{"id":30078,"date":"2024-10-28T03:19:29","date_gmt":"2024-10-28T03:19:29","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30078"},"modified":"2024-11-26T06:50:07","modified_gmt":"2024-11-26T06:50:07","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d-%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c-%ec%96%91%eb%b0%a9%ed%96%a5-rnn-%ea%b5%ac%ec%a1%b0","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30078\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc591\ubc29\ud5a5 RNN \uad6c\uc870"},"content":{"rendered":"

<\/p>\n

\ub525\ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130 \ucc98\ub9ac\uc5d0 \ub300\ud55c \uc694\uad6c\uac00 \ub298\uc5b4\ub098\uace0 \uc788\uc2b5\ub2c8\ub2e4. RNN(\uc21c\ud658 \uc2e0\uacbd\ub9dd)\uc740 \uc774\ub7ec\ud55c \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub300\ud45c\uc801\uc778 \uad6c\uc870 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \uc591\ubc29\ud5a5 RNN(Bi-directional RNN)\uc758 \uac1c\ub150\uacfc \uc774\ub97c \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc774\uc6a9\ud574 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. RNN(\uc21c\ud658 \uc2e0\uacbd\ub9dd) \uc774\ud574\ud558\uae30<\/h2>\n

RNN\uc740 \uc21c\ud658 \uad6c\uc870\ub97c \uac00\uc9c4 \uc2e0\uacbd\ub9dd\uc73c\ub85c, \uc2dc\ud000\uc2a4 \ub370\uc774\ud130(\uc608: \ud14d\uc2a4\ud2b8, \uc2dc\uac04 \uc2dc\uacc4\uc5f4)\ub97c \ucc98\ub9ac\ud560 \uc218 \uc788\ub294 \ub2a5\ub825\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778 \uc2e0\uacbd\ub9dd\uc740 \uc785\ub825\uc744 \ud55c \ubc88\ub9cc \ubc1b\uace0 \ucd9c\ub825\uc744 \ub0b4\ubcf4\ub0b4\ub294 \ubc18\uba74, RNN\uc740 \uc774\uc804\uc758 \uc0c1\ud0dc\ub97c \uae30\uc5b5\ud558\uace0 \uc774\ub97c \uc774\uc6a9\ud574 \ud604\uc7ac\uc758 \uc0c1\ud0dc\ub97c \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4. \uc774\ub85c \uc778\ud574 RNN\uc740 \uc2dc\ud000\uc2a4\uc758 \uc2dc\uac04\uc801 \uc758\uc874\uc131\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

1.1. RNN\uc758 \uae30\ubcf8 \uad6c\uc870<\/h3>\n

RNN\uc758 \uae30\ubcf8 \uad6c\uc870\ub294 \uae30\ubcf8\uc801\uc778 \ub274\ub7f0\uc758 \uad6c\uc870\uc640 \ube44\uc2b7\ud558\uc9c0\ub9cc, \uc2dc\uac04\uc5d0 \ub530\ub77c \ubc18\ubcf5\uc801\uc73c\ub85c \uc5f0\uacb0\ub41c \uad6c\uc870\ub97c \uac00\uc9d1\ub2c8\ub2e4. \uc544\ub798\ub294 \ub2e8\uc77c RNN \uc140\uc758 \uc815\ubcf4 \ud750\ub984\uc744 \ub098\ud0c0\ub0b8 \uac83\uc785\ub2c8\ub2e4:<\/p>\n

\n     h(t-1)\n      |\n      v\n     (W_hh)\n      |\n     +---------+\n     |         |\n    input --> (tanh) --> h(t)\n     |         |\n     +---------+\n<\/pre>\n
\uc774 \uad6c\uc870\uc5d0\uc11c, h(t-1)<\/code>\ub294 \uc774\uc804 \uc2dc\uc810\uc758 \uc740\ub2c9 \uc0c1\ud0dc(hidden state)\uc774\uba70, \uc774 \uac12\uc744 \uc774\uc6a9\ud574 \ud604\uc7ac \uc2dc\uc810\uc758 \uc740\ub2c9 \uc0c1\ud0dc h(t)<\/code>\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c \uac00\uc911\uce58 W_hh<\/code>\ub294 \uc774\uc804 \uc740\ub2c9 \uc0c1\ud0dc\ub97c \ud604\uc7ac \uc740\ub2c9 \uc0c1\ud0dc\ub85c \ubcc0\ud658\ud558\ub294 \uc5ed\ud560\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
1.2. RNN\uc758 \ud55c\uacc4<\/h3>\n
