4. \uc815\ub9ac<\/a><\/li>\n<\/ul>\n1. RNN \uc774\ud574\ud558\uae30<\/h2>\n
\uc804\ud1b5\uc801\uc778 \uc2e0\uacbd\ub9dd\uc740 \uace0\uc815\ub41c \ud06c\uae30\uc758 \uc785\ub825\uc744 \ucc98\ub9ac\ud558\ub294 \ub370 \uc798 \uc791\ub3d9\ud569\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub294 \ub54c\ub54c\ub85c \uac00\ubcc0\uc801\uc778 \uae38\uc774\ub97c \uac00\uc9c0\uba70, \uc774\uc804 \uc0c1\ud0dc \uc815\ubcf4\uac00 \ud604\uc7ac\uc758 \uc608\uce21\uc5d0 \uc911\uc694\ud55c \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4. RNN\uc740 \uc774\ub7ec\ud55c \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ucc98\ub9ac\ud560 \uc218 \uc788\ub294 \uad6c\uc870\uc785\ub2c8\ub2e4.<\/p>\n
RNN\uc758 \uad6c\uc870<\/h3>\n
RNN\uc740 \uae30\ubcf8\uc801\uc73c\ub85c \ubc18\ubcf5\uc801\uc778 \uad6c\uc870\ub97c \uac00\uc9c4 \uc2e0\uacbd\ub9dd\uc785\ub2c8\ub2e4. \uc785\ub825 \uc2dc\ud000\uc2a4\uc758 \uac01 \uc694\uc18c\ub294 RNN \ub124\ud2b8\uc6cc\ud06c\uc758 \ud604\uc7ac \uc0c1\ud0dc\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\uace0, \ub2e4\uc74c \uc2dc\uac04 \ub2e8\uacc4\ub85c \uc774\ub3d9\ud560 \ub54c \uacfc\uac70\uc758 \uc815\ubcf4\ub97c \uc720\uc9c0\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778 RNN\uc758 \uc218\uc2dd\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
h_t = f(W_hh * h_(t-1) + W_xh * x_t + b_h)<\/code><\/pre>\n\uc5ec\uae30\uc11c:<\/p>\n
\nh_t<\/code>: \ud604\uc7ac \uc2dc\uc810 t<\/code>\uc758 \uc740\ub2c9 \uc0c1\ud0dc<\/li>\nh_(t-1)<\/code>: \uc774\uc804 \uc2dc\uc810 t-1<\/code>\uc758 \uc740\ub2c9 \uc0c1\ud0dc<\/li>\nx_t<\/code>: \ud604\uc7ac \uc2dc\uc810 t<\/code>\uc758 \uc785\ub825<\/li>\nW_hh<\/code>: \uc740\ub2c9 \uc0c1\ud0dc \uac04\uc758 \uac00\uc911\uce58<\/li>\nW_xh<\/code>: \uc785\ub825\uacfc \uc740\ub2c9 \uc0c1\ud0dc \uac04\uc758 \uac00\uc911\uce58<\/li>\nb_h<\/code>: \uc740\ub2c9 \uc0c1\ud0dc\uc758 \ud3b8\ud5a5<\/li>\n<\/ul>\n2. PyTorch \uc18c\uac1c<\/h2>\n
PyTorch\ub294 \ud30c\uc774\uc36c \uae30\ubc18\uc758 \uacfc\ud559 \uc5f0\uc0b0 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \uc0ac\uc6a9\uc790 \uce5c\ud654\uc801\uc778 \uc778\ud130\ud398\uc774\uc2a4\uc640 \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \uc81c\uacf5\ud558\uc5ec \ubcf5\uc7a1\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc27d\uac8c \uad6c\ud604\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\uc2b5\ub2c8\ub2e4. PyTorch\ub294 \ub2e4\uc74c\uc758 \uc8fc\uc694 \ud2b9\uc9d5\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504: \uc2e4\ud589 \uc2dc\uc810\uc5d0 \uadf8\ub798\ud504\ub97c \uc0dd\uc131\ud558\uace0 \uc218\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
- \uac15\ub825\ud55c GPU \uc9c0\uc6d0: \ud150\uc11c \uc5f0\uc0b0\uc744 \uc27d\uac8c GPU\uc5d0\uc11c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
- \ud48d\ubd80\ud55c \ucee4\ubba4\ub2c8\ud2f0\uc640 \uc790\ub8cc: \ub9ce\uc740 \ud29c\ud1a0\ub9ac\uc5bc\uacfc \uc608\uc81c \ucf54\ub4dc\uac00 \uc81c\uacf5\ub429\ub2c8\ub2e4.<\/li>\n<\/ul>\n
3. RNN \uad6c\ud604\ud558\uae30<\/h2>\n
\uc774\uc81c PyTorch\ub97c \ud65c\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c RNN \uacc4\uce35\uc744 \uad6c\ud604\ud558\uace0, \uc774\ub97c \ud1b5\ud574 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc5d0 \ub300\ud55c \uc608\uc81c \ucf54\ub4dc\ub97c \ub2e8\uacc4\ubcc4\ub85c \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1. \ud658\uacbd \uc124\uc815<\/h3>\n
\uc6b0\uc120 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud558\uace0 \uc784\ud3ec\ud2b8\ud569\ub2c8\ub2e4:<\/p>\n
