import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler import random # set seed random.seed(42) # thousand random numbers num = [[random.randint(0,1000)] for _ in range(1000)] # standardize values ss = StandardScaler() num_ss = ss.fit_transform...
对于同一个批次的所有输入样本X=[x_1,x_2,...,x_m],\mathbf{x} \in \mathbb{R}^{m \times d}(其中m表示批次中的样本数量,即batch_size,d表示每个样本的特征数量),对每个特征计算其均值\mu和方差\sigma^2: \mu = \frac{1}{m} \sum_{i=1}^{m} x_i, \quad \sigma^2 = \frac{1}{m...
Python numpy 归一化和标准化 代码实现 归一化(Normalization)、标准化(Standardization)和中心化/零均值化(Zero-centered) 不需要负样本对的SOTA的自监督学习方法:BYOL ;SimCLR和MoCo使用的显式对比方法是这样学习的:“这两个特定图像之间的区别是什么?”这两种方法似乎是相同的,因为将一幅图像与许多其他图像进行比较...
导入mnist数据集的方法同前面一样一样的! import numpy as py import tensorflow as tf import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('data/', one_hot=True) 1. 2. 3. 4. 5. 二,参数的初始化 数据集mnist图像输入为...
torchvision.transforms.RandomSizedCrop是做crop的。需要注意的是对于torchvision.transforms.RandomSizedCrop和transforms.RandomHorizontalFlip()等,输入对象都是PIL Image,也就是用python的PIL库读进来的图像内容。 2. torchvision.transforms.ToTensor 将PIL图片或者numpy.ndarray转成Tensor类型的...
in_timeit.ipynb in_timeit.py indentation_usage.ipynb indentation_usage.py inf_calc.ipynb inf_calc.py inf_compare.ipynb inf_compare.py inf_float.ipynb inf_float.py inf_math.ipynb inf_math.py inf_numpy.ipynb inf_numpy.py input_usage.ipynb input_usage.py input_usage_script.py...
/usr/bin/env python#-*- coding: utf8 -*-#author: klchang#Use sklearn.preprocessing.normalize function to normalize data.from__future__importprint_functionimportnumpy as npfromsklearn.preprocessingimportnormalize x= np.array([1, 2, 3, 4], dtype='float32').reshape(1,-1)print("Before ...
self.__X=self.__build_X()self.__Y_=self.__build_Y_()def__build_X(self):rArr=numpy.random.uniform(*self.__rRange,(self.__num,1))gArr=numpy.random.uniform(*self.__gRange,(self.__num,1))bArr=numpy.random.uniform(*self.__bRange,(self.__num,1))X=numpy.hstack((rArr,gArr...
Python working example Here we will use the famousirisdataset that is available through scikit-learn. Reminder: scikit-learn functions expect as input a numpy arrayXwith dimension[samples, features/variables]. from sklearn.datasets import load_iris ...
我正在使用 Python 3.8、Tensorflow 2.5.0 和 keras 2.3.1,我正在尝试制作一个模型,但我从 keras 收到错误消息。 这是我的代码: import cv2 import os import numpy as np from keras.layers import Conv2D,Dropout, Flatten, Dense,MaxPooling2D, MaxPool2D import keras.layers.normalization #from tensorflow...