官网实例详解4.37（pretrained_word_embeddings.py）-keras学习笔记四
预训练词嵌入（向量）
脚本加载预处理的词向量（GloVe embeddings）加载到冻结的Keras嵌入层中，并基于20 Newsgroup数据集使用它来训练文本分类模型。
（把newsgroup消息分为20个类别）
newsgroup，20newsgroups数据集是用于文本分类、文本挖据和信息检索研究的国际标准数据集之一。数据集收集了大约20,000左右的新闻组文档，均匀分为20个不同主题的新闻组集合。一些新闻组的主题特别相似(e.g. comp.sys.ibm.pc.hardware/ comp.sys.mac.hardware)，还有一些却完全不相关 (e.g misc.forsale /soc.religion.christian)。
如何理解
Embedding的作用，转换一个文本句子为（神经网络）容易处理的一个数据样本（矩阵是该样本的数据表现形式）。
可以理解Embedding处理后输出的数据，为一张单通道的图，后续的卷积操作类似处理一张图，期望获取图片（文本句子）的分类。
词向量是训练神经网络时候的隐藏层参数或者说矩阵。
意义：词向量降维，例如用one-hot编码20000个词的词汇表，需用20000x20000的矩阵表示，通过降维20000个词的词汇表可以20000x100（还可以取值200,1000）的矩阵表示
Keras实例目录
代码注释
'''This script loads pre-trained word embeddings (GloVe embeddings)
into a frozen Keras Embedding layer, and uses it to
train a text classification model on the 20 Newsgroup dataset
(classification of newsgroup messages into 20 different categories).
脚本加载预处理的词向量（GloVe embeddings）加载到冻结的Keras嵌入层中，并基于20 Newsgroup数据集使用它来训练文本分类模型。
（把newsgroup消息分为20个类别）
GloVe embedding data can be found at:
数据见：
http://nlp.stanford.edu/data/glove.6B.zip
(source page: http://nlp.stanford.edu/projects/glove/)
(下载见: http://nlp.stanford.edu/projects/glove/)
20 Newsgroup data can be found at:
20 Newsgroup （数据集）下载见:
http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/news20.html
'''
from __future__ import print_function
import os
import sys
import numpy as np
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from keras.layers import Dense, Input, GlobalMaxPooling1D
from keras.layers import Conv1D, MaxPooling1D, Embedding
from keras.models import Model
BASE_DIR = ''
GLOVE_DIR = os.path.join(BASE_DIR, 'glove.6B')
TEXT_DATA_DIR = os.path.join(BASE_DIR, '20_newsgroup')
MAX_SEQUENCE_LENGTH = 1000
MAX_NUM_WORDS = 20000
EMBEDDING_DIM = 100
VALIDATION_SPLIT = 0.2
# first, build index mapping words in the embeddings set
# to their embedding vector
# 首先，在嵌入到其嵌入向量中的索引中建立索引映射。
print('Indexing word vectors.')
embeddings_index = {}
with open(os.path.join(GLOVE_DIR, 'glove.6B.100d.txt')) as f:
for line in f:
values = line.split()
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
print('Found %s word vectors.' % len(embeddings_index))
# second, prepare text samples and their labels
# 第二步，准备文本样本和对应标签
print('Processing text dataset')
texts = [] # list of text samples # 文本样本列表
labels_index = {} # dictionary mapping label name to numeric id # 标签名和编号映射到词典
labels = [] # list of label ids # 标签编号列表
for name in sorted(os.listdir(TEXT_DATA_DIR)):
path = os.path.join(TEXT_DATA_DIR, name)
if os.path.isdir(path):
label_id = len(labels_index)
labels_index[name] = label_id
for fname in sorted(os.listdir(path)):
if fname.isdigit():
fpath = os.path.join(path, fname)
args = {} if sys.version_info < (3,) else {'encoding': 'latin-1'}
with open(fpath, **args) as f:
t = f.read()
i = t.find('\n\n') # skip header
if 0 < i:
t = t[i:]
texts.append(t)
labels.append(label_id)
print('Found %s texts.' % len(texts))
# finally, vectorize the text samples into a 2D integer tensor
# 最后，将文本样本向量化为二维整数张量。
tokenizer = Tokenizer(num_words=MAX_NUM_WORDS)
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
word_index = tokenizer.word_index
print('Found %s unique tokens.' % len(word_index))
data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)
labels = to_categorical(np.asarray(labels))
print('Shape of data tensor:', data.shape)
print('Shape of label tensor:', labels.shape)
# split the data into a training set and a validation set
# 分割数据集为训练和验证集
indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
labels = labels[indices]
num_validation_samples = int(VALIDATION_SPLIT * data.shape[0])
x_train = data[:-num_validation_samples]
y_train = labels[:-num_validation_samples]
x_val = data[-num_validation_samples:]
y_val = labels[-num_validation_samples:]
print('Preparing embedding matrix.')
