數據簡介: Disasters on social media

美國政客的社交媒體消息分類
內容：收集了來自美國參議員和其他美國政客的數千條社交媒體消息，可按內容分類為目標群眾（國家或選民）、政治主張（中立/兩黨或偏見/黨派）和實際內容（如攻擊政敵等）

社交媒體上有些討論是關于災難，疾病，暴亂的，有些只是開玩笑或者是電影情節，我們該如何讓機器能分辨出這兩種討論呢？

import keras import nltk import pandas as pd import numpy as np import re import codecs questions = pd.read_csv("socialmedia_relevant_cols_clean.csv") questions.columns=['text', 'choose_one', 'class_label'] questions.head() textchoose_oneclass_label01234

Just happened a terrible car crash	Relevant	1
Our Deeds are the Reason of this #earthquake M...	Relevant	1
Heard about #earthquake is different cities, s...	Relevant	1
there is a forest fire at spot pond, geese are...	Relevant	1
Forest fire near La Ronge Sask. Canada	Relevant	1

questions.describe() class_labelcountmeanstdmin25%50%75%max

10876.000000
0.432604
0.498420
0.000000
0.000000
0.000000
1.000000
2.000000

數據清洗，去掉無用字符

def standardize_text(df, text_field):df[text_field] = df[text_field].str.replace(r"http\S+", "")df[text_field] = df[text_field].str.replace(r"http", "")df[text_field] = df[text_field].str.replace(r"@\S+", "")df[text_field] = df[text_field].str.replace(r"[^A-Za-z0-9(),!?@\'\`\"\_\n]", " ")df[text_field] = df[text_field].str.replace(r"@", "at")df[text_field] = df[text_field].str.lower()return dfquestions = standardize_text(questions, "text")questions.to_csv("clean_data.csv") questions.head() textchoose_oneclass_label01234

just happened a terrible car crash	Relevant	1
our deeds are the reason of this earthquake m...	Relevant	1
heard about earthquake is different cities, s...	Relevant	1
there is a forest fire at spot pond, geese are...	Relevant	1
forest fire near la ronge sask canada	Relevant	1

clean_questions = pd.read_csv("clean_data.csv") clean_questions.tail() Unnamed: 0textchoose_oneclass_label1087110872108731087410875

10871	m1 94 01 04 utc ?5km s of volcano hawaii	Relevant	1
10872	police investigating after an e bike collided ...	Relevant	1
10873	the latest more homes razed by northern calif...	Relevant	1
10874	meg issues hazardous weather outlook (hwo)	Relevant	1
10875	cityofcalgary has activated its municipal eme...	Relevant	1

數據分布情況

數據是否傾斜

clean_questions.groupby("class_label").count() Unnamed: 0textchoose_oneclass_label012

6187	6187	6187
4673	4673	4673
16	16	16

看起來還算均衡的

處理流程

• 分詞
• 訓練與測試集
• 檢查與驗證

from nltk.tokenize import RegexpTokenizertokenizer = RegexpTokenizer(r'\w+')clean_questions["tokens"] = clean_questions["text"].apply(tokenizer.tokenize) clean_questions.head() Unnamed: 0textchoose_oneclass_labeltokens01234

0	just happened a terrible car crash	Relevant	1	[just, happened, a, terrible, car, crash]
1	our deeds are the reason of this earthquake m...	Relevant	1	[our, deeds, are, the, reason, of, this, earth...
2	heard about earthquake is different cities, s...	Relevant	1	[heard, about, earthquake, is, different, citi...
3	there is a forest fire at spot pond, geese are...	Relevant	1	[there, is, a, forest, fire, at, spot, pond, g...
4	forest fire near la ronge sask canada	Relevant	1	[forest, fire, near, la, ronge, sask, canada]

語料庫情況

from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categoricalall_words = [word for tokens in clean_questions["tokens"] for word in tokens] sentence_lengths = [len(tokens) for tokens in clean_questions["tokens"]] VOCAB = sorted(list(set(all_words))) print("%s words total, with a vocabulary size of %s" % (len(all_words), len(VOCAB))) print("Max sentence length is %s" % max(sentence_lengths)) 154724 words total, with a vocabulary size of 18101 Max sentence length is 34

