\ud6a8\uc728\uc801\uc778 \uc790\uc6d0 \uc0ac\uc6a9: \ucef4\ud4e8\ud305 \uc790\uc6d0\uc740 \uc81c\ud55c\uc801\uc774\ubbc0\ub85c \ud6a8\uc728\uc801\uc778 \uc0ac\uc6a9\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n2. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ucd5c\uc801\ud654<\/h2>\n
\ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub294 \ubaa8\ub378 \ud559\uc2b5 \uacfc\uc815\uc5d0\uc11c \uc124\uc815\ud574\uc57c \ud558\ub294 \ud30c\ub77c\ubbf8\ud130\ub85c, \uc608\ub97c \ub4e4\uc5b4 \ud559\uc2b5\ub960, \ubc30\uce58 \ud06c\uae30, \uc5d0\ud3ec\ud06c \uc218 \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub4e4\uc744 \ucd5c\uc801\ud654\ud558\uba74 \uc131\ub2a5\uc5d0 \ud070 \uc601\ud5a5\uc744 \ubbf8\uce60 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ucd5c\uc801\ud654\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc5d0\ub294 \uc5ec\ub7ec \uac00\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
2.1. \uadf8\ub9ac\ub4dc \uc11c\uce58(Grid Search)<\/h3>\n
\uadf8\ub9ac\ub4dc \uc11c\uce58\ub294 \uc5ec\ub7ec \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\ud569\uc744 \uccb4\uacc4\uc801\uc73c\ub85c \ud0d0\uc0c9\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \uac04\ub2e8\ud558\uc9c0\ub9cc, \uacc4\uc0b0 \ube44\uc6a9\uc774 \ub9ce\uc774 \ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uadf8\ub9ac\ub4dc \uc11c\uce58\ub97c \ud30c\uc774\uc36c\uc73c\ub85c \uad6c\ud604\ud558\ub294 \uc608\uc2dc\uc785\ub2c8\ub2e4:<\/p>\n
import itertools\nimport torch.optim as optim\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uacf5\uac04 \uc815\uc758\nlearning_rates = [0.001, 0.01]\nbatch_sizes = [16, 32]\n\n# \uadf8\ub9ac\ub4dc \uc11c\uce58 \uc218\ud589\nfor lr, batch_size in itertools.product(learning_rates, batch_sizes):\n model = MyModel() # \ubaa8\ub378 \ucd08\uae30\ud654\n optimizer = optim.Adam(model.parameters(), lr=lr)\n train(model, optimizer, batch_size) # \ud6c8\ub828 \ud568\uc218 \ud638\ucd9c\n accuracy = evaluate(model) # \ubaa8\ub378 \ud3c9\uac00\n print(f'Learning Rate: {lr}, Batch Size: {batch_size}, Accuracy: {accuracy}')\n<\/code><\/pre>\n2.2. \ub79c\ub364 \uc11c\uce58(Random Search)<\/h3>\n
\ub79c\ub364 \uc11c\uce58\ub294 \ubb34\uc791\uc704\ub85c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc120\ud0dd\ud574 \ud0d0\uc0c9\ud558\ub294 \ubc29\ubc95\uc73c\ub85c, \uadf8\ub9ac\ub4dc \uc11c\uce58\ubcf4\ub2e4 \ub354 \ub2e4\uc591\ud55c \uc870\ud569\uc744 \uc2dc\ub3c4\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \ub79c\ub364 \uc11c\uce58\uc758 \uc608\uc2dc\uc785\ub2c8\ub2e4:<\/p>\n
import random\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uacf5\uac04 \uc815\uc758\nlearning_rates = [0.001, 0.01, 0.1]\nbatch_sizes = [16, 32, 64]\n\n# \ub79c\ub364 \uc11c\uce58 \uc218\ud589\nfor _ in range(10):\n lr = random.choice(learning_rates)\n batch_size = random.choice(batch_sizes)\n model = MyModel() # \ubaa8\ub378 \ucd08\uae30\ud654\n optimizer = optim.Adam(model.parameters(), lr=lr)\n train(model, optimizer, batch_size) # \ud6c8\ub828 \ud568\uc218 \ud638\ucd9c\n accuracy = evaluate(model) # \ubaa8\ub378 \ud3c9\uac00\n print(f'Learning Rate: {lr}, Batch Size: {batch_size}, Accuracy: {accuracy}')\n<\/code><\/pre>\n2.3. \ubca0\uc774\uc9c0\uc548 \ucd5c\uc801\ud654(Bayesian Optimization)<\/h3>\n
