{"id":29912,"date":"2024-10-28T03:18:37","date_gmt":"2024-10-28T03:18:37","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29912"},"modified":"2024-11-26T06:50:53","modified_gmt":"2024-11-26T06:50:53","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-gru-%ea%b3%84%ec%b8%b5-%ea%b5%ac%ed%98%84","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29912\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, GRU \uacc4\uce35 \uad6c\ud604"},"content":{"rendered":"<p>\ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP), \uc2dc\uacc4\uc5f4 \uc608\uce21, \uc74c\uc131 \uc778\uc2dd \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud544\uc218\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911\uc5d0\uc11c\ub3c4 <strong>GRU(Gated Recurrent Unit)<\/strong>\ub294 \uc7ac\ubc1c\uc131 \uc2e0\uacbd\ub9dd(RNN)\uc758 \ud55c \uc885\ub958\ub85c, \uc624\ub7ab\ub3d9\uc548 \uc758\uc874 \uad00\uacc4\ub97c \ud559\uc2b5\ud558\ub294 \ub370 \ub6f0\uc5b4\ub09c \ud6a8\uc728\uc131\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 GRU \uacc4\uce35\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc0c1\uc138\ud788 \uc124\uba85\ud558\uace0, \ud30c\uc774\uc36c\uacfc PyTorch\ub97c \uc0ac\uc6a9\ud55c \uc608\uc81c \ucf54\ub4dc\ub97c \uc81c\uacf5\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. GRU\uc758 \uc774\ud574<\/h2>\n<p>GRU\ub294 LSTM(Long Short-Term Memory)\uacfc \ud568\uaed8 \ub300\ud45c\uc801\uc778 \uac8c\uc774\ud2b8 \uae30\ubc18 RNN \uc544\ud0a4\ud14d\ucc98\uc785\ub2c8\ub2e4. GRU\ub294 \uc7a5\uae30 \uc758\uc874\uc131 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 \ub9ac\uc14b \uac8c\uc774\ud2b8(reset gate)\uc640 \uc5c5\ub370\uc774\ud2b8 \uac8c\uc774\ud2b8(update gate)\ub97c \ub3c4\uc785\ud558\uc5ec \uc815\ubcf4\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \ucc98\ub9ac\ud569\ub2c8\ub2e4.<\/p>\n<ul>\n<li><strong>\ub9ac\uc14b \uac8c\uc774\ud2b8 (r):<\/strong> \uc774 \uac8c\uc774\ud2b8\ub294 \uc774\uc804\uc758 \uae30\uc5b5\uc744 \uc5bc\ub9c8\ub098 \uc78a\uc5b4\uc57c \ud558\ub294\uc9c0\ub97c \uacb0\uc815\ud569\ub2c8\ub2e4. \uc774 \uac12\uc774 0\uc5d0 \uac00\uae4c\uc6b8\uc218\ub85d \uc774\uc804\uc758 \uc815\ubcf4\ub97c \ubb34\uc2dc\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc5c5\ub370\uc774\ud2b8 \uac8c\uc774\ud2b8 (z):<\/strong> \uc774 \uac8c\uc774\ud2b8\ub294 \uc0c8\ub85c\uc6b4 \uc785\ub825 \uc815\ubcf4\ub97c \uc5bc\ub9c8\ub098 \ubc18\uc601\ud560\uc9c0\ub97c \uacb0\uc815\ud569\ub2c8\ub2e4. z\uac00 1\uc5d0 \uac00\uae4c\uc6b4 \uacbd\uc6b0, \uc774\uc804 \uc0c1\ud0dc\ub97c \ub9ce\uc774 \uc720\uc9c0\ud558\uac8c \ub429\ub2c8\ub2e4.<\/li>\n<li><strong>\ud6c4 \uc0c1\ud0dc (h):<\/strong> \ud604\uc7ac \uc0c1\ud0dc\ub294 \uc774\uc804 \uc0c1\ud0dc\uc640 \uc0c8\ub85c\uc6b4 \uc0c1\ud0dc\uc758 \uc870\ud569\uc73c\ub85c \uacc4\uc0b0\ub429\ub2c8\ub2e4.