句子長度情況

import matplotlib.pyplot as pltfig = plt.figure(figsize=(10, 10)) plt.xlabel('Sentence length') plt.ylabel('Number of sentences') plt.hist(sentence_lengths) plt.show()

特征如何構建？

Bag of Words Counts

from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizerdef cv(data):count_vectorizer = CountVectorizer()emb = count_vectorizer.fit_transform(data)return emb, count_vectorizerlist_corpus = clean_questions["text"].tolist() list_labels = clean_questions["class_label"].tolist()X_train, X_test, y_train, y_test = train_test_split(list_corpus, list_labels, test_size=0.2, random_state=40)X_train_counts, count_vectorizer = cv(X_train) X_test_counts = count_vectorizer.transform(X_test)

PCA展示Bag of Words

from sklearn.decomposition import PCA, TruncatedSVD import matplotlib import matplotlib.patches as mpatchesdef plot_LSA(test_data, test_labels, savepath="PCA_demo.csv", plot=True):lsa = TruncatedSVD(n_components=2)lsa.fit(test_data)lsa_scores = lsa.transform(test_data)color_mapper = {label:idx for idx,label in enumerate(set(test_labels))}color_column = [color_mapper[label] for label in test_labels]colors = ['orange','blue','blue']if plot:plt.scatter(lsa_scores[:,0], lsa_scores[:,1], s=8, alpha=.8, c=test_labels, cmap=matplotlib.colors.ListedColormap(colors))red_patch = mpatches.Patch(color='orange', label='Irrelevant')green_patch = mpatches.Patch(color='blue', label='Disaster')plt.legend(handles=[red_patch, green_patch], prop={'size': 30})fig = plt.figure(figsize=(16, 16)) plot_LSA(X_train_counts, y_train) plt.show()

看起來并沒有將這兩類點區分開

邏輯回歸看一下結果

from sklearn.linear_model import LogisticRegressionclf = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg', multi_class='multinomial', n_jobs=-1, random_state=40) clf.fit(X_train_counts, y_train)y_predicted_counts = clf.predict(X_test_counts)

評估

from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_reportdef get_metrics(y_test, y_predicted): # true positives / (true positives+false positives)precision = precision_score(y_test, y_predicted, pos_label=None,average='weighted') # true positives / (true positives + false negatives)recall = recall_score(y_test, y_predicted, pos_label=None,average='weighted')# harmonic mean of precision and recallf1 = f1_score(y_test, y_predicted, pos_label=None, average='weighted')# true positives + true negatives/ totalaccuracy = accuracy_score(y_test, y_predicted)return accuracy, precision, recall, f1accuracy, precision, recall, f1 = get_metrics(y_test, y_predicted_counts) print("accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f" % (accuracy, precision, recall, f1)) accuracy = 0.754, precision = 0.752, recall = 0.754, f1 = 0.753

混淆矩陣檢查

import numpy as np import itertools from sklearn.metrics import confusion_matrixdef plot_confusion_matrix(cm, classes,normalize=False,title='Confusion matrix',cmap=plt.cm.winter):if normalize:cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]plt.imshow(cm, interpolation='nearest', cmap=cmap)plt.title(title, fontsize=30)plt.colorbar()tick_marks = np.arange(len(classes))plt.xticks(tick_marks, classes, fontsize=20)plt.yticks(tick_marks, classes, fontsize=20)fmt = '.2f' if normalize else 'd'thresh = cm.max() / 2.for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] < thresh else "black", fontsize=40)plt.tight_layout()plt.ylabel('True label', fontsize=30)plt.xlabel('Predicted label', fontsize=30)return plt cm = confusion_matrix(y_test, y_predicted_counts) fig = plt.figure(figsize=(10, 10)) plot = plot_confusion_matrix(cm, classes=['Irrelevant','Disaster','Unsure'], normalize=False, title='Confusion matrix') plt.show() print(cm)

[[970 251 3][274 670 1][ 3 4 0]]