RNN\uc740 \uae34 \uc2dc\ud000\uc2a4\ub97c \ucc98\ub9ac\ud560 \ub54c “\uae30\uc5b5\uc758 \ud55c\uacc4”\ub77c\ub294 \ubb38\uc81c\uc5d0 \uc9c1\uba74\ud569\ub2c8\ub2e4. \ud2b9\ud788 \uae34 \uc2dc\ud000\uc2a4\uc5d0\uc11c \ucd08\uae30 \uc785\ub825 \uc815\ubcf4\uac00 \uc18c\uc2e4\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 LSTM(Long Short-Term Memory)\uc774\ub098 GRU(Gated Recurrent Unit)\uc640 \uac19\uc740 \uad6c\uc870\uac00 \uac1c\ubc1c\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n
2. \uc591\ubc29\ud5a5 RNN(Bi-directional RNN)<\/h2>\n
\uc591\ubc29\ud5a5 RNN\uc740 \uc2dc\ud000\uc2a4\ub97c \ub450 \ubc29\ud5a5\uc73c\ub85c \ucc98\ub9ac\ud560 \uc218 \uc788\ub294 \uad6c\uc870\uc785\ub2c8\ub2e4. \uc989, \uacfc\uac70 \ubc29\ud5a5(\uc55e\uc5d0\uc11c \ub4a4\ub85c)\uacfc \ubbf8\ub798 \ubc29\ud5a5(\ub4a4\uc5d0\uc11c \uc55e\uc73c\ub85c) \ubaa8\ub450\uc5d0\uc11c \uc815\ubcf4\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uad6c\uc870\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \ub3d9\uc791\ud569\ub2c8\ub2e4.<\/p>\n
2.1. \uc591\ubc29\ud5a5 RNN\uc758 \uae30\ubcf8 \uc544\uc774\ub514\uc5b4<\/h3>\n
\uc591\ubc29\ud5a5 RNN\uc740 \ub450 \uac1c\uc758 RNN \uacc4\uce35\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ud55c \uacc4\uce35\uc740 \uc785\ub825 \uc2dc\ud000\uc2a4\ub97c \uc815\ubc29\ud5a5\uc73c\ub85c \ucc98\ub9ac\ud558\uace0, \ub2e4\ub978 \uacc4\uce35\uc740 \uc785\ub825 \uc2dc\ud000\uc2a4\ub97c \uc5ed\ubc29\ud5a5\uc73c\ub85c \ucc98\ub9ac\ud569\ub2c8\ub2e4. \uc544\ub798\ub294 \uc591\ubc29\ud5a5 RNN\uc758 \uad6c\uc870\ub97c \uac04\ub2e8\ud788 \ub098\ud0c0\ub0b8 \uadf8\ub9bc\uc785\ub2c8\ub2e4:<\/p>\n
\n  Forward     Backward\n   RNN         RNN\n     |           |\n    h(t-1)   h(t+1)\n       \\    +--> (merge) --> h(t)\n        \\   |\n         h(t)\n<\/pre>\n
\uc815\ubc29\ud5a5 RNN\uacfc \uc5ed\ubc29\ud5a5 RNN\uc740 \ub3d9\uc2dc\uc5d0 \uc785\ub825\uc744 \ucc98\ub9ac\ud558\uace0, \uc774 \ub450 \uc740\ub2c9 \uc0c1\ud0dc\ub97c \uacb0\ud569\ud558\uc5ec \ucd5c\uc885 \ucd9c\ub825\uac12\uc744 \ub9cc\ub4ed\ub2c8\ub2e4. \uc774\ub807\uac8c \ud568\uc73c\ub85c\uc368 RNN\uc740 \uc2dc\ud000\uc2a4\uc758 \ubaa8\ub4e0 \uc815\ubcf4\ub97c \ubcf4\ub2e4 \ud6a8\uacfc\uc801\uc73c\ub85c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3. \ud30c\uc774\ud1a0\uce58\ub85c \uc591\ubc29\ud5a5 RNN \uad6c\ud604\ud558\uae30<\/h2>\n
\uc774\uc81c \uc591\ubc29\ud5a5 RNN\uc744 \ud30c\uc774\ud1a0\uce58(Pytorch)\ub97c \uc774\uc6a9\ud574 \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294 \ub370\uc774\ud130\ub85c\uc11c \uc784\uc758\uc758 \uc2dc\ud000\uc2a4\ub97c \uc0ac\uc6a9\ud558\uace0, \uc591\ubc29\ud5a5 RNN\uc744 \ud1b5\ud574 \ub2e4\uc74c \ubb38\uc790\ub97c \uc608\uce21\ud558\ub294 \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4 \ubd05\ub2c8\ub2e4.<\/p>\n
3.1. \ud544\uc218 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc784\ud3ec\ud2b8<\/h3>\n
python\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\n<\/code><\/pre>\n
3.2. \ub370\uc774\ud130 \uc900\ube44\ud558\uae30<\/h3>\n
\uc785\ub825 \ub370\uc774\ud130\ub294 \uac04\ub2e8\ud55c \ubb38\uc790\uc5f4\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc774 \ubb38\uc790\uc5f4\uc758 \ub2e4\uc74c \ubb38\uc790\ub97c \uc608\uce21\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4. \ubb38\uc790\uc5f4 \ub370\uc774\ud130\ub294 \uc5f0\uc18d\uc801\uc73c\ub85c \ub098\ud0c0\ub098\ub294 \ubb38\uc790\uc758 \uc2dc\ud000\uc2a4\ub85c \ubcc0\ud658\ub429\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uac04\ub2e8\ud55c \ub370\uc774\ud130 \uc900\ube44 \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
