https://blog.csdn.net/qq_39131592/article/details/79056185
根据自己的理解写的读书笔记。
import collections import math import os import random import zipfile import urllib import numpy as np import tensorflow as tf #定义下载文本数据的函数 # url = 'http://mattmahoney.net/dc/' # # def maybe_download(filename,expected_bytes): # if not os.path.exists(filename): # filename,_ = urllib.request.urlretrieve(url + filename,filename) # statinfo = os.stat(filename) #访问一个文件的详细信息。 # if statinfo.st_size == expected_bytes: #文件大小(以字节为单位) # print('Found and verified(验证）',filename) # else: # print(statinfo.st_size) # raise Exception('Failed to verify(验证）' + filename + 'Can you get to it with a browser(浏览器)?') # return filename # # filename = maybe_download('text8.zip',31344016) filename = './text8.zip' #解压文件，并将数据转化成单词的列表 def read_data(filename): with zipfile.ZipFile(filename) as f: #获得名字列表，读取成字符串，编码成'utf-8'，最后进行分割 data = tf.compat.as_str(f.read(f.namelist()[0])).split() return data words = read_data(filename) # print('Data size',len(words)) # print(words) #创建词汇表，将出现最多的50000个单词作为词汇表，放入字典中。 vocabulary_size = 50000 def build_dataset(words): count = [['UNK',-1]] count.extend(collections.Counter(words).most_common(vocabulary_size - 1)) # c=collections.Counter(words).most_common(10) # print(c) # count.extend(c) # print(count) #[['UNK', -1], ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764), ('in', 372201), ('a', 325873), ('to', 316376), ('zero', 264975), ('nine', 250430), ('two', 192644)] dictionary = dict()#新建空字典 for word,_ in count: dictionary[word] = len(dictionary) # print(dictionary) #{'UNK': 0, 'the': 1, 'of': 2, 'and': 3, 'one': 4, 'in': 5, 'a': 6, 'to': 7, 'zero': 8, 'nine': 9, 'two': 10} data = list() unk_count = 0#未知单词数量 for word in words:#单词索引，不在字典中，则索引为0 if word in dictionary: index = dictionary[word] else: index = 0 unk_count += 1 data.append(index) count[0][1] = unk_count reverse_dictionary = dict(zip(dictionary.values(),dictionary.keys())) return data,count,dictionary,reverse_dictionary data,count,dictionary,reverse_dictionary = build_dataset(words) #删除原始单词列表，节约内存。打印词汇表，了解词频 del words # print('Most common words (+UNK)',count[:5]) # print('Sample data',data[:10],[reverse_dictionary[i] for i in data[:10]]) #以上代码为数据处理，得到单词的词频和在字典中的索引 #skip-gram模式：从目标单词反推语境 data_index = 0 #生成训练用的batch数据 #batch_size为batch大小，num_skips为对每个单词生成样本数，skip_window为单词最远可以联系的距离 def generate_batch(batch_size,num_skips,skip_window): global data_index #声明全局变量 assert batch_size % num_skips == 0#断言batch_size是num_skips的整倍数 assert num_skips <= 2 * skip_window#断言num_skips不大于skip_window的两倍 batch = np.ndarray(shape=(batch_size),dtype=np.int32)#初始化为数组 labels = np.ndarray(shape=(batch_size,1),dtype=np.int32) span = 2 * skip_window + 1 #对某个单词创建相关样本时会使用到的单词数量 buffer = collections.deque(maxlen=span) #创建最大容量为span的队列，即双向队列 for _ in range(span): buffer.append(data[data_index]) data_index = (data_index + 1) % len(data) for i in range(batch_size // num_skips):#'//'取商的整数部分 target = skip_window targets_to_avoid = [skip_window]#因为要预测语境单词，不包括目标单词本身。所以需要一个避免列表 for j in range(num_skips): while target in targets_to_avoid: target = random.randint(0, span - 1) targets_to_avoid.append(target) batch[i * num_skips + j] = buffer[skip_window] labels[i * num_skips + j, 0] = buffer[target] buffer.append(data[data_index]) data_index = (data_index + 1) % len(data) return batch,labels # batch,labels = generate_batch(batch_size=8,num_skips=2,skip_window=1) # print(batch)#[3081 3081 12 12 6 6 195 195] # print(labels)#[[5234] # # [ 12] # # [3081] # # [ 6] # # [ 12] # # [ 195] # # [ 6] # # [ 2]] # for i in range(8): # print(batch[i],reverse_dictionary[batch[i]],'->',labels[i,0],reverse_dictionary[labels[i,0]]) batch_size = 128 embedding_size = 128#将单词转为稠密向量的维度，一般在50~1000范围 