<\/li>\n<\/ul>\n<p>GRU\uc758 \uc218\ud559\uc801 \uc815\uc758\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<p>1. \ub9ac\uc14b \uac8c\uc774\ud2b8: <code>r_t = \u03c3(W_r * [h_{t-1}, x_t])<\/code><\/p>\n<p>2. \uc5c5\ub370\uc774\ud2b8 \uac8c\uc774\ud2b8: <code>z_t = \u03c3(W_z * [h_{t-1}, x_t])<\/code><\/p>\n<p>3. \uc0c8\ub85c\uc6b4 \uae30\uc5b5: <code>\\~h_t = tanh(W * [r_t * h_{t-1}, x_t])<\/code><\/p>\n<p>4. \ucd5c\uc885 \ucd9c\ub825: <code>h_t = (1 - z_t) * h_{t-1} + z_t * \\~h_t<\/code><\/p>\n<h2>2. GRU \uacc4\uce35 \uad6c\ud604\ud558\uae30<\/h2>\n<p>\uc774\uc81c PyTorch\ub85c GRU \uacc4\uce35\uc744 \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. GRU \uacc4\uce35 \uad6c\ud604 \uc2dc \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ubd88\ub7ec\uc628 \ub2e4\uc74c, \uae30\ubcf8\uc801\uc778 GRU \ud074\ub798\uc2a4\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n<h3>2.1 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \ubd88\ub7ec\uc624\uae30<\/h3>\n<pre><code>import torch\nimport torch.nn as nn\nimport torch.nn.functional as F<\/code><\/pre>\n<h3>2.2 GRU \ud074\ub798\uc2a4 \uad6c\ud604\ud558\uae30<\/h3>\n<p>\uc774\uc81c GRU \ud074\ub798\uc2a4\uc758 \uae30\ubcf8 \uad6c\uc870\ub97c \uad6c\ud604\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\uc758 \ud074\ub798\uc2a4\ub294 <code>__init__<\/code> \uba54\uc11c\ub4dc\uc640 <code>forward<\/code> \uba54\uc11c\ub4dc\ub97c \ud3ec\ud568\ud569\ub2c8\ub2e4.<\/p>\n<pre><code>class MyGRU(nn.Module):\n    def __init__(self, input_size, hidden_size):\n        super(MyGRU, self).__init__()\n        self.hidden_size = hidden_size\n\n        # Weight matrices\n        self.W_xz = nn.Linear(input_size, hidden_size)  # Input to update gate\n        self.W_hz = nn.Linear(hidden_size, hidden_size, bias=False)  # Hidden to update gate\n        self.W_xr = nn.Linear(input_size, hidden_size)  # Input to reset gate\n        self.W_hr = nn.Linear(hidden_size, hidden_size, bias=False)  # Hidden to reset gate\n        self.W_xh = nn.Linear(input_size, hidden_size)  # Input to new memory\n        self.W_hh = nn.Linear(hidden_size, hidden_size, bias=False)  # Hidden to new memory\n\n    def forward(self, x, h_prev):\n        # Get gate values\n        z_t = torch.sigmoid(self.W_xz(x) + self.W_hz(h_prev))\n        r_t = torch.sigmoid(self.W_xr(x) + self.W_hr(h_prev))\n\n        # Calculate new memory\n        h_tilde_t = torch.tanh(self.W_xh(x) + self.W_hh(r_t * h_prev))\n\n        # Compute new hidden state\n        h_t = (1 - z_t) * h_prev + z_t * h_tilde_t\n        return h_t<\/code><\/pre>\n<h3>2.3 GRU \uacc4\uce35\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378 \ub9cc\ub4e4\uae30<\/h3>\n<p>GRU \uacc4\uce35\uc744 \ud3ec\ud568\ud558\ub294 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 GRU \uacc4\uce35\uc744 \ud1b5\ud574 \uc785\ub825\uc744 \ucc98\ub9ac\ud55c \ud6c4, \ucd5c\uc885 \uacb0\uacfc\ub97c \ubc18\ud658\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uad6c\uc131\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<pre><code>class MyModel(nn.Module):\n    def __init__(self, input_size, hidden_size, output_size):\n        super(MyModel, self).