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e96031413 / 防止Google Colab自动断开代码.js
Created Jul 10, 2020
防止Google Colab自动断开代码
View 防止Google Colab自动断开代码.js
// 每60分钟自动运行代码刷新,解除90分钟断开限制.
// 使用方法:colab页面按下 F12或者 Ctrl+Shift+I (mac按 Option+Command+I) 在console(控制台) 输入以下代码并回车.
// 复制以下代码粘贴在浏览器console!!不要关闭浏览器以免失效
function ConnectButton(){
console.log("Connect pushed");
document.querySelector("#connect").click()
}
setInterval(ConnectButton,60000);
@e96031413
e96031413 / PyTorch_t-SNE.py
Last active Apr 14, 2022
如何使用PyTorch的Feature Extractor輸出進行t-SNE視覺化?
View PyTorch_t-SNE.py
from tsnecuda import TSNE
from tsne.resnet import ResNet18
# 使用 PyTorch內建的 ResNet18
import os
import torch
import torchvision.models as models
import torch.optim
from torchvision import transforms
model = models.resnet18()
View TensorFlow_Load_Image_from_Dataframe.py
# 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,)
View overkill_leakage.py
# 針對每個元件計算其Overkill rate和Leakage rate
# 目前只適用於batch size = 1的情形
# 用來產生overkill和leakage數值的dataframe
import pandas as pd
import torch
test_df_mapping2_label = test_df.copy() #複製一份要mapping到2個label的testing資料
test_df_mapping2_label.loc[test_df_mapping2_label['class'] == 0, 'class'] = 0 #將大於1的label轉成1
test_df_mapping2_label.loc[test_df_mapping2_label['class'] == 1, 'class'] = 1
test_df_mapping2_label.loc[test_df_mapping2_label['class'] == 2, 'class'] = 1
@e96031413
e96031413 / searchPPT.py
Created Jan 22, 2021
Search keywords in ppt files with python
View searchPPT.py
# REF https://stackoverflow.com/questions/55497789/find-a-word-in-multiple-powerpoint-files-python/55763992#55763992
from pptx import Presentation
from pptx.enum.shapes import MSO_SHAPE_TYPE
import os
path = "./"
files = [x for x in os.listdir(path) if x.endswith(".pptx")]
View RocAucScore.py
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)
View decisionBoundary.py
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
View FocalLoss.py
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
View LabelSmoothing_public.py
# 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):
View multiple-y-axis-value-in-same-plot.py
# 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")