caffe ensemble(模型融合+adaboost)
来源:互联网 发布:html5 modernizer.js 编辑:程序博客网 时间:2024/04/19 07:44
方法一:模型融合(生成fuse_model和fusion_train_val.prototxt(更改层名/直接将各自的ip1层 concat))
Caffe中并没有直接用于融合的官方工具,这介绍一个简单有效的土办法,用融合模型进行ensemble
https://github.com/frombeijingwithlove/dlcv_for_beginners/tree/master/random_bonus/multiple_models_fusion_caffe
http://www.cnblogs.com/frombeijingwithlove/p/6683476.html
方法二:adaboost(集成) (写出ensemble层(前提各模型层名不一样))
参考:http://blog.csdn.net/u014114990/article/details/51005316
ensemble_accuracy_layer.cpp
Softmax 层和 accuracy 层的配置文件如下:
layer { name: "3_prob" type: "Softmax" bottom: "3_ip2" top: "3_prob" } layer { name: "1_accuracy" type: "Accuracy" bottom: "1_prob" bottom: "label" top: "1_accuracy" include { phase: TEST } }
ensemble 层配置函数如下:
layer { name: "ensemble type: "Esemble" bottom: "prob1" bottom: "prob2" bottom: "prob3" bottom: "label" top: "ensemble_accuracy" include { phase: TEST }}
先训练弱分类器,用弱分类器的模型即可,如果把caffe训练好的模型当弱分类器,只需要调用caffe,使用该模型即可,不需要重新训练该弱分类器。
下面代码是调用caffe训练的模型,使用adaboost弱分类器。 这里主要使用了sklearn 库。
#!/usr/bin/env python# -*- coding: utf-8 -*-# author: Tairui Chenimport numpy as npimport osimport sysimport argparseimport globimport timefrom sklearn.base import BaseEstimator, ClassifierMixinfrom sklearn.ensemble import AdaBoostClassifier, BaggingClassifierimport caffeg_rnd = np.random.randint(100000)def create_weighted_db(X, y, weights, name='boost'): X = X.reshape(-1, 3, 32, 32) train_fn = os.path.join(DIR, name + '.h5') dd.io.save(train_fn, dict(data=X, label=y.astype(np.float32), sample_weight=weights), compress=False) with open(os.path.join(DIR, name + '.txt'), 'w') as f: print(train_fn, file=f)class CNN(BaseEstimator, ClassifierMixin): def __init__(self): pass def get_params(self, deep=False): return {} def fit(self, X, y, sample_weight=None): global g_seed global g_loop if sample_weight is None: sample_weight = np.ones(X.shape[0], np.float32) print('Calling fit with sample_weight None') else: sample_weight *= X.shape[0] print('Calling fit with sample_weight sum', sample_weight.sum()) #sample_weight = np.ones(X.shape[0], np.float32) #II = sample_weight > 0 #X = X[II] #y = y[II] #sample_weight = sample_weight[II] #sample_weight = np.ones(X.shape[0]) w = sample_weight #sample_weight[:10] = 0.0 #w[:1000] = 0.0 #w = sample_weight #w0 = w / w.sum() #print('Weight entropy:', -np.sum(w0 * np.log2(w0))) print('Weight max:', w.max()) print('Weight min:', w.min()) #import sys; sys.exit(0) self.classes_ = np.unique(y) self.n_classes_ = len(self.classes_) # Set up weighted database create_weighted_db(X, y, sample_weight) #steps = [(0.001, 2000, 2000)] steps = [(0.001, 0.004, 60000), (0.0001, 0.004, 5000), (0.00001, 0.004, 5000)] #steps = [(0.00001, 10000, 10000), (0.000001, 5000, 15000), (0.0000001, 5000, 20000)] #steps = [(0.001, 10000, 10000)] #steps = [(0.001, 200, 1000)] name = os.path.join(CONF_DIR, 'adaboost_{}_loop{}'.format(g_rnd, g_loop)) bare_conf_fn = os.path.join(CONF_DIR, 'boost_bare.prototxt') conf_fn = os.path.join(CONF_DIR, 'solver.prototxt.template') #bare_conf_fn = 'regaug_bare.prototxt' #conf_fn = 'regaug_solver.prototxt.template' net, info = train_model(name, conf_fn, bare_conf_fn, steps, seed=g_seed, device_id=DEVICE_ID) loss_fn = 'info/info_{}_loop{}.h5'.format(g_rnd, g_loop) dd.io.save(loss_fn, info) print('Saved to', loss_fn) g_loop += 1 print('Classifier set up') self.net_ = net def predict_proba(self, X): X = X.reshape(-1, 3, 32, 32) #X = X.transpose(0, 2, 3, 1) prob = np.zeros((X.shape[0], self.n_classes_)) M = 2500 for k in range(int(np.ceil(X.shape[0] / M))): y = self.net_.forward_all(data=X[k*M:(k+1)*M]).values()[0].squeeze(axis=(2,3)) prob[k*M:(k+1)*M] = y T = 30.0 eps = 0.0001 #prob = prob.clip(eps, 1-eps) log_prob = np.log(prob) print('log_prob', log_prob.min(), log_prob.max()) #log_prob = log_prob.clip(min=-4, max=4) new_prob = np.exp(log_prob / T) new_prob /= dd.apply_once(np.sum, new_prob, [1]) return new_prob def predict(self, X): prob = self.predict_proba(X) return prob.argmax(-1)train_data = np.load('G:/EDU/_SOURCE_CODE/chainer/examples/cifar10/data/train_data.npy')train_labels = np.load('G:/EDU/_SOURCE_CODE/chainer/examples/cifar10/data/train_labels.npy')model_path = 'cifar10/' # substitute your path here# GoogleNetnet_fn = model_path + 'VGG_mini_ABN.prototxt'param_fn = model_path + 'cifar10_vgg_iter_120000.caffemodel'caffe.set_mode_cpu()net = caffe.Classifier(net_fn, param_fn, mean = np.float32([104.0, 116.0, 122.0]), # ImageNet mean, training set dependent channel_swap = (2,1,0)) # the reference model has channels in BGR order instead of RGBdef preprocess(net, img): return np.float32(np.rollaxis(img, 2)[::-1]) - net.transformer.mean['data']for i in range(10):img = train_data[i].transpose((1, 2, 0)) * 255img = img.astype(np.uint8)[:, :, ::-1]end = 'prob'h, w = img.shape[:2]src, dst = net.blobs['data'], net.blobs[end]src.data[0] = preprocess(net, img)net.forward(end=end)features = dst.data[0].copy() X = train_datay = train_labelsX *= 255.0mean_x = X.mean(0)X -= mean_xte_X= np.load('G:/EDU/_SOURCE_CODE/chainer/examples/cifar10/data/test_data.npy')te_y = np.load('G:/EDU/_SOURCE_CODE/chainer/examples/cifar10/data/test_labels.npy')create_weighted_db(te_X, te_y, np.ones(te_X.shape[0], dtype=np.float32), name='test') clf = AdaBoostClassifier(base_estimator=CNN(), algorithm='SAMME.R', n_estimators=10, random_state=0)clf.fit(X.reshape(X.shape[0], -1), y)for i, score in enumerate(clf.staged_score(X.reshape(X.shape[0], -1), y)): print(i+1, 'train score', score)for i, score in enumerate(clf.staged_score(te_X.reshape(te_X.shape[0], -1), te_y)): print(i+1, 'test score', score)
阅读全文
0 0
- caffe ensemble(模型融合+adaboost)
- Ensemble Learning-模型融合-Python实现
- caffe模型融合
- 集成方法(ensemble method) Boosting Adaboost
- 集成学习(ensemble learning)之AdaBoost
- 笔记︱集成学习Ensemble Learning与树模型、Bagging 和 Boosting、模型融合
- 机器学习总结7_从模型融合到Adaboost
- 6. Ensemble learning & AdaBoost
- Hinton Neural Network课程笔记10a:融合模型Ensemble, Boosting, Bagging
- caffe adaboost
- 模型融合
- 模型融合
- 模型融合
- 模型融合
- 模型融合
- 模型融合
- 模型融合
- 模型融合
- Hadoop无法访问web50070端口
- HDOJ 2070 Fibbonacci Number
- Android 常用的依赖和权限
- 为什么知道那么多道理,还是过不好这一生,看看这里吧
- leetcode 120. Triangle
- caffe ensemble(模型融合+adaboost)
- Linux笔记
- uml
- Ubuntu下如何安装TensorFlow
- 杂记
- hive的学习_优化
- Linux平台卸载MySQL总结
- [吴恩达 DL]Class1 Week2 神经网络基础 + 逻辑回归代码实现
- pair排序 线段覆盖 贪心