__init__()\n        self.gru = MyGRU(input_size, hidden_size)  # GRU Layer\n        self.fc = nn.Linear(hidden_size, output_size)  # Fully connected layer\n\n    def forward(self, x):\n        h_t = torch.zeros(x.size(0), self.gru.hidden_size).to(x.device)  # \ucd08\uae30 \uc0c1\ud0dc\n        # GRU\ub97c \ud1b5\ud574 \uc785\ub825\uc744 \ucc98\ub9ac\n        for t in range(x.size(1)):\n            h_t = self.gru(x[:, t, :], h_t)\n\n        output = self.fc(h_t)  # \ucd5c\uc885 \ucd9c\ub825\n        return output<\/code><\/pre>\n<h2>3. \ubaa8\ub378 \ud559\uc2b5 \ubc0f \ud3c9\uac00<\/h2>\n<p>\uc704\uc5d0\uc11c \uad6c\ud604\ud55c GRU \uacc4\uce35\uc744 \ud3ec\ud568\ud55c \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uace0 \ud3c9\uac00\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \uac04\ub2e8\ud55c \uc608\uc81c\ub85c \uc784\uc758\uc758 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n<h3>3.1 \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n<p>\uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc751\uc6a9\uc744 \uc704\ud55c \uac04\ub2e8\ud55c \ub370\uc774\ud130\uc14b\uc744 \uc0dd\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\ub294 \ub79c\ub364 \uc785\ub825\uacfc \uc774\uc5d0 \ub300\ud55c \ub79c\ub364 \ub808\uc774\ube14\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4.<\/p>\n<pre><code>def generate_random_data(num_samples, seq_length, input_size, output_size):\n    x = torch.randn(num_samples, seq_length, input_size)\n    y = torch.randint(0, output_size, (num_samples,))\n    return x, y\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc124\uc815\nnum_samples = 1000\nseq_length = 10\ninput_size = 8\nhidden_size = 16\noutput_size = 4\n\n# \ub370\uc774\ud130 \uc0dd\uc131\nx_train, y_train = generate_random_data(num_samples, seq_length, input_size, output_size)<\/code><\/pre>\n<h3>3.2 \ubaa8\ub378 \ucd08\uae30\ud654 \ubc0f \ud559\uc2b5<\/h3>\n<p>\ubaa8\ub378\uc744 \ucd08\uae30\ud654\ud558\uace0, \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc124\uc815\ud55c \ud6c4, \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4.<\/p>\n<pre><code># \ubaa8\ub378 \ucd08\uae30\ud654\nmodel = MyModel(input_size, hidden_size, output_size)\ncriterion = nn.CrossEntropyLoss()  # \uc190\uc2e4 \ud568\uc218\noptimizer = torch.optim.Adam(model.parameters(), lr=0.001)  # \uc635\ud2f0\ub9c8\uc774\uc800\n\n# \ud559\uc2b5 \ub8e8\ud504\nnum_epochs = 20\nfor epoch in range(num_epochs):\n    model.train()\n    optimizer.zero_grad()  # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n    outputs = model(x_train)  # \ubaa8\ub378 \uc608\uce21\n    loss = criterion(outputs, y_train)  # \uc190\uc2e4 \uacc4\uc0b0\n    loss.backward()  # \uae30\uc6b8\uae30 \uc804\ud30c\n    optimizer.step()  # \ub9e4\uac1c\ubcc0\uc218 \uc5c5\ub370\uc774\ud2b8\n\n    if (epoch + 1) % 5 == 0:\n        print(f'Epoch [{epoch + 1}\/{num_epochs}], Loss: {loss.item():.4f}')<\/code><\/pre>\n<h3>3.3 \ubaa8\ub378 \ud3c9\uac00<\/h3>\n<p>\ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ud6c4, \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \ub9cc\ub4ed\ub2c8\ub2e4.