{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sentiment analysis on forum articles using word2vec and Keras\n", "\n", "참고: http://ahmedbesbes.com/sentiment-analysis-on-twitter-using-word2vec-and-keras.html\n", "\n", "원문과 달리 감정 분석이 아닌 정치글 성향을 분석하는 분류기를 만든다." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import sys\n", "sys.path.append('/Users/kaonpark/workspace/github.com/likejazz/kaon-learn')\n", "import kaonlearn\n", "from kaonlearn.plots import plot_decision_regions, plot_history" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "pd.options.mode.chained_assignment = None\n", "from copy import deepcopy\n", "from string import punctuation\n", "from random import shuffle\n", "\n", "import gensim\n", "from gensim.models.word2vec import Word2Vec # the word2vec model gensim class\n", "LabeledSentence = gensim.models.doc2vec.LabeledSentence # we'll talk about this down below\n", "\n", "from keras_tqdm import TQDMNotebookCallback\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.feature_extraction.text import TfidfVectorizer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "데이타는 커뮤니티 게시글의 형태소 분석 결과 중 명사를 정리한 결과로 한다. \n", "커뮤니티 게시글인 관계로 비문이 많이 섞여 있다.\n", "\n", "참고: http://docs.likejazz.com/cnn-text-classification-tf/" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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SentimentTextSentiment
00 1 공통점0
10 국어 구사 아이돌0
204 도깨비 은탁 엔딩 사랑 물리학0
31 1만 키로 정도0
41 1박2일 2 정준영 등장 눈물 김종민0
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" ], "text/plain": [ " SentimentText Sentiment\n", "0 0 1 공통점 0\n", "1 0 국어 구사 아이돌 0\n", "2 04 도깨비 은탁 엔딩 사랑 물리학 0\n", "3 1 1만 키로 정도 0\n", "4 1 1박2일 2 정준영 등장 눈물 김종민 0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv('data/news-title-class.csv')\n", "\n", "data.head(5)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SentimentTextSentimenttokens
00 1 공통점0[0, 1, 공통점]
10 국어 구사 아이돌0[0, 국어, 구사, 아이돌]
204 도깨비 은탁 엔딩 사랑 물리학0[04, 도깨비, 은탁, 엔딩, 사랑, 물리학]
31 1만 키로 정도0[1, 1만, 키로, 정도]
41 1박2일 2 정준영 등장 눈물 김종민0[1, 1박2일, 2, 정준영, 등장, 눈물, 김종민]
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" ], "text/plain": [ " SentimentText Sentiment tokens\n", "0 0 1 공통점 0 [0, 1, 공통점]\n", "1 0 국어 구사 아이돌 0 [0, 국어, 구사, 아이돌]\n", "2 04 도깨비 은탁 엔딩 사랑 물리학 0 [04, 도깨비, 은탁, 엔딩, 사랑, 물리학]\n", "3 1 1만 키로 정도 0 [1, 1만, 키로, 정도]\n", "4 1 1박2일 2 정준영 등장 눈물 김종민 0 [1, 1박2일, 2, 정준영, 등장, 눈물, 김종민]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import nltk\n", "# data['tokens'] = nltk.word_tokenize(data['SentimentText'])\n", "data['tokens'] = ''\n", "i = 0\n", "for text in data['SentimentText']:\n", " data['tokens'][i] = nltk.word_tokenize(text)\n", " i += 1\n", "\n", "data.head(5)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((5285,), (1322,))" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_train, x_test, y_train, y_test = train_test_split(np.array(data.tokens),\n", " np.array(data.Sentiment), test_size=0.2)\n", "x_train.shape, y_test.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def labelizeTweets(tweets, label_type):\n", " labelized = []\n", " for i, v in enumerate(tweets):\n", " label = '%s_%s' % (label_type, i)\n", " labelized.append(LabeledSentence(v, [label]))\n", " return labelized\n", "\n", "x_train = labelizeTweets(x_train, 'TRAIN')\n", "x_test = labelizeTweets(x_test, 'TEST')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LabeledSentence(words=['콧구멍', '하악하악'], tags=['TRAIN_0'])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_train[0]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[LabeledSentence(words=['콧구멍', '하악하악'], tags=['TRAIN_0']),\n", " LabeledSentence(words=['윤종신', '실제', '느낌'], tags=['TRAIN_1']),\n", " LabeledSentence(words=['게시판'], tags=['TRAIN_2']),\n", " LabeledSentence(words=['후방'], tags=['TRAIN_3']),\n", " LabeledSentence(words=['주갤', '제보'], tags=['TRAIN_4']),\n", " LabeledSentence(words=['도깨비', '가장', '장면'], tags=['TRAIN_5']),\n", " LabeledSentence(words=['이정현', '새누리당', '대표', '장류', '셋트', '구매', '전송', '완료'], tags=['TRAIN_6']),\n", " LabeledSentence(words=['박근혜', '부역자', '황영철'], tags=['TRAIN_7']),\n", " LabeledSentence(words=['프로토', '하루', '따자'], tags=['TRAIN_8']),\n", " LabeledSentence(words=['메이커', '별', '품번', '특징', '정리', '1'], tags=['TRAIN_9'])]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_train[:10]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['콧구멍', '하악하악'],\n", " ['윤종신', '실제', '느낌'],\n", " ['게시판'],\n", " ['후방'],\n", " ['주갤', '제보'],\n", " ['도깨비', '가장', '장면'],\n", " ['이정현', '새누리당', '대표', '장류', '셋트', '구매', '전송', '완료'],\n", " ['박근혜', '부역자', '황영철'],\n", " ['프로토', '하루', '따자'],\n", " ['메이커', '별', '품번', '특징', '정리', '1']]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[x.words