0.45, test_acc:0.3213 epoch:16, train_acc:0.5067, test_acc:0.3439 epoch:17, train_acc:0.54, test_acc:0.3593 epoch:18, train_acc:0.5233, test_acc:0.3687 epoch:19, train_acc:0.5367, test_acc:0.3691 epoch:20, train_acc:0.5667, test_acc:0.4051 epoch:21, train_acc:0.5967, test_acc:0.4265 epoch:22, train_acc:0.63, test_acc:0.4477 epoch:23, train_acc:0.6467, test_acc:0.4627 epoch:24, train_acc:0.6567, test_acc:0.4708 epoch:25, train_acc:0.6533, test_acc:0.4896 epoch:26, train_acc:0.66, test_acc:0.5034 epoch:27, train_acc:0.68, test_acc:0.5107 epoch:28, train_acc:0.6833, test_acc:0.5083 epoch:29, train_acc:0.7067, test_acc:0.5244 epoch:30, train_acc:0.7567, test_acc:0.5564 epoch:31, train_acc:0.7333, test_acc:0.5411 epoch:32, train_acc:0.7533, test_acc:0.5698 epoch:33, train_acc:0.7633, test_acc:0.5738 epoch:34, train_acc:0.7833, test_acc:0.5764 epoch:35, train_acc:0.7633, test_acc:0.5863 epoch:36, train_acc:0.7733, test_acc:0.5915 epoch:37, train_acc:0.8067, test_acc:0.608 epoch:38, train_acc:0.81, test_acc:0.6113 epoch:39, train_acc:0.8033, test_acc:0.5922 epoch:40, train_acc:0.8233, test_acc:0.6192 epoch:41, train_acc:0.83, test_acc:0.6203 epoch:42, train_acc:0.8033, test_acc:0.6066 epoch:43, train_acc:0.8333, test_acc:0.6311 epoch:44, train_acc:0.8433, test_acc:0.6273 epoch:45, train_acc:0.85, test_acc:0.6413 epoch:46, train_acc:0.85, test_acc:0.6375 epoch:47, train_acc:0.86, test_acc:0.6352 epoch:48, train_acc:0.8667, test_acc:0.6504 epoch:49, train_acc:0.8767, test_acc:0.6588 epoch:50, train_acc:0.8667, test_acc:0.6592 epoch:51, train_acc:0.89, test_acc:0.6648 epoch:52, train_acc:0.88, test_acc:0.6605 epoch:53, train_acc:0.88, test_acc:0.6654 epoch:54, train_acc:0.8967, test_acc:0.6674 epoch:55, train_acc:0.8967, test_acc:0.6701 epoch:56, train_acc:0.9, test_acc:0.6636 epoch:57, train_acc:0.9, test_acc:0.6755 epoch:58, train_acc:0.9167, test_acc:0.6763 epoch:59, train_acc:0.9133, test_acc:0.6748 epoch:60, train_acc:0.92, test_acc:0.6788 epoch:61, train_acc:0.9033, test_acc:0.6759 epoch:62, train_acc:0.9133, test_acc:0.6747 epoch:63, train_acc:0.9233, test_acc:0.6915 epoch:64, train_acc:0.9267, test_acc:0.687 epoch:65, train_acc:0.92, test_acc:0.6822 epoch:66, train_acc:0.9133, test_acc:0.6827 epoch:67, train_acc:0.92, test_acc:0.6932 epoch:68, train_acc:0.9333, test_acc:0.6976 epoch:69, train_acc:0.94, test_acc:0.6953 epoch:70, train_acc:0.94, test_acc:0.7031 epoch:71, train_acc:0.9367, test_acc:0.6951 epoch:72, train_acc:0.9433, test_acc:0.7036 epoch:73, train_acc:0.9367, test_acc:0.7051 epoch:74, train_acc:0.9433, test_acc:0.706 epoch:75, train_acc:0.95, test_acc:0.707 epoch:76, train_acc:0.9567, test_acc:0.7052 epoch:77, train_acc:0.9433, test_acc:0.6991 epoch:78, train_acc:0.9567, test_acc:0.7121 epoch:79, train_acc:0.9633, test_acc:0.7055 epoch:80, train_acc:0.96, test_acc:0.7088 epoch:81, train_acc:0.9567, test_acc:0.7105 epoch:82, train_acc:0.9633, test_acc:0.7091 epoch:83, train_acc:0.9567, test_acc:0.7159 epoch:84, train_acc:0.9567, test_acc:0.7072 