<\/p>\n<pre><code># \ubaa8\ub378 \ud3c9\uac00\nmodel.eval()  # \ud3c9\uac00 \ubaa8\ub4dc \uc804\ud658\nwith torch.no_grad():\n    x_test, y_test = generate_random_data(100, seq_length, input_size, output_size)\n    y_pred = model(x_test)\n    _, predicted = torch.max(y_pred, 1)\n    accuracy = (predicted == y_test).float().mean()\n    print(f'Test Accuracy: {accuracy:.4f}')  # \uc815\ud655\ub3c4 \ucd9c\ub825<\/code><\/pre>\n<h2>4. \uacb0\ub860<\/h2>\n<p>\uc774\ubc88 \uac15\uc88c\ub97c \ud1b5\ud574 GRU \uacc4\uce35\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc PyTorch\ub97c \uc0ac\uc6a9\ud55c \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. GRU\ub294 LSTM\ubcf4\ub2e4 \uc0c1\ub300\uc801\uc73c\ub85c \uac04\ub2e8\ud558\uba74\uc11c\ub3c4 \ud6a8\uacfc\uc801\uc778 \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uba70, \ub2e4\uc591\ud55c \uc2dc\ud000\uc2a4 \ub370\uc774\ud130 \ubb38\uc81c\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec GRU \uacc4\uce35\uc744 \uad6c\ud604\ud558\ub294 \uac83\uc740 \ub525\ub7ec\ub2dd\uc5d0 \ub300\ud55c \ub354 \uae4a\uc740 \uc774\ud574\ub97c \ubc14\ud0d5\uc73c\ub85c \ub2e4\uc591\ud55c RNN \uae30\ubc18 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ub370 \ud070 \ub3c4\uc6c0\uc774 \ub420 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<p>\uc6b0\ub9ac\ub294 GRU\uc758 \uae30\ubcf8 \uc544\ud0a4\ud14d\ucc98\uc640 \ud30c\ub77c\ubbf8\ud130\ub97c \ub2e4\ub8e8\uc5c8\uc73c\uba70, \uc2e4\uc81c \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud55c \ubaa8\ub378 \ud559\uc2b5 \ubc0f \ud3c9\uac00 \uacfc\uc815\uc744 \uc608\uc2dc\ub85c \ub4e4\uc5c8\uc2b5\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uc751\uc6a9 \ud504\ub85c\uadf8\ub7a8\uc744 \uc704\ud55c \uc2ec\ud654 \ud559\uc2b5\uc774 \ud544\uc694\ud558\ub2e4\uba74, \ub354 \ub9ce\uc740 \ub370\uc774\ud130\ub97c \uc801\uc6a9\ud558\uace0, \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd \ubc0f \uc815\uaddc\ud654 \uae30\ubc95 \ub4f1\uc744 \uc2dc\ub3c4\ud574\ubcfc \uac83\uc744 \uad8c\uc7a5\ud569\ub2c8\ub2e4.<\/p>\n<p>\uc774\ub807\uac8c GRU \uacc4\uce35\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \uad6c\ud604\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8e8\uc5c8\uc74c\uc744 \ud1b5\ud574, \ub3c5\uc790 \uc5ec\ub7ec\ubd84\uc774 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ub354 \uae4a\uc774 \ud0d0\uad6c\ud558\uace0 \uc2e4\uc9c8\uc801\uc778 \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \uac10\uc0ac\ud569\ub2c8\ub2e4!<\/p>\n<footer>\n<p>\uc774 \uae00\uc774 \ub9c8\uc74c\uc5d0 \ub4dc\uc168\ub2e4\uba74, \uacf5\uc720\ud574 \uc8fc\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n<\/footer>\n","protected":false},"excerpt":{"rendered":"<p>\ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP), \uc2dc\uacc4\uc5f4 \uc608\uce21, \uc74c\uc131 \uc778\uc2dd \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud544\uc218\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911\uc5d0\uc11c\ub3c4 GRU(Gated Recurrent Unit)\ub294 \uc7ac\ubc1c\uc131 \uc2e0\uacbd\ub9dd(RNN)\uc758 \ud55c \uc885\ub958\ub85c, \uc624\ub7ab\ub3d9\uc548 \uc758\uc874 \uad00\uacc4\ub97c \ud559\uc2b5\ud558\ub294 \ub370 \ub6f0\uc5b4\ub09c \ud6a8\uc728\uc131\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 