This script is written by me to complete specific tasks, which can improve my scientific research efficiency. If you are a python language lover or a graduate student of multi-label classification, you will get some enlightenment.But to tell the truth, this task is too specific and not universal. If there is no specific correct format document, it is even difficult to run through, so don't have too much hope. If
Transferred from http://www.cnblogs.com/pinard/p/6117515.html Before, I made a summary of the principle of support vector machine (hereinafter referred to as SVM) algorithm through a series. This paper makes a summary of the use of scikit-learn SVM algorithm library from the perspective of practice.Scikit-learn SVM algorithm library encapsulates the implementation of libsvm and liblinear, and only rewrites the interface part of the algorithm. 1. overview of using scikit-learnsvm algorithm library the algorithm
import tensorflow as tf import numpy as np from scipy.misc import imread, imresize from imagenet_classes import class_names class vgg16: def __init__(self, imgs, weights=None, sess=None): self.imgs = imgs self.convlayers() self.fc_layers() self.probs = tf.nn.softmax(self.fc3l) if weights is not None and sess is not None: self.load_weights(weights, sess) def convlayers(self): self.parameters =  # zero-mean input with tf.name_scope('preprocess') as scope: mean = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean') images = self.imgs-mean
Boosting is a commonly used statistical learning method. in the training process, multiple classifiers are learned by changing the weight of training samples, and the optimal classifier is finally obtained.At the end of each round of training, reduce the weight of correctly classified training samples and increase the weight of incorrectly classified samples. After several rounds of training, some incorrectly classified training samples will receive more attention, while the weight of correctly classified training samples approaches zero, thus obtaining a plurality of simple classifiers.
Simulated Annealing Algorithm is an optimization algorithm inspired by physics.The so-called annealing refers to the process of heating the alloy and then slowly cooling it.A large number of atoms jump around due to excitation, and then gradually stabilize to a state of low energy level, so these atoms can find a configuration of low energy level. annealing algorithm starts with a random solution of a problem.It uses a variable to
For installing CAFFE (Convolutional Architecture for Fast Feature Embedding) under mac, please refer to the previous wiki (installing CAFFE (Convolutional Architecture for Fast Feature Embedding) under Mac), of course, if you encounter other problems, please google yourself.
For various linux systems, there are already many online tutorials.
2. Brief Introduction of CAFFE (Convolutional Architecture for Fast Feature Embedding) Code and Architecture Hierarchy
CAFFE (Convolutional Architecture for Fast Feature Embedding) source code is Cpp language, based on some external libraries, including BLAS (Matrix Computing), CUDA(GPU Driver), GFLAGS, GLOG, Boost, Protobuf, HD F5, Leveldb, LMDB, etc.
function analysis is as follows: SelectSort(SqList &L) Parameters: Sequence Table L Function: Sort (Default Ascending) Space Complexity: O(1) Time Complexity: O(n-side)Stability: unstableThought: Assume that the I-th value is the current minimum value (0 to i-1 are already ascending and are all less than or equal to the I-th value), make min=i, compare backwards from i+1, if less than the I-th valueRecord the subscript (make min equal to the subscript of
If transaction T1 blocks data R, transaction T2 requests blocking R again, so T2 waits.T3 also requested to block R. When T1 releases the block on R, the system first approved T3' s request, and T2 is still waiting.Then T4 requests to block R again, and after T3 releases the block on R, the system approves T4's request, ..., T2 may wait forever, which is the case in livelock, as shown in Figure 8.