識別谷歌街景圖片中的字母
https://github.com/lijingpeng/kaggle/tree/master/competitions/image_recognize
street-view-getting-started-with-julia 讓我們從谷歌街景的圖片中鑑定字母，這個題目是讓我們學習和使用Julia，Julia有python和R的易用性，有C語言的速度，無奈對Julia不是很熟悉，所以還是想用python來試試。
import cv2
import numpy as np
import sys
import pandas as pd我們希望所有的圖片最後存儲在一個numpy的矩陣當中，每一行爲圖片的像素值。爲了得到統一的表達呢，我們將RGB三個通道的值做平均得到的灰度圖像作爲每個圖片的表示:
# typeData 爲"train"或者"test"
# labelsInfo 包含每一個圖片的ID
# 圖片存儲在trainResized和testResized文件夾內
def read_data(typeData, labelsInfo, imageSize):
labelsIndex = labelsInfo["ID"]
x = np.zeros((np.size(labelsIndex), imageSize))
for idx, idImage in enumerate(labelsIndex):
# 得到圖片文件名並讀取
nameFile = typeData + "Resized/" + str(idImage) + ".Bmp"
img = cv2.imread(nameFile)
# 轉化爲灰度圖
temp = np.mean(img, 2)
# 將圖片轉化爲行向量
x[idx, :] = np.reshape(temp, (1, imageSize))
return x預處理訓練集和測試集
imageSize = 400
trainlabels = pd.read_csv("trainLabels.csv")
testlabels = pd.read_csv("sampleSubmission.csv")
# 得到訓練集的特徵
xTrain = read_data('train', trainlabels, imageSize)
# 得到測試集的特徵
xTest = read_data("test", testlabels, imageSize)預覽數據：
print trainlabels.head(2)
print testlabels.head(2) ID Class
0 1 n
1 2 8
ID Class
0 6284 A
1 6285 A
yTrain = trainlabels["Class"]
yTrain = [ord(x) for x in yTrain]模型訓練
隨機森林
使用隨機森林進行訓練，樹的個數和深度需要多次調解尋求最佳值
from sklearn.ensemble import RandomForestClassifier
%time rfc = RandomForestClassifier(n_estimators = 500, max_features = 50, max_depth=None)
rfc.fit(xTrain, yTrain)CPU times: user 121 µs, sys: 367 µs, total: 488 µs
Wall time: 494 µs
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features=50, max_leaf_nodes=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=1,
oob_score=False, random_state=None, verbose=0,
warm_start=False)
預測
將訓練後的模型應用到測試集上，並保存結果：
predTest = rfc.predict(xTest)
predResult = [chr(x) for x in predTest]
testlabels["Class"] = predResult
testlabels.to_csv("rf_500_50_result.csv",index = None)結果
使用50顆樹進行訓練，提交kaggle之後準確率約爲0.40
改用300顆樹進行訓練，提交kaggle之後準確率爲0.46695
改用500顆樹進行訓練，深度爲10，提價kaggle後準確率爲0.40，估計出現了過擬合
改用500顆樹進行訓練，不設置深度，提價kaggle後準確率爲0.47480
貝葉斯
from sklearn.naive_bayes import GaussianNB as GNB
model_GNB = GNB()
model_GNB.fit(xTrain, yTrain)
predTest = model_GNB.predict(xTest)
predResult = [chr(x) for x in predTest]
testlabels["Class"] = predResult
testlabels.to_csv("gnb_result.csv",index = None)貝葉斯的訓練非常的快，把結果提交kaggle後，得到0.02389的準確率，明顯低於隨機森林
GBDT
from sklearn.ensemble import GradientBoostingClassifier
%time GBDT = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, \
min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, init=None, \
random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort='auto')
%time GBDT.fit(xTrain, yTrain)
%time predTest = GBDT.predict(xTest)
predResult = [chr(x) for x in predTest]
testlabels["Class"] = predResult