!pip install torch numpy<\/code><\/pre>\nimport torch\nimport torch.nn as nn\nimport numpy as np\n<\/code><\/pre>\n3.2. RNN \ud074\ub798\uc2a4 \uad6c\ud604<\/h3>\n
\uc774\uc81c RNN \uacc4\uce35\uc744 \ud074\ub798\uc2a4\ub85c \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uae30\ubcf8\uc801\uc73c\ub85c nn.Module<\/code>\uc744 \uc0c1\uc18d\ubc1b\uc544 \ubaa8\ub378\uc744 \uc815\uc758\ud558\uace0, __init__<\/code> \uba54\uc11c\ub4dc\uc5d0\uc11c \ud544\uc694\ud55c \uce35\uacfc \ud30c\ub77c\ubbf8\ud130\ub97c \ucd08\uae30\ud654\ud569\ub2c8\ub2e4. \uadf8\ub9ac\uace0 forward<\/code> \uba54\uc11c\ub4dc\uc5d0\uc11c \uc21c\uc804\ud30c \uacfc\uc815\uc744 \uad6c\ud604\ud569\ub2c8\ub2e4.<\/p>\nclass SimpleRNN(nn.Module):\n def __init__(self, input_size, hidden_size, output_size):\n super(SimpleRNN, self).__init__()\n self.hidden_size = hidden_size\n \n # \uc785\ub825\uacfc \uc740\ub2c9 \uc0c1\ud0dc\ub97c \uc5f0\uacb0\ud558\ub294 \uc120\ud615 \uacc4\uce35\n self.i2h = nn.Linear(input_size + hidden_size, hidden_size)\n # \uc740\ub2c9 \uc0c1\ud0dc\uc5d0\uc11c \ucd9c\ub825\uc73c\ub85c \uac00\ub294 \uc120\ud615 \uacc4\uce35\n self.h2o = nn.Linear(hidden_size, output_size)\n self.activation = nn.Tanh() # \ud65c\uc131\ud654 \ud568\uc218\ub85c tanh \uc0ac\uc6a9\n\n def forward(self, x, hidden):\n combined = torch.cat((x, hidden), 1) # \uc785\ub825\uacfc \uc774\uc804 \uc740\ub2c9 \uc0c1\ud0dc \uc5f0\uacb0\n hidden = self.i2h(combined) # \uc740\ub2c9 \uc0c1\ud0dc \uc5c5\ub370\uc774\ud2b8\n output = self.h2o(hidden) # \ucd9c\ub825 \uacc4\uc0b0\n return output, hidden\n\n def init_hidden(self):\n return torch.zeros(1, self.hidden_size) # \uc740\ub2c9 \uc0c1\ud0dc \ucd08\uae30\ud654\n<\/code><\/pre>\n3.3. \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
RNN\uc744 \ud6c8\ub828\ud558\uae30 \uc704\ud55c \ub370\uc774\ud130\ub97c \uc900\ube44\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uae38\uc774\uac00 10\uc778 \uc2dc\ud000\uc2a4\ub97c \uc0dd\uc131\ud558\uace0, \uac01 \uc694\uc18c\ub294 0\uacfc 1 \uc0ac\uc774\uc758 \ub09c\uc218\ub85c \ucd08\uae30\ud654\ud569\ub2c8\ub2e4:<\/p>\n
def generate_data(seq_length=10):\n return np.random.rand(1, seq_length, 1).astype(np.float32)\n\ndata = generate_data()\ndata_tensor = torch.from_numpy(data)\n<\/code><\/pre>\n3.4. \ubaa8\ub378 \ud6c8\ub828\ud558\uae30<\/h3>\n
\ubaa8\ub378 \ud6c8\ub828\uc744 \uc704\ud55c \ub8e8\ud504\ub97c \uc791\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc190\uc2e4 \ud568\uc218\ub97c \uc815\uc758\ud558\uace0 \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc124\uc815\ud55c \ud6c4, \ubc18\ubcf5\uc801\uc73c\ub85c \ubaa8\ub378\uc758 \ud30c\ub77c\ubbf8\ud130\ub97c \uac31\uc2e0\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc9c4\ud589\ud569\ub2c8\ub2e4:<\/p>\n
def train_rnn(model, data, epochs=500):\n loss_function = nn.MSELoss() # \uc190\uc2e4 \ud568\uc218\ub85c \ud3c9\uade0\uc81c\uacf1\uc624\ucc28 \uc0ac\uc6a9\n optimizer = torch.optim.Adam(model.parameters(), lr=0.01) # Adam \uc635\ud2f0\ub9c8\uc774\uc800\n \n for epoch in range(epochs):\n hidden = model.init_hidden()\n optimizer.zero_grad() # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n \n # \ubaa8\ub378\uc5d0 \uc785\ub825\uac12\uc744 \uc8fc\uace0 \ucd9c\ub825\uac12 \ubc0f \uc740\ub2c9 \uc0c1\ud0dc\ub97c \ubc1b\uc74c\n output, hidden = model(data, hidden)\n target = torch.tensor([[1.0]]) # \ubaa9\ud45c\uac12\n \n loss = loss_function(output, target) # \uc190\uc2e4 \uacc4\uc0b0\n loss.backward() # \uae30\uc6b8\uae30 \uacc4\uc0b0\n optimizer.step() # \ud30c\ub77c\ubbf8\ud130 \uc5c5\ub370\uc774\ud2b8\n \n if epoch % 50 == 0:\n print(f'Epoch {epoch}, Loss: {loss.item()}')\n\n# RNN \ubaa8\ub378 \uc815\uc758 \ubc0f \ud6c8\ub828 \uc2dc\uc791\ninput_size = 1\nhidden_size = 10\noutput_size = 1\n\nrnn_model = SimpleRNN(input_size, hidden_size, output_size)\ntrain_rnn(rnn_model, data_tensor)\n<\/code><\/pre>\n4. \uc815\ub9ac<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 RNN\uc758 \uac1c\ub150\uacfc PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c RNN \uacc4\uce35\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. RNN\uc740 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ucc98\ub9ac\ud560 \uc218 \uc788\ub294 \uc720\uc6a9\ud55c \ubaa8\ub378\uc774\uba70, \ub2e4\uc591\ud55c \uc0c1\ud669\uc5d0\uc11c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub354 \uae4a\uc774 \uc788\ub294 \uc774\ud574\ub97c \uc704\ud574 \ub2e4\uc591\ud55c RNN \ubcc0\ud615 (LSTM, GRU \ub4f1)\ub3c4 \uacf5\ubd80\ud574\ubcf4\ub294 \uac83\uc744 \ucd94\ucc9c\ub4dc\ub9bd\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa8\ub378\uc774 \uc5b4\ub5bb\uac8c \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\uc758 \uc7a5\uae30 \uc758\uc874\uc131\uc744 \ud559\uc2b5\ud558\ub294\uc9c0 \uc54c\uc544\ubcf4\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n
\uc55e\uc73c\ub85c\ub3c4 \ub2e4\uc591\ud55c \ub525\ub7ec\ub2dd \uae30\ubc95\uc744 \uc801\uc6a9\ud574\ubcf4\uba70 \uc2e4\ub825\uc744 \uc313\uc544\uac00\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd \ubd84\uc57c\uc5d0\uc11c Recurrent Neural Networks (RNN)\ub294 \uc8fc\ub85c \uc2dc\ud000\uc2a4 \ub370\uc774\ud130, \uc608\ub97c \ub4e4\uc5b4 \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uc8fc\uac00 \uc608\uce21, \uc74c\uc131 \uc778\uc2dd \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 RNN\uc758 \uae30\ubcf8 \uac1c\ub150\uc744 \uc774\ud574\ud558\uace0, PyTorch\ub97c \ud65c\uc6a9\ud574 \uac04\ub2e8\ud55c RNN \uacc4\uce35\uc744 \uc9c1\uc811 \uad6c\ud604\ud574\ubcf4\ub294 \uacfc\uc815\uc744 \uc18c\uac1c\ud569\ub2c8\ub2e4. \ubaa9\ucc28 1. RNN \uc774\ud574\ud558\uae30 2. PyTorch \uc18c\uac1c 3. RNN \uad6c\ud604\ud558\uae30 4. \uc815\ub9ac 1. RNN \uc774\ud574\ud558\uae30 \uc804\ud1b5\uc801\uc778 \uc2e0\uacbd\ub9dd\uc740 \uace0\uc815\ub41c \ud06c\uae30\uc758 … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, RNN \uacc4\uce35 \uad6c\ud604”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[33],"tags":[],"class_list":["post-29941","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, RNN \uacc4\uce35 \uad6c\ud604 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/29941\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, RNN \uacc4\uce35 \uad6c\ud604 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd \ubd84\uc57c\uc5d0\uc11c Recurrent Neural Networks (RNN)\ub294 \uc8fc\ub85c \uc2dc\ud000\uc2a4 \ub370\uc774\ud130, \uc608\ub97c \ub4e4\uc5b4 \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uc8fc\uac00 \uc608\uce21, \uc74c\uc131 \uc778\uc2dd \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 RNN\uc758 \uae30\ubcf8 \uac1c\ub150\uc744 \uc774\ud574\ud558\uace0, PyTorch\ub97c \ud65c\uc6a9\ud574 \uac04\ub2e8\ud55c RNN \uacc4\uce35\uc744 \uc9c1\uc811 \uad6c\ud604\ud574\ubcf4\ub294 \uacfc\uc815\uc744 \uc18c\uac1c\ud569\ub2c8\ub2e4. \ubaa9\ucc28 1. 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