GRU \uacc4\uce35\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc0c1\uc138\ud788 \uc124\uba85\ud558\uace0, \ud30c\uc774\uc36c\uacfc PyTorch\ub97c \uc0ac\uc6a9\ud55c \uc608\uc81c \ucf54\ub4dc\ub97c \uc81c\uacf5\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. GRU\uc758 \uc774\ud574 GRU\ub294 LSTM(Long Short-Term Memory)\uacfc \ud568\uaed8 \ub300\ud45c\uc801\uc778 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/29912\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, GRU \uacc4\uce35 \uad6c\ud604&#8221;<\/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-29912","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, GRU \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\/29912\/\" \/>\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, GRU \uacc4\uce35 \uad6c\ud604 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP), \uc2dc\uacc4\uc5f4 \uc608\uce21, \uc74c\uc131 \uc778\uc2dd \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud544\uc218\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911\uc5d0\uc11c\ub3c4 GRU(Gated Recurrent Unit)\ub294 \uc7ac\ubc1c\uc131 \uc2e0\uacbd\ub9dd(RNN)\uc758 \ud55c \uc885\ub958\ub85c, \uc624\ub7ab\ub3d9\uc548 \uc758\uc874 \uad00\uacc4\ub97c \ud559\uc2b5\ud558\ub294 \ub370 \ub6f0\uc5b4\ub09c \ud6a8\uc728\uc131\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 GRU \uacc4\uce35\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc0c1\uc138\ud788 \uc124\uba85\ud558\uace0, \ud30c\uc774\uc36c\uacfc PyTorch\ub97c \uc0ac\uc6a9\ud55c \uc608\uc81c \ucf54\ub4dc\ub97c \uc81c\uacf5\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. GRU\uc758 \uc774\ud574 GRU\ub294 LSTM(Long Short-Term Memory)\uacfc \ud568\uaed8 \ub300\ud45c\uc801\uc778 &hellip; \ub354 \ubcf4\uae30 &quot;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, GRU \uacc4\uce35 \uad6c\ud604&quot;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/29912\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T03:18:37+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:50:53+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:label1\" content=\"\uae00\uc4f4\uc774\" \/>\n\t<meta name=\"twitter:data1\" content=\"root\" \/>\n\t<meta name=\"twitter:label2\" content=\"\uc608\uc0c1 \ub418\ub294 \ud310\ub3c5 \uc2dc\uac04\" \/>\n\t<meta name=\"twitter:data2\" content=\"3\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/atmokpo.com\/w\/29912\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29912\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, GRU \uacc4\uce35 \uad6c\ud604\",\"datePublished\":\"2024-10-28T03:18:37+00:00\",\"dateModified\":\"2024-11-26T06:50:53+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29912\/\"},\"wordCount\":45,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"\ud30c\uc774\ud1a0\uce58 \uac15\uc88c\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/29912\/\",\"url\":\"https:\/\/atmokpo.com\/w\/29912\/\",\"name\":\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, GRU \uacc4\uce35 \uad6c\ud604 - 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