第三類咋沒有一個呢。。。因為數據里面就沒幾個啊。。。

進一步檢查模型的關注點

def get_most_important_features(vectorizer, model, n=5):index_to_word = {v:k for k,v in vectorizer.vocabulary_.items()}# loop for each classclasses ={}for class_index in range(model.coef_.shape[0]):word_importances = [(el, index_to_word[i]) for i,el in enumerate(model.coef_[class_index])]sorted_coeff = sorted(word_importances, key = lambda x : x[0], reverse=True)tops = sorted(sorted_coeff[:n], key = lambda x : x[0])bottom = sorted_coeff[-n:]classes[class_index] = {'tops':tops,'bottom':bottom}return classesimportance = get_most_important_features(count_vectorizer, clf, 10) def plot_important_words(top_scores, top_words, bottom_scores, bottom_words, name):y_pos = np.arange(len(top_words))top_pairs = [(a,b) for a,b in zip(top_words, top_scores)]top_pairs = sorted(top_pairs, key=lambda x: x[1])bottom_pairs = [(a,b) for a,b in zip(bottom_words, bottom_scores)]bottom_pairs = sorted(bottom_pairs, key=lambda x: x[1], reverse=True)top_words = [a[0] for a in top_pairs]top_scores = [a[1] for a in top_pairs]bottom_words = [a[0] for a in bottom_pairs]bottom_scores = [a[1] for a in bottom_pairs]fig = plt.figure(figsize=(10, 10)) plt.subplot(121)plt.barh(y_pos,bottom_scores, align='center', alpha=0.5)plt.title('Irrelevant', fontsize=20)plt.yticks(y_pos, bottom_words, fontsize=14)plt.suptitle('Key words', fontsize=16)plt.xlabel('Importance', fontsize=20)plt.subplot(122)plt.barh(y_pos,top_scores, align='center', alpha=0.5)plt.title('Disaster', fontsize=20)plt.yticks(y_pos, top_words, fontsize=14)plt.suptitle(name, fontsize=16)plt.xlabel('Importance', fontsize=20)plt.subplots_adjust(wspace=0.8)plt.show()top_scores = [a[0] for a in importance[1]['tops']] top_words = [a[1] for a in importance[1]['tops']] bottom_scores = [a[0] for a in importance[1]['bottom']] bottom_words = [a[1] for a in importance[1]['bottom']]plot_important_words(top_scores, top_words, bottom_scores, bottom_words, "Most important words for relevance")

我們的模型找到了一些模式，但是看起來還不夠好

TFIDF Bag of Words

這樣我們就不均等對待每一個詞了

def tfidf(data):tfidf_vectorizer = TfidfVectorizer()train = tfidf_vectorizer.fit_transform(data)return train, tfidf_vectorizerX_train_tfidf, tfidf_vectorizer = tfidf(X_train) X_test_tfidf = tfidf_vectorizer.transform(X_test) fig = plt.figure(figsize=(16, 16)) plot_LSA(X_train_tfidf, y_train) plt.show()

看起來好那么一丁丁丁丁點

clf_tfidf = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg', multi_class='multinomial', n_jobs=-1, random_state=40) clf_tfidf.fit(X_train_tfidf, y_train)y_predicted_tfidf = clf_tfidf.predict(X_test_tfidf) accuracy_tfidf, precision_tfidf, recall_tfidf, f1_tfidf = get_metrics(y_test, y_predicted_tfidf) print("accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f" % (accuracy_tfidf, precision_tfidf, recall_tfidf, f1_tfidf)) accuracy = 0.762, precision = 0.760, recall = 0.762, f1 = 0.761 cm2 = confusion_matrix(y_test, y_predicted_tfidf) fig = plt.figure(figsize=(10, 10)) plot = plot_confusion_matrix(cm2, classes=['Irrelevant','Disaster','Unsure'], normalize=False, title='Confusion matrix') plt.show() print("TFIDF confusion matrix") print(cm2) print("BoW confusion matrix") print(cm)

TFIDF confusion matrix [[974 249 1][261 684 0][ 3 4 0]] BoW confusion matrix [[970 251 3][274 670 1][ 3 4 0]]

