plt.title('Confusion matrix (acc='+self.summary()+')') # 在图中标注数量/概率信息 thresh = matrix.max() / 2 for x in range(self.num_classes): for y in range(self.num_classes): # 注意这里的matrix[y, x]不是matrix[x, y] info = int(matrix[y, x]) plt.text(x, y, info, v...
fig.set_facecolor('black') # Plot confusion matrix cm = metrics.confusion_matrix(target, prediction) cm_display = cmd(cm, display_labels = ['GO', 'STOP']) cm_display.plot() --- Accuracy: 0.82 Precision: 0.771 Recall: 0.8296 最终,我们的准确率为 82%,精确率为 77.1%,召回率为 82.96%。
plt.title('Confusion matrix') # 在图中标注数量/概率信息 thresh = matrix.max() / 2 for x in range(self.num_classes): for y in range(self.num_classes): # 注意这里的matrix[y, x]不是matrix[x, y] info = int(matrix[y, x]) plt.text(x, y, info, verticalalignment='center', hori...
savefig('heatmap_confusion_matrix.jpg') plt.show() 结果显示 代码语言:javascript 代码运行次数:0 运行 AI代码解释 加载数据 训练数据: (1257, 64) 测试数据: (540, 64) 定义相关参数构建数据集定义计算评价指标定义模型 Model( (fc1): Linear(in_features=64, out_features=256, bias=True) (relu): ...
confusion_matrix = np.array([(20,5),(5,55)],dtype=np.float64) plt.imshow(confusion_matrix, interpolation='nearest', cmap=plt.cm.Oranges) #按照像素显示出矩阵 plt.title('confusion_matrix') plt.colorbar() tick_marks = np.arange(len(classes)) ...
如何在Python中实现矩阵分析混淆矩阵（Confusion Matrix）是机器学习中用来总结分类模型预测结果的一个分析表...
# Keep track of correct guesses in a confusion matrixconfusion = torch.zeros(n_categories, n_categories)n_confusion = 10000# Just return an output given a linedef evaluate(line_tensor):hidden = rnn.initHidden()for i in range(line_tensor.size()[0]):output, hidden = rnn(line_tensor[i]...
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report import matplotlib.pyplot as plt import seaborn as sns import copy import numpy as np import time import torch import torch.nn as nn from torch.optim import Adam ...
con_matrix = ClassificationInterpretation.from_learner(learn)con_matrix.plot_confusion_matrix()6.利用模型进行预测 在下面的代码片段中，我们可以通过在test_your_image中给出图像的路径来测试我们自己的图像。test_your_image='/content/images (3).jpg'test = open_image(test_your_image)test.show()在下面...
cm = confusion_matrix(test_labels, test_predictions, normalize="pred")fig, ax = plt.subplots(figsize=(8,6))im = ax.imshow(cm, cmap="viridis")fig.colorbar(im)ax.set(xticks=np.arange(len(labels)),yticks=np.arange(len(labels)),xticklabels=labels,yticklabels=labels,ylabel="True labels...