testlabels.to_csv("gbdt_result.csv",index = None)CPU times: user 91 µs, sys: 738 µs, total: 829 µs
Wall time: 2.93 ms
CPU times: user 40min 16s, sys: 52.3 s, total: 41min 9s
Wall time: 2h 55min 22s
CPU times: user 1.75 s, sys: 44.5 ms, total: 1.8 s
Wall time: 1.79 s
使用GBDT僅得到了0.31937的準確率，可能是我的默認參數沒有調節好，關鍵是GBDT的訓練時間太長，調試成本也比較高
神經網絡
import os
from skimage.io import imread
from lasagne import layers
from lasagne.nonlinearities import softmax
from nolearn.lasagne import NeuralNet, BatchIterator# Define functions
def read_datax(typeData, labelsInfo, imageSize, path):
x = np.zeros((labelsInfo.shape[0], imageSize))
for (index, idImage) in enumerate(labelsInfo['ID']):
# use specially created 32 x 32 images
nameFile = '{0}/{1}Resized32/{2}.Bmp'.format(path,
typeData, idImage)
img = imread(nameFile, as_grey = True)
x[index, :] = np.reshape(img, (1, imageSize))
return x
def fit_model(reshaped_train_x, y, image_width,
image_height, reshaped_test_x):
net = NeuralNet(
layers = [
('input', layers.InputLayer),
('conv1', layers.Conv2DLayer),
('pool1', layers.MaxPool2DLayer),
('dropout1', layers.DropoutLayer),
('conv2', layers.Conv2DLayer),
('pool2', layers.MaxPool2DLayer),
('dropout2', layers.DropoutLayer),
('conv3', layers.Conv2DLayer),
('hidden4', layers.DenseLayer),
('output', layers.DenseLayer),
],
input_shape = (None, 1, 32, 32),
conv1_num_filters=32, conv1_filter_size=(5, 5),
pool1_pool_size=(2, 2),
dropout1_p=0.2,
conv2_num_filters=64, conv2_filter_size=(5, 5),
pool2_pool_size=(2, 2),
dropout2_p=0.2,
conv3_num_filters = 128, conv3_filter_size = (5, 5),
hidden4_num_units=500,
output_num_units = 62, output_nonlinearity = softmax,
update_learning_rate = 0.01,
update_momentum = 0.9,
batch_iterator_train = BatchIterator(batch_size = 100),
batch_iterator_test = BatchIterator(batch_size = 100),
use_label_encoder = True,
regression = False,
max_epochs = 100,
verbose = 1,
)
net.fit(reshaped_train_x, y)
prediction = net.predict(reshaped_test_x)
return prediction# 預處理數據，首先將圖片保存爲32*32的小圖片
imageSize = 1024 # 32 x 32
image_width = image_height = int(imageSize ** 0.5)
labelsInfoTrain = pd.read_csv\
('trainLabels.csv'.format(path))
labelsInfoTest = pd.read_csv\
('sampleSubmission.csv'.format(path))
# Load dataset
nnxTrain = read_datax('train', labelsInfoTrain, imageSize, '.')
nnxTest = read_datax('test', labelsInfoTest, imageSize, '.')
nnyTrain = map(ord, labelsInfoTrain['Class'])
nnyTrain = np.array(yTrain)# 歸一化數據
nnxTrain /= nnxTrain.std(axis = None)
nnxTrain -= nnxTrain.mean()
nnxTest /= nnxTest.std(axis = None)
nnxTest -= nnxTest.mean()# Reshape data
train_x_reshaped = nnxTrain.reshape(nnxTrain.shape[0], 1,
image_height, image_width).astype('float32')
test_x_reshaped = nnxTest.reshape(nnxTest.shape[0], 1,
image_height, image_width).astype('float32')# 進行訓練和測試
