Covariate shift – A Literature Survey on Domain Adaptation of Statistical Classifiers
Why does batch normalization help? – Quora
Batch Normalization – SanghyukChun’s Blog
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import bcolz def save_array(fname, arr): c=bcolz.carray(arr, rootdir=fname, mode='w'); c.flush() def load_array(fname): return bcolz.open(fname)[:]
Code snippet from http://course.fast.ai/.
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Using numpy:
In [2]: x = np.array([0, 1, 2, 0, 0]) In [4]: x[:, np.newaxis] Out[4]: array([[0], [1], [2], [0], [0]]) # Broadcasting. In [5]: np.arange(3) == x[:, np.newaxis] Out[5]: array([[ True, False, False], [False, True, False], [False, False, True], [ True, False, False], [ True, False, False]], dtype=bool) # Just change the boolean to the int. In [35]: (np.arange(3) == x[:, np.newaxis]).astype(np.float) Out[35]: array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.], [ 1., 0., 0.], [ 1., 0., 0.]])
Using sklearn:
In [8]: x Out[8]: array([0, 1, 2, 0, 0]) In [9]: from sklearn.preprocessing import OneHotEncoder # Reshape changes shape of the x to (5, 1). In [10]: x.reshape(-1, 1) Out[10]: array([[0], [1], [2], [0], [0]]) # Return value of fit_transform() is csr_matrix. todense() changes it to numpy matrix. In [11]: OneHotEncoder().fit_transform(x.reshape(-1, 1)).todense() Out[11]: matrix([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.], [ 1., 0., 0.], [ 1., 0., 0.]])
Using keras’s numpy util.
In [12]: x Out[12]: array([0, 1, 2, 0, 0]) In [13]: from keras.utils.np_utils import to_categorical In [14]: to_categorical(x, len(set(x))) Out[14]: array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.], [ 1., 0., 0.], [ 1., 0., 0.]])
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This is example code to perform linear regression using keras.
import keras from keras.models import Sequential from keras.layers import Dense from keras.optimizers import SGD import numpy as np from numpy.random import random x = random((30, 2)) w = np.array([3., 2.]) b = 1. y = np.dot(x, w) + b print x print y model = Sequential() # 30 observations. Each observation has 2 features. model.add(Dense(1, input_shape=(2, ))) # MSE because we want linear regression. model.compile(optimizer=SGD(lr=0.1), loss='mse') model.fit(x, y, nb_epoch=60, batch_size=1) print model.get_weights()
Output:
Using Theano backend. Using gpu device 0: GeForce GTX 1080 (CNMeM is disabled, cuDNN 5105) [[ 0.66716875 0.44536398] [ 0.58253148 0.45428442] [ 0.49094032 0.77897022] [ 0.16345683 0.27807741] [ 0.29913647 0.87666064] [ 0.41150342 0.4329423 ] [ 0.30724994 0.56194767] [ 0.1448368 0.61572276] [ 0.89288341 0.45568394] [ 0.20008006 0.1446671 ] [ 0.58501705 0.1407299 ] [ 0.8924096 0.58803216] [ 0.76954948 0.95146172] [ 0.17315788 0.03576668] [ 0.01515587 0.36599027] [ 0.77232613 0.35686848] [ 0.4897022 0.52092717] [ 0.50756237 0.20100097] [ 0.8372522 0.53871228] [ 0.2223611 0.5919245 ] [ 0.89898591 0.24163213] [ 0.571022 0.2140571 ] [ 0.55041835 0.00383233] [ 0.08253098 0.64526628] [ 0.3512973 0.53963146] [ 0.73578765 0.65469051] [ 0.91344962 0.40350727] [ 0.74023006 0.34414037] [ 0.41329666 0.22543498] [ 0.82787326 0.41838276]] [ 3.8922342 3.65616327 4.03076141 2.0465253 3.6507307 3.10039487 3.04564516 2.66595593 4.59001811 1.88957438 3.03651096 4.85329311 5.21157187 1.591007 1.77744815 4.03071534 3.51096094 2.92468906 4.58918115 2.8509323 4.18022197 3.14118018 2.65891972 2.53812548 3.13315483 4.51674398 4.54736341 3.90897091 2.69075993 4.32038531] Epoch 1/60 30/30 [==============================] - 0s - loss: 1.6538 Epoch 2/60 30/30 [==============================] - 0s - loss: 0.1377 Epoch 3/60 30/30 [==============================] - 0s - loss: 0.0793 Epoch 4/60 30/30 [==============================] - 0s - loss: 0.0370 Epoch 5/60 30/30 [==============================] - 0s - loss: 0.0265 Epoch 6/60 30/30 [==============================] - 0s - loss: 0.0146 Epoch 7/60 30/30 [==============================] - 0s - loss: 0.0096 Epoch 8/60 30/30 [==============================] - 0s - loss: 0.0043 Epoch 9/60 30/30 [==============================] - 0s - loss: 0.0030 Epoch 10/60 30/30 [==============================] - 0s - loss: 0.0020 Epoch 11/60 30/30 [==============================] - 0s - loss: 0.0012 Epoch 12/60 30/30 [==============================] - 0s - loss: 6.9193e-04 Epoch 13/60 30/30 [==============================] - 0s - loss: 3.6242e-04 Epoch 14/60 30/30 [==============================] - 0s - loss: 2.2359e-04 Epoch 15/60 30/30 [==============================] - 0s - loss: 1.1410e-04 Epoch 16/60 30/30 [==============================] - 0s - loss: 7.5656e-05 Epoch 17/60 30/30 [==============================] - 0s - loss: 4.6557e-05 Epoch 18/60 30/30 [==============================] - 0s - loss: 2.9460e-05 Epoch 19/60 30/30 [==============================] - 0s - loss: 1.6638e-05 Epoch 20/60 30/30 [==============================] - 0s - loss: 1.0647e-05 Epoch 21/60 30/30 [==============================] - 0s - loss: 6.4342e-06 Epoch 22/60 30/30 [==============================] - 0s - loss: 3.5493e-06 Epoch 23/60 30/30 [==============================] - 0s - loss: 1.8375e-06 Epoch 24/60 30/30 [==============================] - 0s - loss: 1.3024e-06 Epoch 25/60 30/30 [==============================] - 0s - loss: 8.3916e-07 Epoch 26/60 30/30 [==============================] - 0s - loss: 5.3163e-07 Epoch 27/60 30/30 [==============================] - 0s - loss: 2.8679e-07 Epoch 28/60 30/30 [==============================] - 0s - loss: 1.5040e-07 Epoch 29/60 30/30 [==============================] - 0s - loss: 1.1201e-07 Epoch 30/60 30/30 [==============================] - 0s - loss: 6.0981e-08 Epoch 31/60 30/30 [==============================] - 0s - loss: 4.7074e-08 Epoch 32/60 30/30 [==============================] - 0s - loss: 2.9919e-08 Epoch 33/60 30/30 [==============================] - 0s - loss: 1.6059e-08 Epoch 34/60 30/30 [==============================] - 0s - loss: 9.3970e-09 Epoch 35/60 30/30 [==============================] - 0s - loss: 5.7633e-09 Epoch 36/60 30/30 [==============================] - 0s - loss: 3.3312e-09 Epoch 37/60 30/30 [==============================] - 0s - loss: 2.1822e-09 Epoch 38/60 30/30 [==============================] - 0s - loss: 1.2432e-09 Epoch 39/60 30/30 [==============================] - 0s - loss: 6.8956e-10 Epoch 40/60 30/30 [==============================] - 0s - loss: 4.4050e-10 Epoch 41/60 30/30 [==============================] - 0s - loss: 2.5711e-10 Epoch 42/60 30/30 [==============================] - 0s - loss: 1.5499e-10 Epoch 43/60 30/30 [==============================] - 0s - loss: 8.9069e-11 Epoch 44/60 30/30 [==============================] - 0s - loss: 4.6494e-11 Epoch 45/60 30/30 [==============================] - 0s - loss: 3.0863e-11 Epoch 46/60 30/30 [==============================] - 0s - loss: 1.5283e-11 Epoch 47/60 30/30 [==============================] - 0s - loss: 8.9050e-12 Epoch 48/60 30/30 [==============================] - 0s - loss: 4.8648e-12 Epoch 49/60 30/30 [==============================] - 0s - loss: 3.5887e-12 Epoch 50/60 30/30 [==============================] - 0s - loss: 2.0066e-12 Epoch 51/60 30/30 [==============================] - 0s - loss: 1.1189e-12 Epoch 52/60 30/30 [==============================] - 0s - loss: 6.5086e-13 Epoch 53/60 30/30 [==============================] - 0s - loss: 3.3727e-13 Epoch 54/60 30/30 [==============================] - 0s - loss: 1.7621e-13 Epoch 55/60 30/30 [==============================] - 0s - loss: 9.8529e-14 Epoch 56/60 30/30 [==============================] - 0s - loss: 5.6370e-14 Epoch 57/60 30/30 [==============================] - 0s - loss: 5.3054e-14 Epoch 58/60 30/30 [==============================] - 0s - loss: 4.9738e-14 Epoch 59/60 30/30 [==============================] - 0s - loss: 3.9790e-14 Epoch 60/60 30/30 [==============================] - 0s - loss: 4.4527e-14 [array([[ 3. ], [ 1.99999952]], dtype=float32), array([ 1.00000024], dtype=float32)]
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장장 20시간의 강의가 유튜브에 올라와 있습니다.
Day 1: https://youtu.be/eyovmAtoUx0
Day 2: https://youtu.be/9dXiAecyJrY
그리고 http://www.bayareadlschool.org/schedule 에 보시면 발표 자료가 있습니다.
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같은 듯 다른 듯한 두 패키지를 잘 비교한글이 있어 링크합니다.
Comparison of the reshape2 and tidyr packages
tidyr은 어떤일이 벌어지는지 하나하나 적는 형식이라 단계별로 알기 쉽고 함축이 없어 명확한 반면, reshape은 좀 더 선언적이라 코드가 짧고 하고자 하는 일 자체를 더 잘 설명하지 않나 싶습니다. 코드가 선언적이어야하는지 절차적이어야하는지 이 두가지는 항상 업치락뒤치락 하면서 같이 가는 것 같습니다.
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공부하면서 참고한 자료들 올려봅니다.
Tensorflow tutorial
word2vec_basic.py에 대한 주석붙인 설명
CBOW와 Skip-gram의 차이
Xin Rong, word2vec Parameter Learning Explained. Context word가 여러개일때 어떻게 훈련하는가. 결론은 입력도 출력도 평균을 사용한다는 것.
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LeNet-5, convolutional neural networks
LeNet-5 is our latest convolutional network designed for handwritten and machine-printed character recognition.
See LeNet 5 architecture diagram.
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