Tensorflow 实现稠密输入数据的逻辑回归二分类
首先 实现一个尽可能少调用tf.nn模块儿的，自己手写相关的function
import tensorflow as tf
import numpy as np
import melt_dataset
import sys
from sklearn.metrics import roc_auc_score
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.01))
def model(X, w):
return 1.0/(1.0 + tf.exp(-(tf.matmul(X, w)))) #sigmoid
batch_size = 500
learning_rate = 0.001
num_iters = 1020
argv = sys.argv
trainset = argv[1]
testset = argv[2]
trX, trY = melt_dataset.load_dense_data(trainset)
print "finish loading train set ",trainset
teX, teY = melt_dataset.load_dense_data(testset)
print "finish loading test set ", testset
num_features = trX[0].shape[0]
print 'num_features: ',num_features
print 'trainSet size: ', len(trX)
print 'testSet size: ', len(teX)
print 'batch_size:', batch_size, ' learning_rate:', learning_rate, ' num_iters:', num_iters
X = tf.placeholder("float", [None, num_features]) # create symbolic variables
Y = tf.placeholder("float", [None, 1])
w = init_weights([num_features, 1]) # like in linear regression, we need a shared variable weight matrix for logistic regression
py_x = model(X, w)
cost = -tf.reduce_sum(Y*tf.log(py_x) + (1 - Y) * tf.log(1 - py_x))
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # construct optimizer
predict_op = py_x
sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
for i in range(num_iters):
predicts, cost_ = sess.run([predict_op, cost], feed_dict={X: teX, Y: teY})
print i, 'auc:', roc_auc_score(teY, predicts), 'cost:', cost_
for start, end in zip(range(0, len(trX), batch_size), range(batch_size, len(trX), batch_size)):
sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})
predicts, cost_ = sess.run([predict_op, cost], feed_dict={X: teX, Y: teY})
print 'final ', 'auc:', roc_auc_score(teY, predicts),'cost:', cost_
注意如果设置的batch_size 比较大 而learning rate也比较大 可能会出现nan， 可以通过减小batch_size
或者调小learning rate来避免
更好的方式是使用tensorflow自带的函数
tf.nn.sigmoid_cross_entropy_with_logits(logits, targets, name=None)
Computes sigmoid cross entropy given logits.
Measures the probability error in discrete classification tasks in which each class is independent and not mutually exclusive. For instance, one could perform multilabel classification where a picture can contain both an elephant and a dog at the same time.
For brevity, let x = logits, z = targets. The logistic loss is
原始的logistic误差,注意和 -tf.reduce_sum(Y*tf.log(py_x) + (1 - Y) * tf.log(1 - py_x)) 是等价的
x - x * z + log(1 + exp(-x))
Z = 1 , log(1 + exp(-x))
Z = 0 , x + log(1 + exp(-x) ) = log(1 + exp(x))
To ensure stability and avoid overflow, the implementation uses
为了避免溢出。。nan的产生。。
max(x, 0) - x * z + log(1 + exp(-abs(x)))
如果z = 1, x >= 0 和原始一致
如果z = 1, x < 0 那么 -x + log(1 + exp(x)) = log(1+ exp(x) / exp(x)) = log(1 + exp(-x)) 还是一样。。
如果z = 0, x <= 0 log(1 + exp(x))
如果 z = 0, x > 0 x + log(1 + exp(-x)) = log(1 + exp(x))
感觉就是避免了 exp(x) 而 x过大？ @TODO
logits and targets must have the same type and shape.
注意尽管采用这个避免了nan产生 但是实际看 过程的话 auc 会迭代中变化不稳定 感觉还是调整下learning rate比较好。
最后tf.nn.sigmoid也可以替代手写的
y = 1 / (1 + exp(-x)).