predict = fit_model(train_x_reshaped, nnyTrain, image_width, image_height, test_x_reshaped)# Neural Network with 352586 learnable parameters
## Layer information
# name size
--- -------- --------
0 input 1x32x32
1 conv1 32x28x28
2 pool1 32x14x14
3 dropout1 32x14x14
4 conv2 64x10x10
5 pool2 64x5x5
6 dropout2 64x5x5
7 conv3 128x1x1
8 hidden4 500
9 output 62
epoch trn loss val loss trn/val valid acc dur
------- ---------- ---------- --------- ----------- ------
1 [36m4.08201[0m [32m4.01012[0m 1.01793 0.07254 16.55s
2 [36m3.87688[0m [32m3.84326[0m 1.00875 0.04836 17.72s
3 [36m3.82788[0m [32m3.79976[0m 1.00740 0.04914 16.58s
4 [36m3.78741[0m [32m3.78872[0m 0.99965 0.07254 16.14s
5 [36m3.78030[0m [32m3.78600[0m 0.99850 0.07254 16.37s
6 [36m3.77679[0m [32m3.78520[0m 0.99778 0.07254 16.56s
7 [36m3.77487[0m 3.78537 0.99723 0.07254 16.30s
8 [36m3.77411[0m [32m3.78468[0m 0.99721 0.07254 16.51s
9 [36m3.77257[0m 3.78518 0.99667 0.07254 15.92s
10 [36m3.77202[0m [32m3.78459[0m 0.99668 0.07254 16.55s
11 [36m3.76948[0m [32m3.78458[0m 0.99601 0.07254 16.25s
12 [36m3.76882[0m [32m3.78414[0m 0.99595 0.07254 16.31s
13 [36m3.76717[0m [32m3.78411[0m 0.99552 0.07254 15.70s
14 [36m3.76606[0m 3.78469 0.99508 0.07254 16.04s
15 [36m3.76419[0m 3.78671 0.99405 0.07176 15.70s
16 [36m3.76277[0m [32m3.78392[0m 0.99441 0.07176 16.05s
17 [36m3.76014[0m 3.78821 0.99259 0.07176 15.71s
18 3.78179 3.78606 0.99887 0.07254 16.11s
19 3.76928 [32m3.78321[0m 0.99632 0.07254 15.75s
20 3.76688 3.78358 0.99559 0.07254 16.05s
21 3.76434 [32m3.78255[0m 0.99519 0.07254 17.36s
22 3.76186 [32m3.78174[0m 0.99474 0.07254 18.12s
23 [36m3.75829[0m 3.78184 0.99377 0.07878 17.90s
24 [36m3.75370[0m 3.78545 0.99161 0.07488 18.19s
25 [36m3.74749[0m [32m3.77908[0m 0.99164 0.07098 17.81s
26 [36m3.73650[0m [32m3.77806[0m 0.98900 0.07020 18.08s
27 [36m3.71592[0m [32m3.77626[0m 0.98402 0.06474 18.03s
28 [36m3.67805[0m [32m3.74531[0m 0.98204 0.07176 18.04s
29 [36m3.59550[0m 3.79802 0.94668 0.07566 18.12s
30 [36m3.44086[0m [32m3.35483[0m 1.02564 0.19111 18.06s
31 [36m3.14160[0m [32m3.00021[0m 1.04713 0.29251 17.41s
32 [36m2.73389[0m [32m2.89130[0m 0.94556 0.31903 16.19s
33 [36m2.61587[0m [32m2.53098[0m 1.03354 0.38144 15.73s
34 [36m2.25316[0m [32m2.26086[0m 0.99660 0.43994 16.14s
35 [36m1.95499[0m [32m2.03661[0m 0.95993 0.48206 15.76s
36 [36m1.75483[0m [32m1.94987[0m 0.89997 0.49610 16.01s
37 [36m1.60276[0m [32m1.78637[0m 0.89722 0.52106 15.60s
38 [36m1.47862[0m [32m1.73524[0m 0.85211 0.54524 15.98s
39 [36m1.35049[0m [32m1.65705[0m 0.81500 0.55694 15.62s
40 [36m1.27458[0m [32m1.65253[0m 0.77129 0.57254 16.01s
41 [36m1.18548[0m [32m1.60550[0m 0.73839 0.58112 15.61s
42 [36m1.11862[0m 1.62259 0.68940 0.58268 16.51s
43 [36m1.05698[0m 1.68044 0.62899 0.58112 16.24s
44 [36m1.01350[0m 1.64642 0.61558 0.59126 16.50s