python\n# \ub370\uc774\ud130\uc640 \ubb38\uc790 \uc9d1\ud569 \uc124\uc815\ndata = \"hello deep learning with pytorch\"\nchars = sorted(list(set(data)))\nchar_to_index = {ch: ix for ix, ch in enumerate(chars)}\nindex_to_char = {ix: ch for ix, ch in enumerate(chars)}\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\nseq_length = 5\ninput_size = len(chars)\nhidden_size = 128\nnum_layers = 2\noutput_size = len(chars)\n\n# \ub370\uc774\ud130\uc14b \uc0dd\uc131\ninputs = []\ntargets = []\nfor i in range(len(data) - seq_length):\n    inputs.append([char_to_index[ch] for ch in data[i:i + seq_length]])\n    targets.append(char_to_index[data[i + seq_length]])\n\ninputs = np.array(inputs)\ntargets = np.array(targets)\n<\/code><\/pre>\n
3.3. \uc591\ubc29\ud5a5 RNN \ubaa8\ub378 \uc815\uc758\ud558\uae30<\/h3>\n
\uc774\uc81c \uc591\ubc29\ud5a5 RNN \ubaa8\ub378\uc744 \uc815\uc758\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c\ub294 nn.RNN()<\/code> \ub610\ub294 nn.LSTM()<\/code> \ud074\ub798\uc2a4\ub97c \uc0ac\uc6a9\ud558\uc5ec RNN \uacc4\uce35\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 nn.RNN()<\/code>\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4:<\/p>\n
python\nclass BiRNN(nn.Module):\n    def __init__(self, input_size, hidden_size, output_size, num_layers):\n        super(BiRNN, self).__init__()\n        self.hidden_size = hidden_size\n        self.num_layers = num_layers\n        \n        # \uc591\ubc29\ud5a5 RNN \uacc4\uce35\n        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True)\n        self.fc = nn.Linear(hidden_size * 2, output_size) # \ub450 \ubc29\ud5a5\uc744 \uace0\ub824\ud558\uc5ec hidden_size * 2\n        \n    def forward(self, x):\n        # RNN\uc5d0 \ub370\uc774\ud130\ub97c \ud1b5\uacfc\uc2dc\ud0b5\ub2c8\ub2e4.\n        out, _ = self.rnn(x)\n        # \ub9c8\uc9c0\ub9c9 \uc2dc\uc810\uc758 \ucd9c\ub825\uc744 \uac00\uc838\uc635\ub2c8\ub2e4.\n        out = out[:, -1, :]   \n        \n        # \ucd5c\uc885 \ucd9c\ub825\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.\n        out = self.fc(out)\n        return out\n<\/code><\/pre>\n
3.4. \ubaa8\ub378 \ud559\uc2b5\ud558\uae30<\/h3>\n
\ubaa8\ub378\uc744 \uc815\uc758\ud588\uc73c\ub2c8, \uc774\uc81c \ud559\uc2b5 \uacfc\uc815\uc744 \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\uc758 DataLoader<\/code>\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubc30\uce58 \ucc98\ub9ac\ub97c \uc9c0\uc6d0\ud558\uace0, \uc190\uc2e4 \ud568\uc218\ub85c CrossEntropyLoss<\/code>\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4:<\/p>\n
python\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc124\uc815\nnum_epochs = 200\nbatch_size = 10\nlearning_rate = 0.01\n\n# \ubaa8\ub378, \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \ucd08\uae30\ud654\nmodel = BiRNN(input_size, hidden_size, output_size, num_layers)\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=learning_rate)\n\n# \ud559\uc2b5 \ub8e8\ud504\nfor epoch in range(num_epochs):\n    # \ub370\uc774\ud130\ub97c \ud150\uc11c\ub85c \ubcc0\ud658\n    x_batch = torch.tensor(inputs, dtype=torch.float32).view(-1, seq_length, input_size)\n    y_batch = torch.tensor(targets, dtype=torch.long)\n\n    # \uacbd\ub7c9\ud654 \ubc29\uc9c0\n    model.zero_grad()\n\n    # \ubaa8\ub378 \uc608\uce21\n    outputs = model(x_batch)\n    \n    # \uc190\uc2e4 \uacc4\uc0b0\n    loss = criterion(outputs, y_batch)\n    \n    # \uc5ed\uc804\ud30c \ubc0f \uac00\uc911\uce58 \uc5c5\ub370\uc774\ud2b8\n    loss.backward()\n    optimizer.step()\n\n    if (epoch+1) % 20 == 0:\n        print(f'Epoch [{epoch+1}\/{num_epochs}], Loss: {loss.item():.4f}')\n<\/code><\/pre>\n