来自 <http://www.tensorflow.org/api_docs/python/nn.md#sigmoid>
最终版本
import tensorflow as tf
import numpy as np
import melt_dataset
import sys
from sklearn.metrics import roc_auc_score
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.01))
def model(X, w):
return tf.matmul(X,w)
batch_size = 500
learning_rate = 0.001
num_iters = 120
argv = sys.argv
trainset = argv[1]
testset = argv[2]
trX, trY = melt_dataset.load_dense_data(trainset)
print "finish loading train set ",trainset
teX, teY = melt_dataset.load_dense_data(testset)
print "finish loading test set ", testset
num_features = trX[0].shape[0]
print 'num_features: ',num_features
print 'trainSet size: ', len(trX)
print 'testSet size: ', len(teX)
print 'batch_size:', batch_size, ' learning_rate:', learning_rate, ' num_iters:', num_iters
X = tf.placeholder("float", [None, num_features]) # create symbolic variables
Y = tf.placeholder("float", [None, 1])
w = init_weights([num_features, 1]) # like in linear regression, we need a shared variable weight matrix for logistic regression
py_x = model(X, w)
cost = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(py_x, Y))
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # construct optimizer
predict_op = tf.nn.sigmoid(py_x)
sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
for i in range(num_iters):
predicts, cost_ = sess.run([predict_op, cost], feed_dict={X: teX, Y: teY})
print i, 'auc:', roc_auc_score(teY, predicts), 'cost:', cost_
for start, end in zip(range(0, len(trX), batch_size), range(batch_size, len(trX), batch_size)):
sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})
predicts, cost_ = sess.run([predict_op, cost], feed_dict={X: teX, Y: teY})
print 'final ', 'auc:', roc_auc_score(teY, predicts),'cost:', cost_
运行结果
./logistic_regression.py corpus/feature.normed.rand.12000.0_2.txt corpus/feature.normed.rand.12000.1_2.txt
/home/users/chenghuige/.jumbo/lib/python2.7/site-packages/sklearn/externals/joblib/_multiprocessing_helpers.py:29: UserWarning: This platform lacks a functioning sem_open implementation, therefore, the required synchronization primitives needed will not function, see issue 3770.. joblib will operate in serial mode
warnings.warn('%s. joblib will operate in serial mode' % (e,))
... loading data: corpus/feature.normed.rand.12000.0_2.txt
10000
finish loading train set corpus/feature.normed.rand.12000.0_2.txt
... loading data: corpus/feature.normed.rand.12000.1_2.txt
finish loading test set corpus/feature.normed.rand.12000.1_2.txt
num_features: 493
trainSet size: 10001
testSet size: 1999
batch_size: 500 learning_rate: 0.001 num_iters: 120
I tensorflow/core/common_runtime/local_device.cc:25] Local device intra op parallelism threads: 24
I tensorflow/core/common_runtime/local_session.cc:45] Local session inter op parallelism threads: 24
0 auc: 0.380243958856 cost: 1350.72
1 auc: 0.876460425134 cost: 538.122
2 auc: 0.894349010333 cost: 493.974
3 auc: 0.900608480969 cost: 479.184
4 auc: 0.904222074311 cost: 471.299
5 auc: 0.906476144619 cost: 466.076
6 auc: 0.908203871808 cost: 462.155
7 auc: 0.909598799088 cost: 458.986
8 auc: 0.910892234197 cost: 456.31
9 auc: 0.911966162982 cost: 453.987
10 auc: 0.912926798182 cost: 451.936
11 auc: 0.913835507154 cost: 450.105
12 auc: 0.91452234952 cost: 448.458
13 auc: 0.915244596132 cost: 446.968
14 auc: 0.915910195951 cost: 445.613
15 auc: 0.916483744731 cost: 444.375
16 auc: 0.916939279358 cost: 443.243
17 auc: 0.917430218232 cost: 442.202
18 auc: 0.917977803898 cost: 441.244
19 auc: 0.918386132865 cost: 440.36
20 auc: 0.918782660417 cost: 439.541
21 auc: 0.919139063156 cost: 438.782
22 auc: 0.919471863066 cost: 438.076
23 auc: 0.91976925873 cost: 437.418
24 auc: 0.920017088449 cost: 436.803
25 auc: 0.920342807509 cost: 436.229
26 auc: 0.920616600343 cost: 435.69
27 auc: 0.920784180439 cost: 435.185
28 auc: 0.920965922233 cost: 434.709
29 auc: 0.921171266858 cost: 434.261
30 auc: 0.921402574597 cost: 433.839
31 auc: 0.921548912146 cost: 433.439
32 auc: 0.921770778752 cost: 433.061
33 auc: 0.921959601395 cost: 432.703
34 auc: 0.922181468002 cost: 432.363
35 auc: 0.922372650928 cost: 432.04
36 auc: 0.922474143099 cost: 431.734
37 auc: 0.922618120365 cost: 431.441
38 auc: 0.92279278131 cost: 431.163
39 auc: 0.922929677727 cost: 430.897
40 auc: 0.92300992735 cost: 430.643
41 auc: 0.923146823767 cost: 430.401
42 auc: 0.923253036504 cost: 430.169
43 auc: 0.92331204358 cost: 429.947