45 [36m0.93587[0m 1.62059 0.57749 0.59906 15.81s
46 [36m0.87893[0m 1.65983 0.52953 0.59984 16.54s
47 [36m0.83695[0m 1.66309 0.50325 0.60452 16.42s
48 1.72887 2.92194 0.59169 0.54446 16.31s
49 3.85830 3.39520 1.13640 0.21373 15.84s
50 2.26598 1.97743 1.14592 0.46724 18.41s
51 2.11105 1.89927 1.11150 0.49298 18.02s
52 1.66393 1.75705 0.94700 0.51794 17.99s
53 1.48332 1.65795 0.89467 0.54212 17.94s
54 1.38197 [32m1.60296[0m 0.86214 0.55928 17.73s
55 1.28419 [32m1.56050[0m 0.82293 0.56318 17.94s
56 1.21078 [32m1.54983[0m 0.78123 0.57176 17.70s
57 1.13885 1.55330 0.73318 0.55616 17.93s
58 1.10488 [32m1.53462[0m 0.71997 0.57956 17.71s
59 1.03479 1.54234 0.67092 0.58502 17.70s
60 0.98439 [32m1.52492[0m 0.64554 0.59984 17.95s
61 0.93277 [32m1.49128[0m 0.62548 0.59204 17.67s
62 1.03055 1.58280 0.65109 0.57878 18.01s
63 0.89008 1.54904 0.57460 0.59750 17.69s
64 0.83698 1.59463 0.52487 0.58346 17.92s
65 [36m0.79801[0m 1.59534 0.50021 0.60452 17.80s
66 [36m0.77752[0m 1.56702 0.49618 0.60842 17.91s
67 [36m0.73901[0m 1.61821 0.45668 0.59594 17.81s
68 [36m0.71108[0m 1.56703 0.45377 0.61154 17.98s
69 [36m0.67279[0m 1.61497 0.41659 0.61154 17.81s
70 [36m0.64651[0m 1.66452 0.38841 0.60530 17.97s
71 [36m0.61597[0m 1.65828 0.37145 0.62012 17.84s
72 [36m0.59188[0m 1.69796 0.34858 0.60296 17.92s
73 [36m0.57862[0m 1.72392 0.33564 0.60686 17.73s
74 [36m0.56451[0m 1.75449 0.32175 0.60062 17.56s
75 [36m0.53835[0m 1.74351 0.30877 0.62090 17.77s
76 [36m0.53288[0m 1.80642 0.29499 0.60842 18.08s
77 [36m0.49975[0m 1.76941 0.28244 0.61700 17.76s
78 [36m0.48489[0m 1.75930 0.27561 0.60998 17.92s
79 [36m0.45688[0m 1.81943 0.25111 0.61622 17.78s
80 0.46801 1.80187 0.25974 0.62480 17.96s
81 [36m0.45527[0m 1.88136 0.24199 0.61310 17.84s
82 [36m0.43178[0m 1.93961 0.22261 0.61622 18.56s
83 [36m0.41726[0m 1.90341 0.21922 0.61856 16.52s
84 [36m0.38590[0m 1.91029 0.20201 0.61778 15.59s
85 [36m0.38510[0m 1.93524 0.19900 0.61778 16.00s
86 [36m0.37565[0m 1.92514 0.19513 0.61466 15.56s
87 [36m0.36222[0m 1.99870 0.18123 0.61544 15.88s
88 0.38495 2.08839 0.18433 0.61466 15.55s
89 [36m0.34101[0m 1.94872 0.17499 0.62559 15.97s
90 [36m0.33575[0m 2.01506 0.16662 0.61856 15.63s
91 [36m0.32353[0m 2.05956 0.15709 0.62090 16.03s
92 [36m0.30422[0m 2.12548 0.14313 0.64041 15.66s
93 [36m0.29631[0m 2.10645 0.14067 0.63495 16.02s
94 0.32050 2.11861 0.15128 0.62168 15.73s
95 0.30140 2.14516 0.14050 0.62871 15.99s
96 [36m0.28195[0m 2.09292 0.13472 0.63339 15.67s
97 0.30323 2.20744 0.13737 0.62246 16.07s
98 [36m0.27107[0m 2.15645 0.12570 0.63729 16.32s
99 0.27947 2.22565 0.12557 0.62637 16.51s
100 [36m0.26500[0m 2.22825 0.11893 0.64431 16.52s
# 保存結果
yTest = map(chr, predict)
labelsInfoTest['Class'] = yTest
labelsInfoTest.to_csv('nnresult.csv'.format(path), index = False)提交kaggle之後的準確率：0.64562