for x in x_train][:10]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "75624" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# `size` is the dimensionality of the feature vectors.\n", "# `window` is the maximum distance between the current and predicted word within a sentence.\n", "# `min_count` = ignore all words with total frequency lower than this.\n", "tweet_w2v = Word2Vec(size=200, window=3, min_count=2)\n", "tweet_w2v.build_vocab([x.words for x in x_train])\n", "tweet_w2v.train([x.words for x in x_train], total_examples=tweet_w2v.corpus_count, epochs=5)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(5285, 2612)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweet_w2v.corpus_count, len(tweet_w2v.wv.vocab)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('탄핵', 0.45850053429603577),\n", " ('때', 0.3880687355995178),\n", " ('일본', 0.38755983114242554),\n", " ('사람', 0.36928194761276245),\n", " ('의원', 0.3663651645183563),\n", " ('대통령', 0.3569279909133911),\n", " ('게임', 0.35518980026245117),\n", " ('전', 0.35461631417274475),\n", " ('고영태', 0.34320682287216187),\n", " ('우병우', 0.33934956789016724)]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweet_w2v.most_similar('박근혜')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('의원', 0.33990582823753357),\n", " ('시장', 0.3274620771408081),\n", " ('우병우', 0.3198774755001068),\n", " ('탄핵', 0.31063830852508545),\n", " ('이유', 0.30737459659576416),\n", " ('인가', 0.2894013524055481),\n", " ('가결', 0.28872376680374146),\n", " ('게임', 0.28291991353034973),\n", " ('황교안', 0.28228309750556946),\n", " ('박근혜', 0.28173351287841797)]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweet_w2v.most_similar('문재인')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('소리야', 0.24287334084510803),\n", " ('온라인', 0.2331192046403885),\n", " ('조심', 0.2304215282201767),\n", " ('결론', 0.2289251685142517),\n", " ('자존심', 0.22776442766189575),\n", " ('당시', 0.21566548943519592),\n", " ('급', 0.21559274196624756),\n", " ('단일화', 0.21372069418430328),\n", " ('새누리당', 0.2122083604335785),\n", " ('41', 0.21167846024036407)]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweet_w2v.most_similar('김제동')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['실제', '느낌', '게시판', '후방', '주갤', '제보', '도깨비', '가장', '장면', '이정현']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w = tweet_w2v.wv.vocab.keys()\n", "# word_vectors = [tweet_w2v[w] for w in tweet_w2v.wv.vocab.keys()[:10]]\n", "list(w)[:10]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[t-SNE] Computing pairwise distances...\n", "[t-SNE] Computing 91 nearest neighbors...\n", "[t-SNE] Computed conditional probabilities for sample 100 / 100\n", "[t-SNE] Mean sigma: 0.006469\n", "[t-SNE] KL divergence after 100 iterations with early exaggeration: 1.759106\n", "[t-SNE] Error after 400 iterations: 1.759106\n" ] }, { "data": { "image/png": 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YxI8zvmLFZ59jMpp86iPLMn5+flUS+txuN9u3f8/SpSu9jxQqKsp5+OGBPPTQ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PTgypUrhDZugde9D2PLycQtsBrFmelEf/4+z4ydwnVVRVX/PJeyUqlgRXbOjoWFhbFgwRwEwfmb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\nJQpVLSsWlWUHdruD5557ni5d7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# getting a list of word vectors. limit to 100. each is of 100 dimensions\n", "word_vectors = [tweet_w2v[w] for w in tweet_w2v.wv.vocab.keys()]\n", "word_vectors = list(word_vectors)[:100]\n", "\n", "# dimensionality reduction. converting the vectors to 2d vectors\n", "from sklearn.manifold import TSNE\n", "tsne_model = TSNE(n_components=2, verbose=1, random_state=0)\n", "tsne_w2v = tsne_model.fit_transform(word_vectors)\n", "\n", "# putting everything in a dataframe\n", "tsne_df = pd.DataFrame(tsne_w2v, columns=['x', 'y'])\n", "tsne_df['words'] = list(tweet_w2v.wv.vocab.keys())[:100]\n", "\n", "import matplotlib\n", "matplotlib.rc('font', family='AppleGothic')\n", "\n", "i = 0\n", "for i in range(tsne_df['words'].size):\n", " plt.scatter(tsne_df['x'][i], tsne_df['y'][i])\n", " plt.annotate(tsne_df['words'][i],\n", " xy=(tsne_df['x'][i],tsne_df['y'][i]))\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "t-SNE의 값은 PCA와 달리 지나치게 랜덤하게 변해서 올바른 현상인지 확인이 필요하다." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "building tf-idf matrix ...\n" ] }, { "data": { "text/plain": [ "(5285, 2587)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print ('building tf-idf matrix ...')\n", "vectorizer = TfidfVectorizer(analyzer=lambda x: x, min_df=2)\n", "matrix = vectorizer.fit_transform([x.words for x in x_train])\n", "matrix.shape" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vocab size : 2587\n" ] } ], "source": [ "tfidf = dict(zip(vectorizer.get_feature_names(), vectorizer.idf_))\n", "print ('vocab size :', len(tfidf))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.1539764873676361" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tfidf['문재인']" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.5892104734558981" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tfidf['박근혜']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "tfidf를 워드 벡터에 반영한다." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def buildWordVector(tokens, size):\n", " vec = np.zeros(size).reshape((1, size))\n", " count = 0.\n", " for word in tokens:\n", " try:\n", " vec += tweet_w2v[word].reshape((1, size)) * tfidf[word]\n", " count += 1.\n", " except KeyError: # handling the case where the token is not\n", " # in the corpus. useful for testing.\n", " continue\n", " if count != 0:\n", " vec /= count\n", " return vec" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(5285, 200)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import scale\n", "train_vecs_w2v = np.concatenate([buildWordVector(z, 200) for z in map(lambda x: x.words, x_train)])\n", "train_vecs_w2v = scale(train_vecs_w2v)\n", "\n", "test_vecs_w2v = np.concatenate([buildWordVector(z, 200) for z in map(lambda x: x.words, x_test)])\n", "test_vecs_w2v = scale(test_vecs_w2v)\n", "\n", "train_vecs_w2v.shape" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1.5 , 3.63333333],\n", " [ 2.1 , 6.3 ]])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_v = {}\n", "test_tfidf = {}\n", "\n", "test_v['A'] = np.array([0.1, 0.4])\n", "test_v['B'] = np.array([0.2, 0.3])\n", "test_v['C'] = np.array([0.3, 0.9])\n", "\n", "test_tfidf['A'] = 4\n", "test_tfidf['B'] = 10\n", "test_tfidf['C'] = 7\n", "\n", "def testBuildWordVector(tokens, size):\n", " vec = np.zeros(size).reshape(1,size)\n", " count = 0.\n", " for word in tokens:\n", " # token에 해당하는 벡터에 tfidf 가중치 부여하여 합산\n", " vec += test_v[word].reshape(1,size) * test_tfidf[word]\n", " count += 1.\n", "\n", " # token 수 만큼 나누어 1 token 기준 벡터 값 추출\n", " vec /= count\n", " \n", " return vec\n", " # vec\n", "\n", "# 각 벡터를 하나의 numpy array로 join\n", "test_v = np.concatenate([testBuildWordVector(z, 2) for z in [['A', 'B', 'C'], ['C']]])\n", "test_v" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Just regular densely-connected NN layer" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "dense_1 (Dense) (None, 32) 6432 \n", "_________________________________________________________________\n", "dense_2 (Dense) (None, 1) 33 \n", "=================================================================\n", "Total params: 6,465\n", "Trainable params: 6,465\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "from keras.models import Sequential\n", "from keras.layers.core import Dense, Dropout, Activation\n", "\n", "model = Sequential()\n", "model.add(Dense(32, activation='relu', input_dim=200))\n", "model.add(Dense(1, activation='sigmoid'))\n", "model.compile(optimizer='rmsprop',\n", " loss='binary_crossentropy',\n", " metrics=['accuracy'])\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b15f2a67f221422db7cac5978aeda180" } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "history = model.fit(train_vecs_w2v, y_train, epochs=120, batch_size=32, verbose=0,\n", " validation_split=0.2,\n", " callbacks=[TQDMNotebookCallback(show_inner=False)])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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hTheS6IcIRVEoqmolMlSPNcI42OEIIYQ4TUiiHyLqWzpoc3kYkxwpk/CEEEKc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\n", 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lMzW1NmM1Wtp61d8Xr6TF7eTclDMxGU0EG4O5atilzMt+jSdXPU+MvR8hlmBM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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_history(history)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.48672030119960857, 0.90620272332709428]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "score = model.evaluate(test_vecs_w2v, y_test, batch_size=128, verbose=2)\n", "score # score[1] is accuracy" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(5285, 200)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_vecs_w2v.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# embedding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- pad_sequences for embedding\n", "- word2vec\n", " - word2vec embedding\n", " - reshape model for CNN" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# pad_sequences for embedding\n", "2차원의 값이 `(5250,)`등으로 명확히 정의되어 있지 않을때는 `pad_sequence`로 `maxlen` 만큼의 고정 길이를 할당해야 임베딩 적용이 가능하다.\n", "\n", "우리는 이미 고정 길이로 할당된 상태이기 때문에 의미가 없다." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(5285, 200)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_vecs_w2v.shape" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pad sequences (samples x time)\n" ] } ], "source": [ "from keras.preprocessing import sequence\n", "print(\"Pad sequences (samples x time)\")\n", "train_vecs_w2v = sequence.pad_sequences(train_vecs_w2v, maxlen=200)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(5285, 200)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_vecs_w2v.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "수치 벡터로 표현한 문장의 임베딩은 아래와 같이 진행한다. (one-hot encoding 아님)\n", "\n", "* `input_dim`: 중복을 제외한 전체 단어 수\n", "* `output_dim`: 임베딩 할 차원의 수\n", "* `input_length`: 가장 긴 문장의 단어 수\n", "\n", "```\n", "model = Sequential()\n", "model.add(Embedding(input_dim=25000, output_dim=5, \n", " input_length=40))\n", "model.add(SpatialDropout1D(0.2))\n", "```\n", "\n", "Output Shape는 `(None, 40, 5)`이 된다.\n", "\n", "추후 CNN등에서 filter size를 5로 설정하면 임베딩 차원을 그대로 유지하며 진행한다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# word2vec\n", "## word2vec embedding\n", "\n", "```\n", "vocab_sz = 5285\n", "EMBED_SIZE = 200\n", "\n", "embedding_weights = np.zeros((vocab_sz, EMBED_SIZE))\n", "embedding_weights[0,:] = [1,2,3 ... 200]\n", "\n", "model.add(Embedding(vocab_sz, EMBED_SIZE, input_length=maxlen,\n", " weights=[embedding_weights]))\n", "```\n", "이 경우 `fit()` 실행시 문장의 벡터를 입력하고 word2vec은 weights로 삽입한다. weights는 각 단어별 word2vec 값이다.\n", "\n", "여기서는 임베딩 차원의 수가 word2vec 임베딩과 동일하다. 나중에 CNN의 filters(필터의 갯수)도 이 값으로 처리된다. 즉, 200을 유지하려면 filters=200으로 진행해야 한다.\n", "\n", "우리는 이미 상기에서 각 단어별 word2vec 값에 tfidf 가중치를 부여하고 문장을 토큰 단위 평균을 낸 상태이기 때문에 이 과정이 의미가 없다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## reshape model for CNN" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(5285, 200)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_vecs_w2v.shape" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((5285, 200, 1), (5285,))" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# CNN filters와 값을 맞추기 위해 5로 설정해봤으나 `cannot reshape array` 오류 발생. 1로 진행한다.\n", "train_vecs_w2v = train_vecs_w2v.reshape(train_vecs_w2v.shape[0], train_vecs_w2v.shape[1], 1)\n", "train_vecs_w2v.shape, y_train.shape" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1322, 200, 1)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_vecs_w2v = test_vecs_w2v.reshape(test_vecs_w2v.shape[0], test_vecs_w2v.shape[1], 1)\n", "test_vecs_w2v.