# prepare embedding matrix
# 准备嵌入矩阵
num_words = min(MAX_NUM_WORDS, len(word_index))
embedding_matrix = np.zeros((num_words, EMBEDDING_DIM))
for word, i in word_index.items():
if i >= MAX_NUM_WORDS:
continue
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
# 嵌入索引中找不到的词设置为全零。
embedding_matrix[i] = embedding_vector
# load pre-trained word embeddings into an Embedding layer
# note that we set trainable = False so as to keep the embeddings fixed
# 将预先训练的单词嵌入到嵌入层中，注意我们设置trainable= false，以便保持嵌入固定。
embedding_layer = Embedding(num_words,
EMBEDDING_DIM,
weights=[embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=False)
print('Training model.')
# train a 1D convnet with global maxpooling
# 用全局最大池技术训练1D卷积网络
sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
x = Conv1D(128, 5, activation='relu')(embedded_sequences)
x = MaxPooling1D(5)(x)
x = Conv1D(128, 5, activation='relu')(x)
x = MaxPooling1D(5)(x)
x = Conv1D(128, 5, activation='relu')(x)
x = GlobalMaxPooling1D()(x)
x = Dense(128, activation='relu')(x)
preds = Dense(len(labels_index), activation='softmax')(x)
model = Model(sequence_input, preds)
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['acc'])
model.fit(x_train, y_train,
batch_size=128,
epochs=10,
validation_data=(x_val, y_val))
代码执行
C:\ProgramData\Anaconda3\python.exe E:/keras-master/examples/pretrained_word_embeddings.py
Using TensorFlow backend.
Indexing word vectors.
Found 400000 word vectors.
Processing text dataset
Found 19997 texts.
Found 174074 unique tokens.
Shape of data tensor: (19997, 1000)
Shape of label tensor: (19997, 20)
Preparing embedding matrix.
Training model.
Train on 15998 samples, validate on 3999 samples
Epoch 1/10
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15998/15998 [==============================] - 26s 2ms/step - loss: 2.3898 - acc: 0.2123 - val_loss: 1.7477 - val_acc: 0.3823
Epoch 2/10
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15872/15998 [============================>.] - ETA: 0s - loss: 1.5594 - acc: 0.4621
15998/15998 [==============================] - 9s 550us/step - loss: 1.5585 - acc: 0.4625 - val_loss: 1.4428 - val_acc: 0.5076
Epoch 3/10
128/15998 [..............................] - ETA: 8s - loss: 1.4432 - acc: 0.5156
256/15998 [..............................] - ETA: 8s - loss: 1.4685 - acc: 0.5000
384/15998 [..............................] - ETA: 8s - loss: 1.4377 - acc: 0.5000
512/15998 [..............................] - ETA: 8s - loss: 1.4322 - acc: 0.5098
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768/15998 [>.............................] - ETA: 8s - loss: 1.3286 - acc: 0.5365
896/15998 [>.............................] - ETA: 7s - loss: 1.2856 - acc: 0.5547
1024/15998 [>.............................] - ETA: 7s - loss: 1.2669 - acc: 0.5625
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14976/15998 [===========================>..] - ETA: 0s - loss: 1.2060 - acc: 0.5802
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15232/15998 [===========================>..] - ETA: 0s - loss: 1.2048 - acc: 0.5809
15360/15998 [===========================>..] - ETA: 0s - loss: 1.2036 - acc: 0.5816
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15872/15998 [============================>.] - ETA: 0s - loss: 1.2013 - acc: 0.5823
15998/15998 [==============================] - 9s 548us/step - loss: 1.2004 - acc: 0.5824 - val_loss: 1.0866 - val_acc: 0.6412
Epoch 4/10