\ubca0\uc774\uc9c0\uc548 \ucd5c\uc801\ud654\ub294 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\uc758 \ud655\ub960\uc801 \ubaa8\ub378\uc744 \uc774\uc6a9\ud558\uc5ec \ucd5c\uc801\ud654\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \ud6a8\uc728\uc801\uc778 \ud0d0\uc0c9\uc744 \ud1b5\ud574 \uc131\ub2a5 \uac1c\uc120\uc744 \uc774\ub8f0 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\uc640 \ud568\uaed8 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c\ub294 optuna<\/code>\uac00 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\nimport optuna\n\ndef objective(trial):\n lr = trial.suggest_loguniform('lr', 1e-5, 1e-1)\n batch_size = trial.suggest_int('batch_size', 16, 64)\n model = MyModel()\n optimizer = optim.Adam(model.parameters(), lr=lr)\n train(model, optimizer, batch_size)\n return evaluate(model)\n\nstudy = optuna.create_study(direction='maximize')\nstudy.optimize(objective, n_trials=100)\nprint(study.best_params)\n<\/code><\/pre>\n3. \ubaa8\ub378 \uad6c\uc870 \ucd5c\uc801\ud654<\/h2>\n
\ubaa8\ub378\uc758 \uad6c\uc870\ub97c \ucd5c\uc801\ud654\ud558\ub294 \uac83\uc740 \uc131\ub2a5 \ud5a5\uc0c1\uc5d0 \ud070 \uae30\uc5ec\ub97c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uba87 \uac00\uc9c0 \ubc29\ubc95\uc785\ub2c8\ub2e4:<\/p>\n
3.1. \ub124\ud2b8\uc6cc\ud06c \uae4a\uc774 \uc870\uc808<\/h3>\n
\ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \uce35\uc774 \ub9ce\uc744\uc218\ub85d \ubcf5\uc7a1\ud55c \ud568\uc218 \uadfc\uc0ac\uac00 \uac00\ub2a5\ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \ub108\ubb34 \uae4a\uc740 \ub124\ud2b8\uc6cc\ud06c\ub294 \uacfc\uc801\ud569\uacfc \uae30\uc6b8\uae30 \uc18c\uc2e4 \ubb38\uc81c\ub97c \ucd08\ub798\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc801\uc808\ud55c \uae4a\uc774\ub97c \ucc3e\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n
3.2. \uce35\uc758 \uc218 \uc870\uc815<\/h3>\n
Dense, Convolutional, Recurrent \uce35 \ub4f1 \ub2e4\uc591\ud55c \uce35\uc744 \uc801\uc6a9\ud558\uc5ec \uc131\ub2a5\uc744 \uc99d\uac00\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac01 \uce35\uc758 \ub178\ub4dc \uc218\uc640 \ud65c\uc131\ud654 \ud568\uc218\ub97c \uc870\uc808\ud558\uc5ec \ubaa8\ub378 \uad6c\uc870\ub97c \ucd5c\uc801\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch.nn as nn\n\nclass MyOptimizedModel(nn.Module):\n def __init__(self):\n super(MyOptimizedModel, self).__init__()\n self.layer1 = nn.Linear(784, 256) # \uc778\ud48b 784, \uc544\uc6c3\ud48b 256\n self.layer2 = nn.ReLU()\n self.layer3 = nn.Linear(256, 128)\n self.layer4 = nn.ReLU()\n self.output_layer = nn.Linear(128, 10) # \ucd5c\uc885 \uc544\uc6c3\ud48b \ud074\ub798\uc2a4 \uc218\n\n def forward(self, x):\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n return self.output_layer(x)\n<\/code><\/pre>\n4. \uc815\uaddc\ud654 \uae30\ubc95\uacfc \ub4dc\ub86d\uc544\uc6c3<\/h2>\n
\uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\uae30 \uc704\ud574 \uc5ec\ub7ec \uc815\uaddc\ud654 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub4dc\ub86d\uc544\uc6c3\uc740 \uce35\uc758 \uc77c\ubd80 \ub274\ub7f0\uc744 \ubb34\uc791\uc704\ub85c \ube44\ud65c\uc131\ud654\ud558\uc5ec \ud6c8\ub828\ud558\ub294 \uae30\ubc95\uc73c\ub85c, \uacfc\uc801\ud569\uc744 \uc904\uc774\ub294 \ub370 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4.<\/p>\n
class MyModelWithDropout(nn.Module):\n def __init__(self):\n super(MyModelWithDropout, self).__init__()\n self.layer1 = nn.Linear(784, 256)\n self.dropout = nn.Dropout(0.5) # 50% \ub4dc\ub86d\uc544\uc6c3 \uc801\uc6a9\n self.output_layer = nn.Linear(256, 10)\n\n def forward(self, x):\n x = self.layer1(x)\n x = self.dropout(x) # \ub4dc\ub86d\uc544\uc6c3 \uc801\uc6a9\n return self.output_layer(x)\n<\/code><\/pre>\n5. Optimizer \ubc0f Learning Rate \uc870\uc815<\/h2>\n
\ud30c\uc774\ud1a0\uce58\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ub2e4\uc591\ud55c \uc635\ud2f0\ub9c8\uc774\uc800\uc640 \ud559\uc2b5\ub960 \uc870\uc815 \uae30\ubc95\uc740 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud558\ub294 \ub370 \ud070 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \ub300\ud45c\uc801\uc778 \uc635\ud2f0\ub9c8\uc774\uc800\ub294 SGD, Adam, RMSprop \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5.1. Adaptive Learning Rate<\/h3>\n
Adaptive Learning Rate\ub294 \ud559\uc2b5 \uacfc\uc815\uc5d0\uc11c \uc801\uc808\ud55c \ud559\uc2b5\ub960\uc744 \uc790\ub3d9\uc73c\ub85c \uc870\uc815\ud558\ub294 \uae30\ubc95\uc73c\ub85c, Adam\uacfc \uac19\uc740 \uc635\ud2f0\ub9c8\uc774\uc800\uc5d0\uc11c \uc9c0\uc6d0\ub429\ub2c8\ub2e4. \ub2e4\uc74c\uc740 Adam \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc0ac\uc6a9\ud558\ub294 \uc608\uc2dc\uc785\ub2c8\ub2e4:<\/p>\n
optimizer = optim.Adam(model.parameters(), lr=0.001)\n<\/code><\/pre>\n5.2. Learning Rate Scheduler<\/h3>\n
\ud6c8\ub828 \uacfc\uc815\uc5d0\uc11c \ud559\uc2b5\ub960\uc744 \ub3d9\uc801\uc73c\ub85c \uc870\uc815\ud560 \uc218 \uc788\ub294 \uc2a4\ucf00\uc904\ub7ec\ub97c \ud65c\uc6a9\ud558\ub294 \uac83\ub3c4 \uc131\ub2a5 \ucd5c\uc801\ud654\uc5d0 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uc2a4\ud15d \ub2e8\uc704\ub85c \ud559\uc2b5\ub960\uc744 \uac10\uc18c\uc2dc\ud0a4\ub294 \uc608\uc2dc\uc785\ub2c8\ub2e4:<\/p>\n
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)\n\nfor epoch in range(num_epochs):\n train(model, optimizer)\n scheduler.step() # \uc5d0\ud3ec\ud06c\ub9c8\ub2e4 \ud559\uc2b5\ub960 \ub514\ud06c\ub9ac\uba58\ud2b8\n<\/code><\/pre>\n6. \ub370\uc774\ud130 \uc99d\uac15(Data Augmentation)<\/h2>\n
