47 lines
1.1 KiB
Python
47 lines
1.1 KiB
Python
import torch
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import torch.nn as nn
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import pandas as pd
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from abc import ABC, abstractmethod
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from sklearn.metrics import confusion_matrix as cm
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from torch.utils.data import DataLoader, TensorDataset
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from Qfunctions.divSet import divSet as ds
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from Qfunctions.saveToxlsx import save_to_xlsx as stx
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class Qnn(nn.Module, ABC):
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def __init__(self, labels=None):
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super(Qnn, self).__init__()
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# 保存原始label， 混淆矩阵使用
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self.original_labels = labels
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# 定义结果
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self.result = {
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'acc_and_loss' : {
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'epoch' : [],
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'loss': [],
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'train_accuracy': [],
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'test_accuracy': [],
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},
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'confusion_matrix': None,
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}
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def accuracy(self, output, target):
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pass
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# 定义损失函数
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def hinge_loss(self, output, target):
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pass
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def confusion_matrix(self, test_outputs):
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predicted = torch.argmax(test_outputs, dim=1)
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true_label = torch.argmax(self.y_test, dim=1)
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return cm(predicted.cpu(), true_label.cpu())
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def fit(self, epochs = 100):
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self.train_model(epochs) |