128/15998 [..............................] - ETA: 7s - loss: 1.0405 - acc: 0.7188
256/15998 [..............................] - ETA: 7s - loss: 0.9775 - acc: 0.7109
384/15998 [..............................] - ETA: 7s - loss: 1.0334 - acc: 0.6849
512/15998 [..............................] - ETA: 7s - loss: 1.0898 - acc: 0.6621
640/15998 [>.............................] - ETA: 7s - loss: 1.0770 - acc: 0.6625
768/15998 [>.............................] - ETA: 7s - loss: 1.0884 - acc: 0.6510
896/15998 [>.............................] - ETA: 7s - loss: 1.0646 - acc: 0.6518
1024/15998 [>.............................] - ETA: 7s - loss: 1.0433 - acc: 0.6592
1152/15998 [=>............................] - ETA: 7s - loss: 1.0249 - acc: 0.6667
1280/15998 [=>............................] - ETA: 7s - loss: 1.0287 - acc: 0.6672
1408/15998 [=>............................] - ETA: 7s - loss: 1.0226 - acc: 0.6719
1536/15998 [=>............................] - ETA: 7s - loss: 1.0540 - acc: 0.6608
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1920/15998 [==>...........................] - ETA: 6s - loss: 1.0381 - acc: 0.6635
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14976/15998 [===========================>..] - ETA: 0s - loss: 0.9896 - acc: 0.6605
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15232/15998 [===========================>..] - ETA: 0s - loss: 0.9891 - acc: 0.6608
15360/15998 [===========================>..] - ETA: 0s - loss: 0.9882 - acc: 0.6609
15488/15998 [============================>.] - ETA: 0s - loss: 0.9869 - acc: 0.6614
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15872/15998 [============================>.] - ETA: 0s - loss: 0.9856 - acc: 0.6619
15998/15998 [==============================] - 9s 550us/step - loss: 0.9845 - acc: 0.6625 - val_loss: 1.0490 - val_acc: 0.6517
Epoch 5/10
128/15998 [..............................] - ETA: 7s - loss: 0.7645 - acc: 0.7188
256/15998 [..............................] - ETA: 7s - loss: 0.7739 - acc: 0.7305
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512/15998 [..............................] - ETA: 7s - loss: 0.7363 - acc: 0.7539
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768/15998 [>.............................] - ETA: 7s - loss: 0.8080 - acc: 0.7253
896/15998 [>.............................] - ETA: 7s - loss: 0.8588 - acc: 0.7109
1024/15998 [>.............................] - ETA: 7s - loss: 0.8869 - acc: 0.6973
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14208/15998 [=========================>....] - ETA: 0s - loss: 0.8385 - acc: 0.7139
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14464/15998 [==========================>...] - ETA: 0s - loss: 0.8384 - acc: 0.7143
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14848/15998 [==========================>...] - ETA: 0s - loss: 0.8381 - acc: 0.7144
14976/15998 [===========================>..] - ETA: 0s - loss: 0.8384 - acc: 0.7141
15104/15998 [===========================>..] - ETA: 0s - loss: 0.8424 - acc: 0.7133
15232/15998 [===========================>..] - ETA: 0s - loss: 0.8420 - acc: 0.7134
15360/15998 [===========================>..] - ETA: 0s - loss: 0.8408 - acc: 0.7143
15488/15998 [============================>.] - ETA: 0s - loss: 0.8411 - acc: 0.7144
15616/15998 [============================>.] - ETA: 0s - loss: 0.8401 - acc: 0.7148