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1D convolution layer" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "conv1d_1 (Conv1D) (None, 198, 5) 20 \n", "_________________________________________________________________\n", "max_pooling1d_1 (MaxPooling1 (None, 66, 5) 0 \n", "_________________________________________________________________\n", "conv1d_2 (Conv1D) (None, 63, 5) 105 \n", "_________________________________________________________________\n", "max_pooling1d_2 (MaxPooling1 (None, 21, 5) 0 \n", "_________________________________________________________________\n", "conv1d_3 (Conv1D) (None, 17, 5) 130 \n", "_________________________________________________________________\n", "max_pooling1d_3 (MaxPooling1 (None, 5, 5) 0 \n", "_________________________________________________________________\n", "flatten_1 (Flatten) (None, 25) 0 \n", "_________________________________________________________________\n", "dense_3 (Dense) (None, 10) 260 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 10) 0 \n", "_________________________________________________________________\n", "activation_1 (Activation) (None, 10) 0 \n", "_________________________________________________________________\n", "dense_4 (Dense) (None, 1) 11 \n", "_________________________________________________________________\n", "activation_2 (Activation) (None, 1) 0 \n", "=================================================================\n", "Total params: 526\n", "Trainable params: 526\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "from keras.layers.embeddings import Embedding\n", "from keras.layers import Dense, Activation, Flatten, Dropout, Embedding, LSTM, Bidirectional\n", "from keras.layers.convolutional import Conv1D, Conv2D\n", "from keras.layers.pooling import MaxPooling1D, GlobalMaxPooling1D\n", "from keras.layers.normalization import BatchNormalization\n", "from keras.layers.core import SpatialDropout1D\n", "from keras.models import Sequential\n", "\n", "model = Sequential()\n", "\n", "# we add a Convolution1D, which will learn filters\n", "# word group filters of size filter_length:\n", "model.add(Conv1D(filters=5, kernel_size=3, activation='relu', input_shape=[200,1]))\n", "model.add(MaxPooling1D(3))\n", "\n", "model.add(Conv1D(filters=5, kernel_size=4, activation='relu'))\n", "model.add(MaxPooling1D(3))\n", "\n", "model.add(Conv1D(filters=5, kernel_size=5, activation='relu'))\n", "model.add(MaxPooling1D(3))\n", "\n", "# GlobalMaxPooling1D는 필터 수 만큼 남긴다.\n", "# model.add(GlobalMaxPooling1D())\n", "# Flatten은 2차원x3차원(필터 수)를 합한 만큼 2차원에 표현(추가됨)한다.\n", "model.add(Flatten())\n", "\n", "# We add a vanilla hidden layer:\n", "model.add(Dense(10))\n", "model.add(Dropout(0.2))\n", "model.add(Activation('relu'))\n", "\n", "# We project onto a single unit output layer, and squash it with a sigmoid:\n", "model.add(Dense(1))\n", "model.add(Activation('sigmoid'))\n", "\n", "model.compile(loss='binary_crossentropy',\n", " optimizer='adam',\n", " metrics=['accuracy'])\n", "\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "64fb5b11277f4348b7f1306ff3ddbb6c" } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "history = model.fit(train_vecs_w2v, y_train,\n", " batch_size=32,\n", " epochs=200,\n", " verbose=0,\n", " validation_split=0.2,\n", " callbacks=[TQDMNotebookCallback(show_inner=False)])" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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5mFc5zFpuF1vrshi9OxCTSvq8wXQMfzCLvsyqh1ajRFd/GHvafVAg7d1Qq5Ro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/uOTW9pOMxH5iU60U/iRDu14CwgZgtzSKsZrKmT218I9VrsCJGGxTBEVvB63TdQ9EQlV+4tXdBN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\n8uXLxdmzZ4t/+ctf5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0tLTQ3Nzsl/Nv3ryZiy66CIC0tDRSUlLYu3evX87dUzqZJ0AIIUSQCmhLQHl5Oenp6V22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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_history(history)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.64774580036096963, 0.69591527942810405]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "score = model.evaluate(test_vecs_w2v, y_test, batch_size=128, verbose=2)\n", "score # score[1] is accuracy" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['loss', 'acc']" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.metrics_names" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }