머신러닝스터디/2016
머신러닝스터디/2016/목차

내용 


tokenizer = Tokenizer(nb_words=1000)
X_train = tokenizer.sequences_to_matrix(X_train, mode="freq")

  • optimizer
    • adamax 를 썼는데 accuracy가 50% 대에 머무름
      • tensorflow는 adamax를 제공하지 않음. keras 자체 구현됨(code(https://github.com/fchollet/keras/blob/c30432a665e23d598d2cec11094b0cc3cac2f46d/keras/optimizers.py#L356)).

  • 적절한 batch size
    • batch size가 너무 작으면(e.g. 32) 학습이 오래 걸린다.
    • 반면 너무 크면 메모리를 많이 사용하게 된다.

코드 

import keras
import numpy as np
from keras.datasets import imdb
from keras.preprocessing.text import Tokenizer
from keras.models import Sequential
from keras.layers import Dense, Dropout, Embedding, LSTM

(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=1000)

from keras.preprocessing.sequence import pad_sequences
X_train = pad_sequences(X_train, 1000)
X_test = pad_sequences(X_test, 1000)

model = Sequential()
model.add(Embedding(1000, 64, input_length=1000))
model.add(LSTM(output_dim=32, activation='sigmoid', inner_activation='hard_sigmoid'))
model.add(Dense(16, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(8, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(1, activation="sigmoid"))

model.compile(loss="binary_crossentropy", optimizer="adagrad", metrics=["accuracy"])

model.fit(X_train, y_train, batch_size=500, nb_epoch=100)
model.evaluate(X_test, y_test, batch_size=1000)
pred = model.predict(X_test, batch_size=20000)

print (pred[0], y_test[0])
print (pred[1], y_test[1])
print (pred[2], y_test[2])

Padding

pad_sequences은 배열의 길이가 다를 때 특정값을 채워넣어 길이를 맞춘다.
X_train = pad_sequences(X_train, 1000)
위의 코드는 X_train의 인풋 배열중 1000보다 길이가 짧은 배열에 0을 채워넣는다.
그러나 1000보다 더 긴 배열을 줄여주진 않는다. 
(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=1000)
다음과 같이 input length가 될 단어 인덱스 길이를 1000으로 제한해야 한다.

결과 예시
array([  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
         0,   0,   1,  20,  28, 716,  48, 495,  79,  27, 493,   8,   2,
         7,  50,   5,   2,   2,  10,   5, 852, 157,  11,   5,   2,   2,
        10,   5, 500,   2,   6,  33, 256,  41,   2,   7,  17,  23,  48,
         2,   2,  26, 269, 929,  18,   2,   7,   2,   2,   8, 105,   5,
         2, 182, 314,  38,  98, 103,   7,  36,   2, 246, 360,   7,  19,
       396,  17,  26, 269, 929,  18,   2, 493,   6, 116,   7, 105,   5,
       575, 182,  27,   5,   2,   2, 130,  62,  17,  24,  89,  17,  13,
       381,   2,   8,   2,   7,   5,   2,  38, 325,   7,  17,  23,  93,
         9, 156, 252,  19, 235,  20,  28,   5, 104,  76,   7,  17, 169,
        35,   2,  17,  23,   2,   7,  36,   2, 934,  56,   2,   6,  17,
       891, 214,  11,   5,   2,   6,  92,   6,  33, 256,  82,   7], dtype=int32)

nb_words로 배열의 최대 길이를 지정하지 않으면 Embedding 단계에서 out of index 에러가 난다.
IndexError: index 4414 is out of bounds for size 1000

학습 실패 

Using Theano backend.
Epoch 1/10
22500/22500 [==============================] - 115s - loss: 0.6932 - acc: 0.5014     
Epoch 2/10
22500/22500 [==============================] - 115s - loss: 0.6932 - acc: 0.5010     
Epoch 3/10
22500/22500 [==============================] - 114s - loss: 0.6932 - acc: 0.5014     
Epoch 4/10
22500/22500 [==============================] - 115s - loss: 0.6931 - acc: 0.5014     
Epoch 5/10
22500/22500 [==============================] - 115s - loss: 0.6931 - acc: 0.5014     
Epoch 6/10
22500/22500 [==============================] - 115s - loss: 0.6932 - acc: 0.5014     
Epoch 7/10
22500/22500 [==============================] - 114s - loss: 0.6931 - acc: 0.5014     
Epoch 8/10
22500/22500 [==============================] - 114s - loss: 0.6932 - acc: 0.5016     
Epoch 9/10
22500/22500 [==============================] - 115s - loss: 0.6932 - acc: 0.5014     
Epoch 10/10
22500/22500 [==============================] - 115s - loss: 0.6932 - acc: 0.5014

학습 성공 

Epoch 1/10
22500/22500 [==============================] - 14282s - loss: 0.6927 - acc: 0.5164     
Epoch 2/10
22500/22500 [==============================] - 10235s - loss: 0.6864 - acc: 0.5618     
Epoch 3/10
22500/22500 [==============================] - 3236s - loss: 0.6541 - acc: 0.6508     
Epoch 4/10
22500/22500 [==============================] - 3230s - loss: 0.5829 - acc: 0.7528     
Epoch 5/10
22500/22500 [==============================] - 3222s - loss: 0.5490 - acc: 0.7745     
Epoch 6/10
22500/22500 [==============================] - 3229s - loss: 0.5250 - acc: 0.7946     
Epoch 7/10
22500/22500 [==============================] - 3230s - loss: 0.5052 - acc: 0.8030     
Epoch 8/10
22300/22500 [============================>.] - ETA: 28s - loss: 0.4963 - acc: 0.8046
(사실 다음날 보니 프로세스가 죽어있어서 Epoch 8/10 이후의 결과는 없음... 학습 실패임)

다음 시간에는

  • Coursera 동영상 week 7 보기

더 보기

Retrieved from http://wiki.zeropage.org/wiki.php/머신러닝스터디/2016/2016_07_09
last modified 2021-02-07 05:29:27