skip_window = 1 num_skips = 2 valid_size = 16 valid_window = 100 valid_examples = np.random.choice(valid_window,valid_size,replace=False)#生成验证数据，随机抽取词频最高（前valid_window）的valid_size个单词 num_sampled = 64#做负样本的噪声单词数量 #定义skip-gram网络结构 graph = tf.Graph() with graph.as_default(): train_inputs = tf.placeholder(tf.int32,shape=[batch_size]) train_labels = tf.placeholder(tf.int32,shape=[batch_size,1]) valid_dataset = tf.constant(valid_examples,dtype=tf.int32) #限定所有计算都在cpu上执行，因为接下来一些计算操作在GPU上可能还没有实现 with tf.device('/cpu:0'): embeddings = tf.Variable(tf.random_uniform([vocabulary_size,embedding_size],-1.0,1.0))#随机生成所有单词的词向量，单词表大小50000，维度128 embed = tf.nn.embedding_lookup(embeddings,train_inputs)#查找输入train_inputs在embeddings里对应的向量 #用截断正态分布truncated_normal初始化NCE Loss中的权重参数nce_weights，并将其初始化为0 nce_weights = tf.Variable(tf.truncated_normal([vocabulary_size,embedding_size],stddev=1.0/math.sqrt(embedding_size))) nce_biases = tf.Variable(tf.zeros([vocabulary_size])) loss = tf.reduce_mean(tf.nn.nce_loss(weights=nce_weights,biases=nce_biases,labels=train_labels,inputs=embed,num_sampled=num_sampled,num_classes=vocabulary_size)) #优化器SGD，学习率1.0 optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss) #先计算embeddings的平方，并按第二维降维到1，计算嵌入向量embeddings的L2范数 norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings),1,keep_dims=True)) #标准化embeddings normalized_embeddings = embeddings/norm #查询单词的嵌入向量，并计算验证单词的嵌入向量与词汇表中所有单词的相似性 valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings,valid_dataset) #transpose_b=True 将b转置 similarity = tf.matmul(valid_embeddings,normalized_embeddings,transpose_b=True) #初始化所有模型参数 init = tf.global_variables_initializer() num_steps = 100001#迭代10万次 with tf.Session(graph=graph) as session: init.run() print('Initialized') average_loss = 0 for step in range(num_steps): batch_inputs,batch_labels = generate_batch(batch_size,num_skips,skip_window) feed_dict = {train_inputs : batch_inputs,train_labels : batch_labels} _,loss_val = session.run([optimizer,loss],feed_dict=feed_dict) average_loss += loss_val if step % 2000 == 0: if step > 0: average_loss /= 2000 print('Average loss at step ',step,': ',average_loss) average_loss = 0 if step % 10000 == 0: sim = similarity.eval() for i in range(valid_size): valid_word = reverse_dictionary[valid_examples[i]] top_k = 8 nearest = (-sim[i, :]).argsort()[1:top_k+1]#argsort将数组从小到大排列，并返回索引 log_str = 'Nearest to %s:' % valid_word for k in range(top_k): close_word = reverse_dictionary[nearest[k]] log_str = '%s %s,' % (log_str,close_word) print(log_str) final_embeddings = normalized_embeddings.eval() from sklearn.manifold import TSNE#此降维算法比PCA更高级，可视化 import matplotlib.pyplot as plt def plot_with_labels(low_dim_embs,labels,filename='tsne.png'): assert low_dim_embs.shape[0] >= len(labels),'More labels than embeddings' plt.figure(figsize=(18,18)) for i,label in enumerate(labels):#enumerate枚举可遍历、迭代（列表、字符串）对象，加上索引 x,y = low_dim_embs[i,:] plt.scatter(x,y)#显示散点图 #（工具书p242）annotate在图上添加注释，xy设置箭头所指处的坐标，xytext注释内容坐标，textcoords注释内容坐标的坐标变换方式。 #'offset points'以点为单位，相对于点xy的坐标 # ha='right'点在注释右边（right,center,left），va='bottom'点在注释底部('top', 'bottom', 'center', 'baseline') plt.annotate(label,xy=(x,y),xytext=(5,2),textcoords='offset points',ha='right',va='bottom') plt.savefig(filename) #perplexity（混乱，复杂）与最近邻数有关，一般在5~50，n_iter达到最优化所需的最大迭代次数，应当不少于250次 #init='pca'pca初始化比random稳定，n_components嵌入空间的维数（即降到2维，默认为2 tsne = TSNE(perplexity=30,n_components=2,init='pca',n_iter=5000) plot_only = 100#显示词频最高的一百个 low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only,:]) labels = [reverse_dictionary[i] for i in range(plot_only)] plot_with_labels(low_dim_embs,labels) # plt.show()
笔记
tf.compat(compat兼容性)
NAME
tensorflow.python.util.compat - Functions for Python 2 vs. 3 compatibility.