3.5. \ubaa8\ub378 \ud3c9\uac00\ud558\uae30<\/h3>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\ud55c \ud6c4\uc5d0\ub294 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uace0, \uc785\ub825 \uc2dc\ud000\uc2a4\uc5d0 \ub300\ud574 \ub2e4\uc74c \ubb38\uc790\ub97c \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\n
python\ndef predict_next_char(model, input_seq):\n    model.eval()  # \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc804\ud658\n    with torch.no_grad():\n        input_tensor = torch.tensor([[char_to_index[ch] for ch in input_seq]], dtype=torch.float32)\n        input_tensor = input_tensor.view(-1, seq_length, input_size)\n        output = model(input_tensor)\n        _, predicted_index = torch.max(output, 1)\n    return index_to_char[predicted_index.item()]\n\n# \uc608\uce21 \ud14c\uc2a4\ud2b8\ntest_seq = \"hello\"\npredicted_char = predict_next_char(model, test_seq)\nprint(f'\uc785\ub825 \uc2dc\ud000\uc2a4: {test_seq} \ub2e4\uc74c \ubb38\uc790 \uc608\uce21: {predicted_char}')\n<\/code><\/pre>\n
4. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \uc591\ubc29\ud5a5 RNN\uc758 \uac1c\ub150\uacfc \uc774\ub97c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc591\ubc29\ud5a5 RNN\uc740 \uacfc\uac70\uc640 \ubbf8\ub798\uc758 \uc815\ubcf4\ub97c \ub3d9\uc2dc\uc5d0 \ud65c\uc6a9\ud560 \uc218 \uc788\ub294 \uac15\ub825\ud55c \uad6c\uc870\ub85c, \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \uc2dc\ud000\uc2a4 \ub370\uc774\ud130 \ucc98\ub9ac\uc5d0 \uc788\uc5b4 \uc720\uc6a9\ud558\uac8c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c RNN \uad6c\uc870\ub97c \ud1b5\ud558\uc5ec \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\uc758 \ud328\ud134\uacfc \uc758\uc874\uc131\uc744 \ub354 \ud6a8\uacfc\uc801\uc73c\ub85c \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc55e\uc73c\ub85c\ub3c4 \ub2e4\uc591\ud55c \ub525\ub7ec\ub2dd \uae30\ubc95\uacfc \uad6c\uc870\uc5d0 \ub300\ud574 \uc9c0\uc18d\uc801\uc73c\ub85c \ud0d0\uad6c\ud560 \uac83\uc774\uba70, \uc774 \uae00\uc774 \uc5ec\ub7ec\ubd84\uc758 \ub525\ub7ec\ub2dd \ud559\uc2b5\uc5d0 \ub9ce\uc740 \ub3c4\uc6c0\uc774 \ub418\uae38 \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n
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\ub525\ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130 \ucc98\ub9ac\uc5d0 \ub300\ud55c \uc694\uad6c\uac00 \ub298\uc5b4\ub098\uace0 \uc788\uc2b5\ub2c8\ub2e4. RNN(\uc21c\ud658 \uc2e0\uacbd\ub9dd)\uc740 \uc774\ub7ec\ud55c \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub300\ud45c\uc801\uc778 \uad6c\uc870 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \uc591\ubc29\ud5a5 RNN(Bi-directional RNN)\uc758 \uac1c\ub150\uacfc \uc774\ub97c \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc774\uc6a9\ud574 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. 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