詞語的解釋

importance_tfidf = get_most_important_features(tfidf_vectorizer, clf_tfidf, 10) top_scores = [a[0] for a in importance_tfidf[1]['tops']] top_words = [a[1] for a in importance_tfidf[1]['tops']] bottom_scores = [a[0] for a in importance_tfidf[1]['bottom']] bottom_words = [a[1] for a in importance_tfidf[1]['bottom']]plot_important_words(top_scores, top_words, bottom_scores, bottom_words, "Most important words for relevance")

這些詞看起來比之前強一些了

問題

我們現在考慮的是每一個詞基于頻率的情況，如果在新的測試環境下有些詞變了呢？比如說goog和positive.有些詞可能表達的意義差不多但是卻長得不一樣，這樣我們的模型就難捕捉到了。

word2vec

一句話解釋：比較牛逼。。。

import gensimword2vec_path = "GoogleNews-vectors-negative300.bin" word2vec = gensim.models.KeyedVectors.load_word2vec_format(word2vec_path, binary=True) def get_average_word2vec(tokens_list, vector, generate_missing=False, k=300):if len(tokens_list)<1:return np.zeros(k)if generate_missing:vectorized = [vector[word] if word in vector else np.random.rand(k) for word in tokens_list]else:vectorized = [vector[word] if word in vector else np.zeros(k) for word in tokens_list]length = len(vectorized)summed = np.sum(vectorized, axis=0)averaged = np.divide(summed, length)return averageddef get_word2vec_embeddings(vectors, clean_questions, generate_missing=False):embeddings = clean_questions['tokens'].apply(lambda x: get_average_word2vec(x, vectors, generate_missing=generate_missing))return list(embeddings) embeddings = get_word2vec_embeddings(word2vec, clean_questions) X_train_word2vec, X_test_word2vec, y_train_word2vec, y_test_word2vec = train_test_split(embeddings, list_labels, test_size=0.2, random_state=40) X_train_word2vec[0] array([ 0.05639939, 0.02053833, 0.07635207, 0.06914993, -0.01007262,-0.04978943, 0.02546038, -0.06045968, 0.04264323, 0.02419935,0.00375076, -0.15124639, 0.02915809, -0.01554943, -0.10182699,0.05523972, 0.00953747, 0.0834525 , 0.00200544, -0.0238909 ,-0.01706369, 0.09193638, 0.03979783, 0.04899052, 0.04707618,-0.09235491, -0.10698809, 0.07503255, 0.04905628, -0.01991781,0.04036749, -0.0117856 , -0.00576346, 0.01624843, -0.01823952,-0.01545715, 0.06020392, 0.02975609, 0.02211217, 0.07844525,0.05023847, -0.09430913, 0.20582217, -0.05274091, 0.00881231,0.04394059, -0.01748512, -0.0403268 , 0.03178769, 0.06038993,0.03867458, 0.00492932, 0.05121649, 0.01256743, -0.02096994,0.02814593, -0.06389218, 0.01661319, -0.02686709, -0.07981364,-0.00288318, 0.07032367, -0.07524182, -0.01155599, -0.0259661 ,0.00625901, -0.05474758, -0.00059877, -0.01737177, 0.07586161,0.0273136 , -0.00077093, 0.0752638 , 0.05861119, -0.15668742,-0.00779506, 0.04997617, 0.08768209, 0.04078311, 0.07749503,0.02886018, -0.08094715, 0.05818976, -0.02744593, -0.00559489,-0.00488863, -0.06092762, 0.15089634, -0.02423968, 