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15872/15998 [============================>.] - ETA: 0s - loss: 0.8385 - acc: 0.7157
15998/15998 [==============================] - 9s 549us/step - loss: 0.8377 - acc: 0.7157 - val_loss: 1.1228 - val_acc: 0.6347
Epoch 6/10
128/15998 [..............................] - ETA: 8s - loss: 0.8649 - acc: 0.7344
256/15998 [..............................] - ETA: 8s - loss: 0.7988 - acc: 0.7461
384/15998 [..............................] - ETA: 8s - loss: 0.7534 - acc: 0.7630
512/15998 [..............................] - ETA: 7s - loss: 0.7067 - acc: 0.7773
640/15998 [>.............................] - ETA: 7s - loss: 0.7061 - acc: 0.7750
768/15998 [>.............................] - ETA: 7s - loss: 0.7240 - acc: 0.7695
896/15998 [>.............................] - ETA: 7s - loss: 0.7294 - acc: 0.7701
1024/15998 [>.............................] - ETA: 7s - loss: 0.6924 - acc: 0.7812
1152/15998 [=>............................] - ETA: 7s - loss: 0.6977 - acc: 0.7804
1280/15998 [=>............................] - ETA: 7s - loss: 0.6832 - acc: 0.7820
1408/15998 [=>............................] - ETA: 7s - loss: 0.6789 - acc: 0.7820
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1664/15998 [==>...........................] - ETA: 7s - loss: 0.6817 - acc: 0.7806
1792/15998 [==>...........................] - ETA: 7s - loss: 0.6799 - acc: 0.7818
1920/15998 [==>...........................] - ETA: 7s - loss: 0.6860 - acc: 0.7776
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2304/15998 [===>..........................] - ETA: 6s - loss: 0.7277 - acc: 0.7626
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2560/15998 [===>..........................] - ETA: 6s - loss: 0.7368 - acc: 0.7598
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15104/15998 [===========================>..] - ETA: 0s - loss: 0.7139 - acc: 0.7604
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15872/15998 [============================>.] - ETA: 0s - loss: 0.7170 - acc: 0.7598
15998/15998 [==============================] - 9s 558us/step - loss: 0.7168 - acc: 0.7600 - val_loss: 0.8966 - val_acc: 0.7147
Epoch 7/10
128/15998 [..............................] - ETA: 7s - loss: 0.5712 - acc: 0.8047
256/15998 [..............................] - ETA: 7s - loss: 0.5291 - acc: 0.8164
384/15998 [..............................] - ETA: 7s - loss: 0.5010 - acc: 0.8255
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768/15998 [>.............................] - ETA: 7s - loss: 0.5242 - acc: 0.8112
896/15998 [>.............................] - ETA: 7s - loss: 0.5497 - acc: 0.8036
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14848/15998 [==========================>...] - ETA: 0s - loss: 0.6020 - acc: 0.7940
14976/15998 [===========================>..] - ETA: 0s - loss: 0.6007 - acc: 0.7948
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15232/15998 [===========================>..] - ETA: 0s - loss: 0.6018 - acc: 0.7942
15360/15998 [===========================>..] - ETA: 0s - loss: 0.6030 - acc: 0.7935
15488/15998 [============================>.] - ETA: 0s - loss: 0.6045 - acc: 0.7929
15616/15998 [============================>.] - ETA: 0s - loss: 0.6060 - acc: 0.7919
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15872/15998 [============================>.] - ETA: 0s - loss: 0.6054 - acc: 0.7919
15998/15998 [==============================] - 9s 558us/step - loss: 0.6055 - acc: 0.7917 - val_loss: 0.9708 - val_acc: 0.6957