epoch:85, train_acc:0.9633, test_acc:0.7138 epoch:86, train_acc:0.9767, test_acc:0.7127 epoch:87, train_acc:0.9733, test_acc:0.7167 epoch:88, train_acc:0.9733, test_acc:0.7241 epoch:89, train_acc:0.98, test_acc:0.721 epoch:90, train_acc:0.9767, test_acc:0.7202 epoch:91, train_acc:0.9767, test_acc:0.7232 epoch:92, train_acc:0.9833, test_acc:0.717 epoch:93, train_acc:0.9867, test_acc:0.7215 epoch:94, train_acc:0.9867, test_acc:0.7299 epoch:95, train_acc:0.9833, test_acc:0.728 epoch:96, train_acc:0.99, test_acc:0.7223 epoch:97, train_acc:0.9867, test_acc:0.7205 epoch:98, train_acc:0.99, test_acc:0.7287 epoch:99, train_acc:0.9967, test_acc:0.7298 epoch:100, train_acc:0.99, test_acc:0.7288 epoch:101, train_acc:1.0, test_acc:0.7258 epoch:102, train_acc:0.9967, test_acc:0.7274 epoch:103, train_acc:0.9967, test_acc:0.7238 epoch:104, train_acc:1.0, test_acc:0.7275 epoch:105, train_acc:0.9967, test_acc:0.7275 epoch:106, train_acc:1.0, test_acc:0.7209 epoch:107, train_acc:1.0, test_acc:0.7306 epoch:108, train_acc:0.9933, test_acc:0.7267 epoch:109, train_acc:0.9967, test_acc:0.7278 epoch:110, train_acc:1.0, test_acc:0.7306 epoch:111, train_acc:1.0, test_acc:0.7279 epoch:112, train_acc:0.9967, test_acc:0.7326 epoch:113, train_acc:0.9967, test_acc:0.7274 epoch:114, train_acc:0.9967, test_acc:0.7279 epoch:115, train_acc:1.0, test_acc:0.7301 epoch:116, train_acc:1.0, test_acc:0.7296 epoch:117, train_acc:1.0, test_acc:0.7327 epoch:118, train_acc:1.0, test_acc:0.7248 epoch:119, train_acc:1.0, test_acc:0.733 epoch:120, train_acc:1.0, test_acc:0.7286 epoch:121, train_acc:1.0, test_acc:0.7302 epoch:122, train_acc:1.0, test_acc:0.7346 epoch:123, train_acc:1.0, test_acc:0.7309 epoch:124, train_acc:1.0, test_acc:0.7309 epoch:125, train_acc:1.0, test_acc:0.7327 epoch:126, train_acc:1.0, test_acc:0.7353 epoch:127, train_acc:1.0, test_acc:0.7316 epoch:128, train_acc:1.0, test_acc:0.7296 epoch:129, train_acc:1.0, test_acc:0.731 epoch:130, train_acc:1.0, test_acc:0.733 epoch:131, train_acc:1.0, test_acc:0.7331 epoch:132, train_acc:1.0, test_acc:0.732 epoch:133, train_acc:1.0, test_acc:0.7333 epoch:134, train_acc:1.0, test_acc:0.7288 epoch:135, train_acc:1.0, test_acc:0.7347 epoch:136, train_acc:1.0, test_acc:0.7349 epoch:137, train_acc:1.0, test_acc:0.7356 epoch:138, train_acc:1.0, test_acc:0.7308 epoch:139, train_acc:1.0, test_acc:0.7359 epoch:140, train_acc:1.0, test_acc:0.7337 epoch:141, train_acc:1.0, test_acc:0.7355 epoch:142, train_acc:1.0, test_acc:0.7349 epoch:143, train_acc:1.0, test_acc:0.7327 epoch:144, train_acc:1.0, test_acc:0.7344 epoch:145, train_acc:1.0, test_acc:0.7367 epoch:146, train_acc:1.0, test_acc:0.7372 epoch:147, train_acc:1.0, test_acc:0.7353 epoch:148, train_acc:1.0, test_acc:0.7373 epoch:149, train_acc:1.0, test_acc:0.7362 epoch:150, train_acc:1.0, test_acc:0.7366 epoch:151, train_acc:1.0, test_acc:0.7376 epoch:152, train_acc:1.0, test_acc:0.7357 epoch:153, train_acc:1.0, test_acc:0.7341 epoch:154, train_acc:1.0, test_acc:0.7338 epoch:155, train_acc:1.0, test_acc:0.7351 