0.02867635,0.0041097 , 0.00409226, -0.05106317, -0.0156715 , -0.06731596,0.00594657, 0.02464658, 0.10740153, 0.0207287 , -0.02535357,-0.05631002, -0.01714507, -0.04964483, -0.00834728, -0.01148841,0.04122198, 0.00281052, -0.02053833, 0.01521229, -0.10191563,-0.07321421, -0.01803589, -0.02788144, 0.00172424, 0.07978603,-0.01517505, 0.03893743, -0.0548212 , 0.03782436, 0.04642305,-0.05222284, 0.01304263, -0.06944965, 0.01763625, -0.02670433,-0.03698331, -0.02478899, -0.06544131, 0.05864679, -0.00175549,-0.11564055, -0.10066441, -0.04190209, -0.02992467, -0.08564534,-0.02061244, 0.02688017, -0.0045171 , 0.00165086, 0.10750544,-0.028361 , -0.03209577, 0.0515936 , -0.04164342, 0.02281843,0.08524286, -0.10112653, -0.14161319, -0.05427769, -0.01017171,0.09955125, 0.02694847, -0.0915055 , 0.09549531, -0.0138172 ,0.01547096, 0.00868443, -0.04557078, -0.00442069, 0.01043919,-0.00775728, 0.02804129, 0.10577102, 0.07417879, -0.0414545 ,-0.10446894, 0.07996532, -0.06722441, 0.0636742 , -0.05054583,-0.11369978, 0.02922131, -0.03643508, -0.09067681, -0.06278338,-0.01135545, 0.09446498, -0.02156576, 0.00918143, 0.0722787 ,-0.01088969, 0.03180022, -0.00304031, 0.0532895 , 0.07494827,-0.02797735, -0.06948853, 0.06283715, 0.10689872, 0.02087112,0.05185082, 0.06266276, 0.01831927, 0.10564604, 0.00259254,0.08089193, -0.01426479, 0.00684974, -0.03707304, -0.1198062 ,-0.05715216, 0.01687549, 0.03455462, -0.08835565, 0.05120559,-0.06600516, -0.01664807, -0.02856736, 0.02654157, -0.00975818,-0.03065236, -0.04041981, -0.01071312, -0.05153402, -0.14723714,-0.00877744, 0.08035714, 0.00351824, -0.10722714, -0.03078206,-0.00496383, -0.01665388, 0.0004069 , -0.02276175, 0.14360192,-0.09488932, 0.00554548, 0.13301958, -0.02263096, -0.03730701,0.03650629, -0.02395339, 0.00687372, -0.02563804, 0.03732518,-0.02720424, -0.0106114 , -0.05050805, 0.00444685, -0.02968924,0.07124983, -0.00694057, 0.00107829, -0.08331589, -0.03359186,0.0081293 , -0.0008138 , 0.01801554, 0.02518827, -0.03804089,0.06714594, 0.00194731, 0.08901033, 0.06102903, 0.03237479,-0.05186026, 0.02203078, -0.02689325, -0.01497105, -0.07096935,0.00406174, 0.03199695, -0.05650693, -0.00124395, 0.08180745,0.10938081, 0.0316787 , 0.01944987, -0.02388909, 0.00355748,0.0249256 , 0.00739524, 0.0506243 , -0.01226516, 0.01143035,-0.09211658, -0.02129836, -0.11622447, -0.04239509, -0.05391511,-0.00467064, -0.01021031, 0.00030227, 0.12456985, -0.0130964 ,0.02393832, -0.04647537, 0.06130255, 0.02752686, 0.04820469,-0.06352307, 0.0357637 , -0.1455921 , 0.01995268, -0.04385739,-0.03136626, -0.04338237, -0.08235096, 0.02723331, -0.01401483]) fig = plt.figure(figsize=(16, 16)) plot_LSA(embeddings, list_labels) plt.show()