Epoch 8/10
128/15998 [..............................] - ETA: 8s - loss: 0.5848 - acc: 0.8438
256/15998 [..............................] - ETA: 8s - loss: 0.4698 - acc: 0.8750
384/15998 [..............................] - ETA: 7s - loss: 0.4496 - acc: 0.8776
512/15998 [..............................] - ETA: 7s - loss: 0.4409 - acc: 0.8730
640/15998 [>.............................] - ETA: 7s - loss: 0.4466 - acc: 0.8594
768/15998 [>.............................] - ETA: 7s - loss: 0.4539 - acc: 0.8555
896/15998 [>.............................] - ETA: 7s - loss: 0.4467 - acc: 0.8516
1024/15998 [>.............................] - ETA: 7s - loss: 0.4512 - acc: 0.8467
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14720/15998 [==========================>...] - ETA: 0s - loss: 0.5156 - acc: 0.8231
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15360/15998 [===========================>..] - ETA: 0s - loss: 0.5133 - acc: 0.8234
15488/15998 [============================>.] - ETA: 0s - loss: 0.5119 - acc: 0.8241
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15872/15998 [============================>.] - ETA: 0s - loss: 0.5163 - acc: 0.8229
15998/15998 [==============================] - 9s 559us/step - loss: 0.5183 - acc: 0.8224 - val_loss: 1.0839 - val_acc: 0.6692
Epoch 9/10
128/15998 [..............................] - ETA: 8s - loss: 0.4888 - acc: 0.8516
256/15998 [..............................] - ETA: 7s - loss: 0.4361 - acc: 0.8516
384/15998 [..............................] - ETA: 7s - loss: 0.4150 - acc: 0.8568
512/15998 [..............................] - ETA: 7s - loss: 0.4276 - acc: 0.8555
640/15998 [>.............................] - ETA: 7s - loss: 0.4024 - acc: 0.8656
768/15998 [>.............................] - ETA: 7s - loss: 0.3762 - acc: 0.8750
896/15998 [>.............................] - ETA: 7s - loss: 0.3715 - acc: 0.8806
1024/15998 [>.............................] - ETA: 7s - loss: 0.3590 - acc: 0.8867
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14464/15998 [==========================>...] - ETA: 0s - loss: 0.4423 - acc: 0.8514
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14976/15998 [===========================>..] - ETA: 0s - loss: 0.4405 - acc: 0.8520
15104/15998 [===========================>..] - ETA: 0s - loss: 0.4398 - acc: 0.8520
15232/15998 [===========================>..] - ETA: 0s - loss: 0.4407 - acc: 0.8515
15360/15998 [===========================>..] - ETA: 0s - loss: 0.4408 - acc: 0.8516
15488/15998 [============================>.] - ETA: 0s - loss: 0.4400 - acc: 0.8518
15616/15998 [============================>.] - ETA: 0s - loss: 0.4401 - acc: 0.8519
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15872/15998 [============================>.] - ETA: 0s - loss: 0.4409 - acc: 0.8516
15998/15998 [==============================] - 9s 558us/step - loss: 0.4422 - acc: 0.8514 - val_loss: 1.0354 - val_acc: 0.7007
Epoch 10/10
128/15998 [..............................] - ETA: 7s - loss: 0.3569 - acc: 0.8672
256/15998 [..............................] - ETA: 7s - loss: 0.3463 - acc: 0.8828
384/15998 [..............................] - ETA: 7s - loss: 0.3560 - acc: 0.8828
512/15998 [..............................] - ETA: 7s - loss: 0.3268 - acc: 0.8926
640/15998 [>.............................] - ETA: 7s - loss: 0.3182 - acc: 0.8984