epoch:156, train_acc:1.0, test_acc:0.7339 epoch:157, train_acc:1.0, test_acc:0.7383 epoch:158, train_acc:1.0, test_acc:0.7366 epoch:159, train_acc:1.0, test_acc:0.7376 epoch:160, train_acc:1.0, test_acc:0.7383 epoch:161, train_acc:1.0, test_acc:0.7404 epoch:162, train_acc:1.0, test_acc:0.7373 epoch:163, train_acc:1.0, test_acc:0.7357 epoch:164, train_acc:1.0, test_acc:0.7359 epoch:165, train_acc:1.0, test_acc:0.7392 epoch:166, train_acc:1.0, test_acc:0.7384 epoch:167, train_acc:1.0, test_acc:0.7381 epoch:168, train_acc:1.0, test_acc:0.734 epoch:169, train_acc:1.0, test_acc:0.7352 epoch:170, train_acc:1.0, test_acc:0.7356 epoch:171, train_acc:1.0, test_acc:0.7381 epoch:172, train_acc:1.0, test_acc:0.7384 epoch:173, train_acc:1.0, test_acc:0.7398 epoch:174, train_acc:1.0, test_acc:0.7395 epoch:175, train_acc:1.0, test_acc:0.7413 epoch:176, train_acc:1.0, test_acc:0.7387 epoch:177, train_acc:1.0, test_acc:0.7402 epoch:178, train_acc:1.0, test_acc:0.7378 epoch:179, train_acc:1.0, test_acc:0.7389 epoch:180, train_acc:1.0, test_acc:0.7396 epoch:181, train_acc:1.0, test_acc:0.7375 epoch:182, train_acc:1.0, test_acc:0.7403 epoch:183, train_acc:1.0, test_acc:0.7392 epoch:184, train_acc:1.0, test_acc:0.7382 epoch:185, train_acc:1.0, test_acc:0.7389 epoch:186, train_acc:1.0, test_acc:0.7385 epoch:187, train_acc:1.0, test_acc:0.7385 epoch:188, train_acc:1.0, test_acc:0.7401 epoch:189, train_acc:1.0, test_acc:0.7382 epoch:190, train_acc:1.0, test_acc:0.7401 epoch:191, train_acc:1.0, test_acc:0.7404 epoch:192, train_acc:1.0, test_acc:0.739 epoch:193, train_acc:1.0, test_acc:0.7398 epoch:194, train_acc:1.0, test_acc:0.7411 epoch:195, train_acc:1.0, test_acc:0.7401 epoch:196, train_acc:1.0, test_acc:0.7394 epoch:197, train_acc:1.0, test_acc:0.7415 epoch:198, train_acc:1.0, test_acc:0.7408 epoch:199, train_acc:1.0, test_acc:0.74 epoch:200, train_acc:1.0, test_acc:0.7395

2、MultiLayerNet[6*100+ReLU+SGD,weight_decay]: DIY Overfitting Data Set based on mnist,train_acc VS test_acc

epoch:0, train_acc:0.1067, test_acc:0.1358 epoch:1, train_acc:0.1233, test_acc:0.1302 epoch:2, train_acc:0.1433, test_acc:0.1246 epoch:3, train_acc:0.16, test_acc:0.1227 epoch:4, train_acc:0.17, test_acc:0.1264 epoch:5, train_acc:0.1933, test_acc:0.1408 epoch:6, train_acc:0.2133, test_acc:0.147 epoch:7, train_acc:0.2433, test_acc:0.1677 epoch:8, train_acc:0.2867, test_acc:0.1847 epoch:9, train_acc:0.3333, test_acc:0.2162 epoch:10, train_acc:0.3667, test_acc:0.2291 epoch:11, train_acc:0.4067, test_acc:0.2616 epoch:12, train_acc:0.4367, test_acc:0.2827 epoch:13, train_acc:0.44, test_acc:0.3016 epoch:14, train_acc:0.4733, test_acc:0.32 epoch:15, train_acc:0.4733, test_acc:0.3389 epoch:16, train_acc:0.52, test_acc:0.3503 epoch:17, train_acc:0.5567, test_acc:0.363 epoch:18, train_acc:0.5367, test_acc:0.3748 epoch:19, train_acc:0.5933, test_acc:0.3959 epoch:20, train_acc:0.6033, test_acc:0.4055 epoch:21, train_acc:0.6133, test_acc:0.4226 epoch:22, train_acc:0.62, test_acc:0.4284 