這看起來就好多啦！

clf_w2v = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg', multi_class='multinomial', random_state=40) clf_w2v.fit(X_train_word2vec, y_train_word2vec) y_predicted_word2vec = clf_w2v.predict(X_test_word2vec) accuracy_word2vec, precision_word2vec, recall_word2vec, f1_word2vec = get_metrics(y_test_word2vec, y_predicted_word2vec) print("accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f" % (accuracy_word2vec, precision_word2vec, recall_word2vec, f1_word2vec)) accuracy = 0.777, precision = 0.776, recall = 0.777, f1 = 0.777 cm_w2v = confusion_matrix(y_test_word2vec, y_predicted_word2vec) fig = plt.figure(figsize=(10, 10)) plot = plot_confusion_matrix(cm, classes=['Irrelevant','Disaster','Unsure'], normalize=False, title='Confusion matrix') plt.show() print("Word2Vec confusion matrix") print(cm_w2v) print("TFIDF confusion matrix") print(cm2) print("BoW confusion matrix") print(cm)

Word2Vec confusion matrix [[980 242 2][232 711 2][ 2 5 0]] TFIDF confusion matrix [[974 249 1][261 684 0][ 3 4 0]] BoW confusion matrix [[970 251 3][274 670 1][ 3 4 0]]

這是目前為止最好的啦

基于深度學習的自然語言處理（CNN與RNN）

from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categoricalEMBEDDING_DIM = 300 MAX_SEQUENCE_LENGTH = 35 VOCAB_SIZE = len(VOCAB)VALIDATION_SPLIT=.2 tokenizer = Tokenizer(num_words=VOCAB_SIZE) tokenizer.fit_on_texts(clean_questions["text"].tolist()) sequences = tokenizer.texts_to_sequences(clean_questions["text"].tolist())word_index = tokenizer.word_index print('Found %s unique tokens.' % len(word_index))cnn_data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH) labels = to_categorical(np.asarray(clean_questions["class_label"]))indices = np.arange(cnn_data.shape[0]) np.random.shuffle(indices) cnn_data = cnn_data[indices] labels = labels[indices] num_validation_samples = int(VALIDATION_SPLIT * cnn_data.shape[0])embedding_weights = np.zeros((len(word_index)+1, EMBEDDING_DIM)) for word,index in word_index.items():embedding_weights[index,:] = word2vec[word] if word in word2vec else np.random.rand(EMBEDDING_DIM) print(embedding_weights.shape) Found 19098 unique tokens. (19099, 300)

Now, we will define a simple Convolutional Neural Network

from keras.layers import Dense, Input, Flatten, Dropout, Merge from keras.layers import Conv1D, MaxPooling1D, Embedding from keras.layers import LSTM, Bidirectional from keras.models import Modeldef ConvNet(embeddings, max_sequence_length, num_words, embedding_dim, labels_index, trainable=False, extra_conv=True):embedding_layer = Embedding(num_words,embedding_dim,weights=[embeddings],input_length=max_sequence_length,trainable=trainable)sequence_input = Input(shape=(max_sequence_length,), dtype='int32')embedded_sequences = embedding_layer(sequence_input)# Yoon Kim model (https://arxiv.org/abs/1408.5882)convs = []filter_sizes = [3,4,5]for filter_size in filter_sizes:l_conv = Conv1D(filters=128, kernel_size=filter_size, activation='relu')(embedded_sequences)l_pool = MaxPooling1D(pool_size=3)(l_conv)convs.append(l_pool)l_merge = Merge(mode='concat', concat_axis=1)(convs)# add a 1D convnet with global maxpooling, instead of Yoon Kim modelconv = Conv1D(filters=128, kernel_size=3, activation='relu')(embedded_sequences)pool = MaxPooling1D(pool_size=3)(conv)if extra_conv==True:x = Dropout(0.5)(l_merge) else:# Original Yoon Kim modelx = Dropout(0.5)(pool)x = Flatten()(x)x = Dense(128, activation='relu')(x)#x = Dropout(0.5)(x)preds = Dense(labels_index, activation='softmax')(x)model = Model(sequence_input, preds)model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['acc'])return model

訓練網絡

x_train = cnn_data[:-num_validation_samples] y_train = labels[:-num_validation_samples] x_val = cnn_data[-num_validation_samples:] y_val = labels[-num_validation_samples:]model = ConvNet(embedding_weights, MAX_SEQUENCE_LENGTH, len(word_index)+1, EMBEDDING_DIM, len(list(clean_questions["class_label"].unique())), False) model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=3, batch_size=128) Train on 8701 samples, validate on 2175 samples Epoch 1/3 8701/8701 [==============================] - 11s - loss: 0.5964 - acc: 0.7067 - val_loss: 0.4970 - val_acc: 0.7848 Epoch 2/3 8701/8701 [==============================] - 11s - loss: 0.4434 - acc: 0.8019 - val_loss: 0.4722 - val_acc: 0.8005 Epoch 3/3 8701/8701 [==============================] - 11s - loss: 0.3968 - acc: 0.8283 - val_loss: 0.4985 - val_acc: 0.7880 <keras.callbacks.History at 0x12237bc88>

總結

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