44 auc: 0.923441859148 cost: 429.735
45 auc: 0.923557513017 cost: 429.531
46 auc: 0.923649564056 cost: 429.336
47 auc: 0.923741615094 cost: 429.148
48 auc: 0.923826585284 cost: 428.968
49 auc: 0.923897393775 cost: 428.795
50 auc: 0.923991805097 cost: 428.629
51 auc: 0.924067334155 cost: 428.469
52 auc: 0.924131061797 cost: 428.315
53 auc: 0.924161745477 cost: 428.166
54 auc: 0.924220752553 cost: 428.024
55 auc: 0.924251436232 cost: 427.886
56 auc: 0.92429628161 cost: 427.754
57 auc: 0.924355288686 cost: 427.627
58 auc: 0.924440258876 cost: 427.504
59 auc: 0.924515787933 cost: 427.385
60 auc: 0.924560633311 cost: 427.27
61 auc: 0.92465268435 cost: 427.16
62 auc: 0.924699890011 cost: 427.053
63 auc: 0.924780139634 cost: 426.95
64 auc: 0.924834426144 cost: 426.851
65 auc: 0.924829705578 cost: 426.755
66 auc: 0.924867470107 cost: 426.663
67 auc: 0.924891072937 cost: 426.573
68 auc: 0.924907594919 cost: 426.487
69 auc: 0.924950080014 cost: 426.404
70 auc: 0.924961881429 cost: 426.323
71 auc: 0.925023248788 cost: 426.245
72 auc: 0.925051572185 cost: 426.17
73 auc: 0.925079895581 cost: 426.097
74 auc: 0.925120020393 cost: 426.027
75 auc: 0.92517194662 cost: 425.959
76 auc: 0.925240394828 cost: 425.894
77 auc: 0.925294681338 cost: 425.83
78 auc: 0.925330085584 cost: 425.769
79 auc: 0.925391452943 cost: 425.71
80 auc: 0.925410335207 cost: 425.653
81 auc: 0.925436298321 cost: 425.598
82 auc: 0.925485864265 cost: 425.544
83 auc: 0.925500025963 cost: 425.493
84 auc: 0.925530709643 cost: 425.443
85 auc: 0.92555195219 cost: 425.395
86 auc: 0.925594437285 cost: 425.349
87 auc: 0.92563692238 cost: 425.304
88 auc: 0.925665245776 cost: 425.261
89 auc: 0.925703010305 cost: 425.219
90 auc: 0.925721892569 cost: 425.179
91 auc: 0.925771458513 cost: 425.14
92 auc: 0.925778539362 cost: 425.103
93 auc: 0.925778539362 cost: 425.066
94 auc: 0.925771458513 cost: 425.032
95 auc: 0.925780899645 cost: 424.998
96 auc: 0.925809223042 cost: 424.966
97 auc: 0.925806862759 cost: 424.935
98 auc: 0.925835186156 cost: 424.905
99 auc: 0.925851708137 cost: 424.876
100 auc: 0.925868230118 cost: 424.848
101 auc: 0.925863509552 cost: 424.821
102 auc: 0.925887112383 cost: 424.795
103 auc: 0.925905994647 cost: 424.77
104 auc: 0.925922516628 cost: 424.747
105 auc: 0.925915435779 cost: 424.724
106 auc: 0.925934318043 cost: 424.702
107 auc: 0.925920156345 cost: 424.681
108 auc: 0.925965001723 cost: 424.661
109 auc: 0.925955560591 cost: 424.642
110 auc: 0.926005126535 cost: 424.623
111 auc: 0.926026369082 cost: 424.605
112 auc: 0.926035810214 cost: 424.588
113 auc: 0.926009847101 cost: 424.572
114 auc: 0.926000405969 cost: 424.557
115 auc: 0.926021648516 cost: 424.542
116 auc: 0.92604053078 cost: 424.528
117 auc: 0.926057052762 cost: 424.514
118 auc: 0.926075935026 cost: 424.501
119 auc: 0.926090096724 cost: 424.489
final auc: 0.926087736441 cost: 424.478
程序另外一个需要注意的是 Y,label 需要
[batch_size, 1]这样
[
[0],
[1],
[0],
…
]
不能是[0,1,0…]也就是[batch_size,]是不行的
看一下LinearSVM和gbdt的结果
mlt -c tt ./corpus/feature.normed.rand.12000.0_2.txt ./corpus/feature.normed.rand.12000.1_2.tx
Confusion table:
||===============================||
|| PREDICTED ||
TRUTH || positive | negative || RECALL
||===============================||
positive|| 676 | 532 || 0.5596 (676/1208)
negative|| 192 | 8601 || 0.9782 (8601/8793)
||===============================||
PRECISION 0.7788 (676/868) 0.9417(8601/9133)
LOG-LOSS/instance: 0.2412
LOG-LOSS-PROB/instance: 0.1912
TEST-SET ENTROPY (prior LL/in): 0.3685
LOG-LOSS REDUCTION (RIG): 48.1082%
OVERALL 0/1 ACCURACY: 0.9276 (9277/10001)
POS.PRECISION: 0.7788
POS.RECALL: 0.5596
NEG.PRECISION: 0.9417
NEG.RECALL: 0.9782
F1.SCORE: 0.6513
OuputAUC: 0.9309
AUC: [0.9309]
mlt -c tt ./corpus/feature.normed.rand.12000.0_2.txt ./corpus/feature.normed.rand.12000.1_2.txt -cl gbdt
Confusion table:
||===============================||
|| PREDICTED ||
TRUTH || positive | negative || RECALL
||===============================||
positive|| 1194 | 14 || 0.9884 (1194/1208)
negative|| 0 | 8793 || 1.0000 (8793/8793)
||===============================||
PRECISION 1.0000 (1194/1194) 0.9984(8793/8807)
LOG-LOSS/instance: 0.0214
LOG-LOSS-PROB/instance: 0.0097
TEST-SET ENTROPY (prior LL/in): 0.3685
LOG-LOSS REDUCTION (RIG): 97.3625%
OVERALL 0/1 ACCURACY: 0.9986 (9987/10001)
POS.PRECISION: 1.0000
POS.RECALL: 0.9884
NEG.PRECISION: 0.9984
NEG.RECALL: 1.0000
F1.SCORE: 0.9942
OuputAUC: 0.9988
AUC: [0.9988]