epoch:23, train_acc:0.64, test_acc:0.4561 epoch:24, train_acc:0.63, test_acc:0.4728 epoch:25, train_acc:0.6533, test_acc:0.4675 epoch:26, train_acc:0.6433, test_acc:0.4762 epoch:27, train_acc:0.6767, test_acc:0.4792 epoch:28, train_acc:0.6967, test_acc:0.4964 epoch:29, train_acc:0.6833, test_acc:0.489 epoch:30, train_acc:0.71, test_acc:0.504 epoch:31, train_acc:0.7067, test_acc:0.5129 epoch:32, train_acc:0.7167, test_acc:0.5162 epoch:33, train_acc:0.7167, test_acc:0.5191 epoch:34, train_acc:0.72, test_acc:0.5264 epoch:35, train_acc:0.7067, test_acc:0.5359 epoch:36, train_acc:0.6933, test_acc:0.5414 epoch:37, train_acc:0.6933, test_acc:0.5541 epoch:38, train_acc:0.7233, test_acc:0.5613 epoch:39, train_acc:0.7233, test_acc:0.5569 epoch:40, train_acc:0.7267, test_acc:0.5688 epoch:41, train_acc:0.7467, test_acc:0.5641 epoch:42, train_acc:0.75, test_acc:0.5778 epoch:43, train_acc:0.7667, test_acc:0.571 epoch:44, train_acc:0.7767, test_acc:0.5788 epoch:45, train_acc:0.7567, test_acc:0.5662 epoch:46, train_acc:0.7833, test_acc:0.5926 epoch:47, train_acc:0.7933, test_acc:0.6 epoch:48, train_acc:0.8, test_acc:0.6023 epoch:49, train_acc:0.7933, test_acc:0.6004 epoch:50, train_acc:0.8033, test_acc:0.6044 epoch:51, train_acc:0.7767, test_acc:0.596 epoch:52, train_acc:0.8, test_acc:0.5882 epoch:53, train_acc:0.82, test_acc:0.6071 epoch:54, train_acc:0.82, test_acc:0.6031 epoch:55, train_acc:0.8133, test_acc:0.6175 epoch:56, train_acc:0.8067, test_acc:0.6142 epoch:57, train_acc:0.81, test_acc:0.6024 epoch:58, train_acc:0.8333, test_acc:0.6019 epoch:59, train_acc:0.82, test_acc:0.6278 epoch:60, train_acc:0.8167, test_acc:0.6345 epoch:61, train_acc:0.8333, test_acc:0.6403 epoch:62, train_acc:0.8267, test_acc:0.6305 epoch:63, train_acc:0.8167, test_acc:0.6305 epoch:64, train_acc:0.8067, test_acc:0.64 epoch:65, train_acc:0.8167, test_acc:0.6473 epoch:66, train_acc:0.8433, test_acc:0.6434 epoch:67, train_acc:0.84, test_acc:0.6408 epoch:68, train_acc:0.8333, test_acc:0.6393 epoch:69, train_acc:0.8333, test_acc:0.6478 epoch:70, train_acc:0.8433, test_acc:0.6405 epoch:71, train_acc:0.84, test_acc:0.6541 epoch:72, train_acc:0.8433, test_acc:0.6414 epoch:73, train_acc:0.85, test_acc:0.6289 epoch:74, train_acc:0.8433, test_acc:0.6467 epoch:75, train_acc:0.85, test_acc:0.6302 epoch:76, train_acc:0.8433, test_acc:0.6449 epoch:77, train_acc:0.8433, test_acc:0.6508 epoch:78, train_acc:0.8567, test_acc:0.6455 epoch:79, train_acc:0.8433, test_acc:0.6471 epoch:80, train_acc:0.8667, test_acc:0.6641 epoch:81, train_acc:0.8633, test_acc:0.6516 epoch:82, train_acc:0.8567, test_acc:0.6501 epoch:83, train_acc:0.85, test_acc:0.6602 epoch:84, train_acc:0.8533, test_acc:0.6608 epoch:85, train_acc:0.85, test_acc:0.6535 epoch:86, train_acc:0.8533, test_acc:0.6701 epoch:87, train_acc:0.8433, test_acc:0.6629 epoch:88, train_acc:0.8633, test_acc:0.6659 epoch:89, train_acc:0.88, test_acc:0.6699 epoch:90, train_acc:0.86, test_acc:0.6624 epoch:91, train_acc:0.86, test_acc:0.6618 epoch:92, train_acc:0.85, test_acc:0.6644 