Skip to content

Various ways to create One Hot Encoding

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.]])

Similar Posts:
Posted 29 December 2016 § Uncategorized ‡ Comments (0) °

Linear Regression using Keras

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)]

Similar Posts:
Posted 29 December 2016 § Uncategorized ‡ Comments (0) °

Bay Area Deep Learning School 2016

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.

Similar Posts:
Posted 18 December 2016 § Uncategorized ‡ Comments (0) °

reshape2 vs tidyr

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.

Similar Posts:
Posted 13 June 2016 § Uncategorized ‡ Comments (0) °

word2vec in tensorflow

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.

Similar Posts:
Posted 30 May 2016 § Uncategorized ‡ Comments (0) °

Playing go

인공지능 바둑에 대한 글들을 모아보려합니다.

The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions

Similar Posts:
Posted 26 May 2016 § Uncategorized ‡ Comments (0) °

LeNet-5

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.

Similar Posts:
Posted 17 May 2016 § Uncategorized ‡ Comments (0) °

Optimization algorithms

다양한 알고리즘의 동작 모습

Visualizing Optimization Algos

Similar Posts:
Posted 17 May 2016 § Uncategorized ‡ Comments (0) °

Changing numpy array dimension: from 1d to 2d

Method 1. Using [np.newaxis].

In [28]: a = np.array([5, 4])

In [29]: a
Out[29]: array([5, 4])

In [30]: a[np.newaxis]
Out[30]: array([[5, 4]])

In [31]: a[np.newaxis].T
Out[31]: 
array([[5],
       [4]])

In [35]: a[:, np.newaxis]
Out[35]: 
array([[5],
       [4]])

Method 2. Using None.

In [32]: a
Out[32]: array([5, 4])

In [33]: a[:, None]
Out[33]: 
array([[5],
       [4]])

In [34]: a[None].T
Out[34]: 
array([[5],
       [4]])

Method 3. Using reshape.

In [51]: a = np.array([5, 4])

# If a shape is -1, the value is inferred.
In [52]: a.reshape((-1, 1))
Out[52]: 
array([[5],
       [4]])

Method 4. Using expand_dims.

In [2]: x = np.array([1, 2])

In [4]: np.expand_dims(x, axis=0)
Out[4]: array([[1, 2]])

In [5]: np.expand_dims(x, axis=1)
Out[5]: 
array([[1],
       [2]])

Similar Posts:
Posted 09 May 2016 § Uncategorized ‡ Comments (0) °

Image dataset for deep learning

http://mscoco.org/

Microsoft COCO is a new image recognition, segmentation, and captioning dataset.

Crowd sourcing 으로 caption data 구축한다고 합니다.

Similar Posts:
Posted 02 May 2016 § Uncategorized ‡ Comments (0) °