768/15998 [>.............................] - ETA: 7s - loss: 0.3285 - acc: 0.8958
896/15998 [>.............................] - ETA: 7s - loss: 0.3226 - acc: 0.8962
1024/15998 [>.............................] - ETA: 7s - loss: 0.3167 - acc: 0.8984
1152/15998 [=>............................] - ETA: 7s - loss: 0.3295 - acc: 0.8958
1280/15998 [=>............................] - ETA: 7s - loss: 0.3342 - acc: 0.8945
1408/15998 [=>............................] - ETA: 7s - loss: 0.3308 - acc: 0.8956
1536/15998 [=>............................] - ETA: 7s - loss: 0.3319 - acc: 0.8945
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2304/15998 [===>..........................] - ETA: 6s - loss: 0.3609 - acc: 0.8832
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2560/15998 [===>..........................] - ETA: 6s - loss: 0.3550 - acc: 0.8852
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2816/15998 [====>.........................] - ETA: 6s - loss: 0.3464 - acc: 0.8874
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Process finished with exit code 0
详细介绍
1.下载 glove.6B.100d.txt 文件，存放在 pretrained_word_embeddings.py 文件同级目录的 glove.6B 文件夹下：
glove.6B.100d.txt （40万条记录） 中存放词和对应的向量（每个词100维）
格式：词+空格+向量（向量各元素，以空格分割）
范围：文本单词、标点符号
下图是“the”、“，”，“.”,“of”，“to”词和词向量
the -0.038194 -0.24487 0.72812 -0.39961 0.083172 0.043953 -0.39141 0.3344-0.57545 0.087459 0.28787 -0.06731 0.30906 -0.26384 -0.13231 -0.20757 0.33395 -0.33848 -0.31743 -0.48336 0.1464 -0.37304 0.34577 0.052041 0.44946 -0.46971 0.02628 -0.54155 -0.15518 -0.14107 -0.039722 0.28277 0.14393 0.23464 -0.31021 0.086173 0.20397 0.52624 0.17164 -0.082378 -0.71787 -0.41531 0.20335 -0.12763 0.41367 0.55187 0.57908 -0.33477 -0.36559 -0.54857 -0.062892 0.26584 0.30205 0.99775 -0.80481 -3.0243 0.01254 -0.36942 2.2167 0.72201 -0.24978 0.92136 0.034514 0.46745 1.1079 -0.19358 -0.074575 0.23353 -0.052062 -0.22044 0.057162 -0.15806 -0.30798 -0.41625 0.37972 0.15006 -0.53212 -0.2055 -1.2526 0.071624 0.70565 0.49744 -0.42063 0.26148 -1.538 -0.30223 -0.073438 -0.28312 0.37104 -0.25217 0.016215 -0.017099 -0.38984 0.87424 -0.72569 -0.51058 -0.52028 -0.1459 0.8278 0.27062
, -0.10767 0.11053 0.59812 -0.54361 0.67396 0.10663 0.038867 0.35481 0.06351 -0.094189 0.15786 -0.81665 0.14172 0.21939 0.58505 -0.52158 0.22783 -0.16642 -0.68228 0.3587 0.42568 0.19021 0.91963 0.57555 0.46185 0.42363 -0.095399 -0.42749 -0.16567 -0.056842 -0.29595 0.26037 -0.26606 -0.070404 -0.27662 0.15821 0.69825 0.43081 0.27952 -0.45437 -0.33801 -0.58184 0.22364 -0.5778 -0.26862 -0.20425 0.56394 -0.58524 -0.14365 -0.64218 0.0054697 -0.35248 0.16162 1.1796 -0.47674 -2.7553 -0.1321 -0.047729 1.0655 1.1034 -0.2208 0.18669 0.13177 0.15117 0.7131 -0.35215 0.91348 0.61783 0.70992 0.23955 -0.14571 -0.37859 -0.045959 -0.47368 0.2385 0.20536 -0.18996 0.32507 -1.1112 -0.36341 0.98679 -0.084776 -0.54008 0.11726 -1.0194 -0.24424 0.12771 0.013884 0.080374 -0.35414 0.34951 -0.7226 0.37549 0.4441 -0.99059 0.61214 -0.35111 -0.83155 0.45293 0.082577
. -0.33979 0.20941 0.46348 -0.64792 -0.38377 0.038034 0.17127 0.15978 0.46619 -0.019169 0.41479 -0.34349 0.26872 0.04464 0.42131 -0.41032 0.15459 0.022239 -0.64653 0.25256 0.043136 -0.19445 0.46516 0.45651 0.68588 0.091295 0.21875 -0.70351 0.16785 -0.35079 -0.12634 0.66384 -0.2582 0.036542 -0.13605 0.40253 0.14289 0.38132 -0.12283 -0.45886 -0.25282 -0.30432 -0.11215 -0.26182 -0.22482 -0.44554 0.2991 -0.85612 -0.14503 -0.49086 0.0082973 -0.17491 0.27524 1.4401 -0.21239 -2.8435 -0.27958 -0.45722 1.6386 0.78808 -0.55262 0.65 0.086426 0.39012 1.0632 -0.35379 0.48328 0.346 0.84174 0.098707 -0.24213 -0.27053 0.045287 -0.40147 0.11395 0.0062226 0.036673 0.018518 -1.0213 -0.20806 0.64072 -0.068763 -0.58635 0.33476 -1.1432 -0.1148 -0.25091 -0.45907 -0.096819 -0.17946 -0.063351 -0.67412 -0.068895 0.53604 -0.87773 0.31802 -0.39242 -0.23394 0.47298 -0.028803
