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Created Jul 14, 2022
View plot_decision_boundary.py
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 # ref: https://blog.csdn.net/MachineLearner/article/details/104587288 def plot_decision_boundary(pred_func, X, y, figure=None): """Plot a decision boundary""" if figure is None: # If no figure is given, create a new one plt.figure() # Set min and max values and give it some padding x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5 y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5 h = 0.01
Last active Jun 19, 2022
View ListAndDictReorder.py
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 # 無重複數字 # input: name_list = [0, 1, 2, 3, 8] for idx, num in enumerate(set(name_list)): if idx != num: name_list[idx] = idx # output: name_list = [0, 1, 2, 3, 4] # 有重複數字 # input: name_list = [0, 2, 2, 1, 6, 6] for idx, num in enumerate(set(name_list)):
Last active Jun 11, 2022
View metric_learning_NMI.py
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 # evaluate the clustering performance from sklearn.cluster import KMeans from sklearn.metrics.cluster import normalized_mutual_info_score import numpy as np def evaluation(X, Y): classN = np.max(Y)+1 kmeans = KMeans(n_clusters=classN).fit(X) nmi = normalized_mutual_info_score(Y, kmeans.labels_, average_method='arithmetic') return nmi
Last active Mar 23, 2022
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 # https://stackoverflow.com/questions/63761717/load-image-dataset # https://stackoverflow.com/questions/60655280/how-to-split-an-image-dataset-in-x-train-y-train-x-test-y-test-by-tensorflow import tensorflow as tf import pandas as pd train_df = pd.read_csv('train.csv') train_df['class'] = train_df['class'].apply(str) train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(horizontal_flip=True, vertical_flip=True,)
Last active Feb 21, 2022
View RocAucScore.py
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 import numpy as np from sklearn import metrics from sklearn.metrics import roc_auc_score y = np.array([1, 1, 2, 2]) #scores可以是模型預測結果(Label) #scores也可以是模型預測的confidence(softmax probability) scores = np.array([1, 1, 2, 2]) scores = np.array([0.1, 0.4, 0.35, 0.8]) area_under_curve = roc_auc_score(y, scores)
Last active Feb 16, 2022
View decisionBoundary.py
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 def eval(loader, gt_labels_t, output_file="output.txt"): G.eval() # 特徵提取器 F1.eval() # 分類器 size = 0 correct = 0 y_pred=[] y_true=[] pred_prob = None pred_result = None
Created Jan 25, 2022
View FocalLoss.py
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 import torch.nn.functional as F import torch import numpy as np from torch.autograd import Function import torch.nn as nn from pdb import set_trace as breakpoint import sys import math from torch.nn.parameter import Parameter from torch.nn import init
Created Jan 20, 2022
View LabelSmoothing_public.py
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 # LabelSmoothing.py # https://www.aiuai.cn/aifarm1333.html 示例 3 # From: Github - NVIDIA/DeepLearningExamples/PyTorch/Classification # smoothing.py import torch import torch.nn as nn # 一般版本LabelSmoothing class LabelSmoothing(nn.Module):
Last active Jan 20, 2022
View multiple-y-axis-value-in-same-plot.py
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 # https://stackoverflow.com/a/45925049/13369757 import matplotlib.pyplot as plt import numpy as np fig, host = plt.subplots(figsize=(12,10)) # (width, height) in inches par1 = host.twinx() par2 = host.twinx() host.set_xlabel("Threshold")
Created Jan 12, 2022
View extractDatasetFeatureToPickle.py
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 import numpy as np import torch import pickle from torch.utils.data import Dataset, DataLoader # 提取特徵 def extract(loader): featureExtractor.eval() features = None with torch.no_grad():