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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torch.optim as optim |
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from sklearn.metrics import recall_score, precision_score, accuracy_score |
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class SingleLabelTaskHead(nn.Module): |
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def __init__(self, input_size, output_size, device): |
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super(SingleLabelTaskHead, self).__init__() |
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self.fc1 = nn.Linear(input_size, 50) |
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self.fc2 = nn.Linear(50, 50) |
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self.fc3 = nn.Linear(50, output_size) |
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self.softmax = nn.Softmax(dim=1) |
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self.device = device |
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def forward(self, x): |
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x = F.relu(self.fc1(x)) |
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x = F.relu(self.fc2(x)) |
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x = self.fc3(x) |
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x = self.softmax(x) |
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return x |
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def predict(self, x): |
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x = self.forward(x) |
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x = torch.argmax(x, dim=1) |
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return x |
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def accuracy(self, prediction, target): |
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prediction = torch.argmax(prediction, dim=1) |
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return torch.mean((prediction == target).float()) |
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def recall(self, prediction, target): |
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prediction = torch.argmax(prediction, dim=1) |
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return recall_score(target.cpu().detach().numpy(), prediction.cpu().detach().numpy(), average='micro') |
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def precision(self, prediction, target): |
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prediction = torch.argmax(prediction, dim=1) |
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return precision_score(target.cpu().detach().numpy(), prediction.cpu().detach().numpy(), average='micro') |
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