\ub370\uc774\ud130 \uc99d\uac15\uc740 \ud6c8\ub828 \ub370\uc774\ud130\uc758 \ub2e4\uc591\uc131\uc744 \uc99d\uac00\uc2dc\ud0a4\uace0 \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\ub294 \uc911\uc694\ud55c \uae30\ubc95\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c\ub294 torchvision<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0 \ub370\uc774\ud130 \uc99d\uac15\uc744 \uc27d\uac8c \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\nimport torchvision.transforms as transforms\n\ntransform = transforms.Compose([\n transforms.RandomHorizontalFlip(),\n transforms.RandomRotation(10),\n transforms.ToTensor()\n])\n\n# \ub370\uc774\ud130\uc14b \ub85c\ub529 \uc2dc \ubcc0\ud658 \uc801\uc6a9\ntrain_dataset = torchvision.datasets.MNIST(root='.\/data', train=True, transform=transform, download=True)\n<\/code><\/pre>\n7. \uc870\uae30 \uc885\ub8cc(Early Stopping)<\/h2>\n
\uc870\uae30 \uc885\ub8cc\ub294 \uac80\uc99d \ub370\uc774\ud130\uc758 \uc131\ub2a5\uc774 \ub354 \uc774\uc0c1 \ud5a5\uc0c1\ub418\uc9c0 \uc54a\ub294 \uacbd\uc6b0 \ud6c8\ub828\uc744 \uc911\ub2e8\ud558\ub294 \uae30\ubc95\uc73c\ub85c, \uc774\ub294 \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\uace0 \ud559\uc2b5 \uc2dc\uac04\uc744 \ub2e8\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uc870\uae30 \uc885\ub8cc\ub97c \uad6c\ud604\ud558\ub294 \uae30\ubcf8\uc801\uc778 \ubc29\ubc95\uc785\ub2c8\ub2e4:<\/p>\n
best_accuracy = 0\npatience = 5\ntrigger_times = 0\n\nfor epoch in range(num_epochs):\n train(model, optimizer)\n accuracy = evaluate(model)\n \n if accuracy > best_accuracy:\n best_accuracy = accuracy\n trigger_times = 0 # \uc131\ub2a5 \uac1c\uc120\n else:\n trigger_times += 1 # \uc131\ub2a5 \uac10\uc18c\n \n if trigger_times > patience:\n print('Early stopping!')\n break\n<\/code><\/pre>\n8. \uacb0\ub860<\/h2>\n
\ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5 \ucd5c\uc801\ud654\ub294 \ud6a8\uc728\uc801\uc778 \uc790\uc6d0 \uc0ac\uc6a9\uacfc \ud6c8\ub828 \uc2dc\uac04 \ub2e8\ucd95, \uadf8\ub9ac\uace0 \ucd5c\uc885 \uc131\ub2a5\uc758 \ud5a5\uc0c1\uc5d0 \uae30\uc5ec\ud558\ub294 \ub9e4\uc6b0 \uc911\uc694\ud55c \uacfc\uc815\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ucd5c\uc801\ud654, \ubaa8\ub378 \uad6c\uc870 \ucd5c\uc801\ud654, \ub370\uc774\ud130 \uc99d\uac15 \ub4f1 \ub2e4\uc591\ud55c \uae30\ubc95\uc744 \uc18c\uac1c\ud588\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uae30\ubc95\ub4e4\uc744 \uc801\uc808\ud788 \ud65c\uc6a9\ud55c\ub2e4\uba74, \ubcf5\uc7a1\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ub354 \ud6a8\uacfc\uc801\uc73c\ub85c \ud6c8\ub828\uc2dc\ud0ac \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\uc774 \uac15\uc88c\uac00 \uc5ec\ub7ec\ubd84\uc758 \ub525\ub7ec\ub2dd \ubaa8\ub378 \uc131\ub2a5 \ucd5c\uc801\ud654\uc5d0 \ub3c4\uc6c0\uc774 \ub418\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \ub2e4\uc74c \uac15\uc88c\uc5d0\uc11c\ub294 \uc2e4\uc81c \ud504\ub85c\uc81d\ud2b8\uc5d0\uc11c\uc758 \uc0ac\ub840\ub97c \ud1b5\ud574 \ucd5c\uc801\ud654 \uae30\ubc95\ub4e4\uc744 \ub354 \uae4a\uc774 \uc788\uac8c \ub2e4\ub8f0 \uc608\uc815\uc785\ub2c8\ub2e4. \ub9ce\uc740 \uae30\ub300 \ubd80\ud0c1\ub4dc\ub9bd\ub2c8\ub2e4!<\/p>\n
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