epoch:93, train_acc:0.8567, test_acc:0.6593 epoch:94, train_acc:0.8633, test_acc:0.6718 epoch:95, train_acc:0.8667, test_acc:0.6734 epoch:96, train_acc:0.87, test_acc:0.6708 epoch:97, train_acc:0.87, test_acc:0.6643 epoch:98, train_acc:0.86, test_acc:0.6616 epoch:99, train_acc:0.8567, test_acc:0.6675 epoch:100, train_acc:0.8533, test_acc:0.6665 epoch:101, train_acc:0.8733, test_acc:0.6718 epoch:102, train_acc:0.8733, test_acc:0.6682 epoch:103, train_acc:0.8633, test_acc:0.6683 epoch:104, train_acc:0.8733, test_acc:0.6705 epoch:105, train_acc:0.8733, test_acc:0.6764 epoch:106, train_acc:0.8733, test_acc:0.6822 epoch:107, train_acc:0.8767, test_acc:0.674 epoch:108, train_acc:0.8633, test_acc:0.6744 epoch:109, train_acc:0.8767, test_acc:0.6698 epoch:110, train_acc:0.86, test_acc:0.6671 epoch:111, train_acc:0.8633, test_acc:0.6772 epoch:112, train_acc:0.8733, test_acc:0.6798 epoch:113, train_acc:0.87, test_acc:0.6808 epoch:114, train_acc:0.8767, test_acc:0.6723 epoch:115, train_acc:0.8933, test_acc:0.6751 epoch:116, train_acc:0.8733, test_acc:0.6779 epoch:117, train_acc:0.87, test_acc:0.6751 epoch:118, train_acc:0.8667, test_acc:0.6751 epoch:119, train_acc:0.8833, test_acc:0.6725 epoch:120, train_acc:0.8733, test_acc:0.6761 epoch:121, train_acc:0.88, test_acc:0.6768 epoch:122, train_acc:0.87, test_acc:0.6777 epoch:123, train_acc:0.8733, test_acc:0.6815 epoch:124, train_acc:0.87, test_acc:0.6835 epoch:125, train_acc:0.8833, test_acc:0.6808 epoch:126, train_acc:0.8633, test_acc:0.6693 epoch:127, train_acc:0.8733, test_acc:0.671 epoch:128, train_acc:0.87, test_acc:0.6723 epoch:129, train_acc:0.8767, test_acc:0.6769 epoch:130, train_acc:0.8767, test_acc:0.6815 epoch:131, train_acc:0.8933, test_acc:0.6834 epoch:132, train_acc:0.89, test_acc:0.6831 epoch:133, train_acc:0.8833, test_acc:0.6838 epoch:134, train_acc:0.87, test_acc:0.6846 epoch:135, train_acc:0.8967, test_acc:0.6797 epoch:136, train_acc:0.8833, test_acc:0.6835 epoch:137, train_acc:0.9033, test_acc:0.6854 epoch:138, train_acc:0.89, test_acc:0.6842 epoch:139, train_acc:0.8833, test_acc:0.6844 epoch:140, train_acc:0.8833, test_acc:0.6848 epoch:141, train_acc:0.88, test_acc:0.6799 epoch:142, train_acc:0.8867, test_acc:0.6839 epoch:143, train_acc:0.88, test_acc:0.6771 epoch:144, train_acc:0.88, test_acc:0.6788 epoch:145, train_acc:0.8867, test_acc:0.6898 epoch:146, train_acc:0.89, test_acc:0.6788 epoch:147, train_acc:0.8867, test_acc:0.685 epoch:148, train_acc:0.8833, test_acc:0.6782 epoch:149, train_acc:0.87, test_acc:0.6819 epoch:150, train_acc:0.89, test_acc:0.6852 epoch:151, train_acc:0.8933, test_acc:0.687 epoch:152, train_acc:0.8867, test_acc:0.6759 epoch:153, train_acc:0.8833, test_acc:0.6887 epoch:154, train_acc:0.8867, test_acc:0.6894 epoch:155, train_acc:0.89, test_acc:0.6785 epoch:156, train_acc:0.8833, test_acc:0.6815 epoch:157, train_acc:0.8833, test_acc:0.6843 epoch:158, train_acc:0.8867, test_acc:0.6871 epoch:159, train_acc:0.8867, 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