of -0.1529 -0.24279 0.89837 0.16996 0.53516 0.48784 -0.58826 -0.17982 -1.3581 0.42541 0.15377 0.24215 0.13474 0.41193 0.67043 -0.56418 0.42985 -0.012183 -0.11677 0.31781 0.054177 -0.054273 0.35516 -0.30241 0.31434 -0.33846 0.71715 -0.26855 -0.15837 -0.47467 0.051581 -0.33252 0.15003 -0.1299 -0.54617 -0.37843 0.64261 0.82187 -0.080006 0.078479 -0.96976 -0.57741 0.56491 -0.39873 -0.057099 0.19743 0.065706 -0.48092 -0.20125 -0.40834 0.39456 -0.02642 -0.11838 1.012 -0.53171 -2.7474 -0.042981 -0.74849 1.7574 0.59085 0.04885 0.78267 0.38497 0.42097 0.67882 0.10337 0.6328 -0.026595 0.58647 -0.44332 0.33057 -0.12022 -0.55645 0.073611 0.20915 0.43395 -0.012761 0.089874 -1.7991 0.084808 0.77112 0.63105 -0.90685 0.60326 -1.7515 0.18596 -0.50687 -0.70203 0.66578 -0.81304 0.18712 -0.018488 -0.26757 0.727 -0.59363 -0.34839 -0.56094 -0.591 1.0039 0.20664
to -0.1897 0.050024 0.19084 -0.049184 -0.089737 0.21006 -0.54952 0.098377 -0.20135 0.34241 -0.092677 0.161 -0.13268 -0.2816 0.18737 -0.42959 0.96039 0.13972 -1.0781 0.40518 0.50539 -0.55064 0.4844 0.38044 -0.0029055 -0.34942 -0.099696 -0.78368 1.0363 -0.2314 -0.47121 0.57126 -0.21454 0.35958 -0.48319 1.0875 0.28524 0.12447 -0.039248 -0.076732 -0.76343 -0.32409 -0.5749 -1.0893 -0.41811 0.4512 0.12112 -0.51367 -0.13349 -1.1378 -0.28768 0.16774 0.55804 1.5387 0.018859 -2.9721 -0.24216 -0.92495 2.1992 0.28234 -0.3478 0.51621 -0.43387 0.36852 0.74573 0.072102 0.27931 0.92569 -0.050336 -0.85856 -0.1358 -0.92551 -0.33991 -1.0394 -0.067203 -0.21379 -0.4769 0.21377 -0.84008 0.052536 0.59298 0.29604 -0.67644 0.13916 -1.5504 -0.20765 0.7222 0.52056 -0.076221 -0.15194 -0.13134 0.058617 -0.31869 -0.61419 -0.62393 -0.41548 -0.038175 -0.39804 0.47647 -0.15983
20_newsgroup 文件夹中内容（20种新闻类别）
20_newsgroup 每个子文件中有1000个文件，例如alt.atheism文件夹中文件列表
alt.atheism文件夹中49960文件中内容如下：
embedding_layer = Embedding(num_words,
EMBEDDING_DIM, # EMBEDDING_DIM = 100
weights=[embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH, # MAX_SEQUENCE_LENGTH = 1000
trainable=False)
1.首先构建一个单词和降维编码（已经不是one-hot编码（维度太高），100维数据，已经降维）的对应关系表
表 单词和降维编码举例（假设该词汇表有20000个词，）
| 编号 | 词 | 索引 | one-hot编码 | 降维编码 |
| 1 | the | 1 | 1 0. . . 0 (1后面19999个0，维度太高，需要降维) | 0.256 0.311 ... 0.123 (100维) |
| 2 | to | 2 | 0 1 . . . 0 (1后面19998个0，维度太高，需要降维) | 0.152 0.201 ... 0.423 (100维) |
如果句子是1000个单词，每个单词100维度（怎么来，见1降维编码）
| 编号 | 词（句子的词按顺序为矩阵的一行） | 索引 | 降维编码（用一组数字表示，每组1000个数字） | |
| 1 | the | 1 | 0.256 0.311 ... 0.123 (100维) | |
| 2 | to | 2 | 0.152 0.201 ... 0.423 (100维) |
依次类推，构成一个1000（句子单词数）x100（每个单词的降维编码）的（句子）特征矩阵。
可以理解为一张单通道的图，后续的卷积操作类似在一张图处理，结果是获取图片的分类。
Embedding的作用，转换一个文本句子为容易（神经网络）处理的一个数据（样本，矩阵是该样本的数据表现形式）。
建议阅读
Embedding
本分类中的降维方法总结
word2vec－词向量embeding实现
【word2vec】Distributed Representation——词向量
不懂word2vec，还敢说自己是做NLP？
Keras详细介绍
英文：https://keras.io/
中文：http://keras-cn.readthedocs.io/en/latest/
实例下载
https://github.com/keras-team/keras
https://github.com/keras-team/keras/tree/master/examples
完整项目下载
方便没积分童鞋，请加企鹅452205574，共享文件夹。
包括：代码、数据集合（图片）、已生成model、安装库文件等。