DESCRIPTION
## Conversion routines
In addition to the functions below, `as_str` converts an object to a `str`.
@@as_bytes
@@as_text
@@as_str_any
## Types
The compatibility module also provides the following types:
* `bytes_or_text_types`
* `complex_types`
* `integral_types`
* `real_types`
FUNCTIONS
as_bytes(bytes_or_text, encoding=’utf-8’)
Converts either bytes or unicode to `bytes`, using utf-8 encoding for text.
Args:
bytes_or_text: A `bytes`, `str`, or `unicode` object.
encoding: A string indicating the charset for encoding unicode.
Returns:
A `bytes` object.
Raises:
TypeError: If `bytes_or_text` is not a binary or unicode string.
as_str = as_text(bytes_or_text, encoding=’utf-8’)
Returns the given argument as a unicode string.
Args:
bytes_or_text: A `bytes`, `str`, or `unicode` object.
encoding: A string indicating the charset for decoding unicode.
Returns:
A `unicode` (Python 2) or `str` (Python 3) object.
Raises:
TypeError: If `bytes_or_text` is not a binary or unicode string.
as_str_any(value)
Converts to `str` as `str(value)`, but use `as_str` for `bytes`.
Args:
value: A object that can be converted to `str`.
Returns:
A `str` object.
as_text(bytes_or_text, encoding=’utf-8’)
Returns the given argument as a unicode string.
Args:
bytes_or_text: A `bytes`, `str`, or `unicode` object.
encoding: A string indicating the charset for decoding unicode.
Returns:
A `unicode` (Python 2) or `str` (Python 3) object.
Raises:
TypeError: If `bytes_or_text` is not a binary or unicode string.
DATA
bytes_or_text_types = (<class ‘bytes’>, <class ‘str’>)
complex_types = (<class ‘numbers.Complex’>, <class ‘numpy.number’>)
integral_types = (<class ‘numbers.Integral’>, <class ‘numpy.integer’>)
real_types = (<class ‘numbers.Real’>, <class ‘numpy.integer’>, <class …
zipfile.ZipFile.namelist(self)
Return a list of file names in the archive(档案文件).
zipfile.ZipFile.read
read(self, name, pwd=None)
Return file bytes (as a string) for name.
split()通过指定分隔符对字符串进行切片，如果参数num 有指定值，则仅分隔 num 个子字符串
str.split(str=’’,num=string.count(str))
str – 分隔符，默认为所有的空字符，包括空格、换行(\n)、制表符(\t)等。
num – 分割次数。
collections.Counter
跟踪值出现的次数，以字典形势储存，元素做key，其计数做value.
>>> c = collections.Counter(‘abcdeabcdabcaba’)
>>> c
Counter({‘a’: 5, ‘b’: 4, ‘c’: 3, ‘d’: 2, ‘e’: 1})
>>> c.most_common(3)
[(‘a’, 5), (‘b’, 4), (‘c’, 3)]
most_common (List the n most common elements) 从多到少返回一个有前n多的元素的列表（list），如果n被忽略或者为none，返回所有元素，相同数量的元素次序任意。
collections.deque
使用list存储数据时，按索引访问元素很快，但是插入和删除元素就很慢了，因为list是线性存储，数据量大的时候，插入和删除效率很低。
deque是为了高效实现插入和删除操作的双向列表，适合用于队列和栈：
append(…)
| Add an element to the right side of the deque.
|
appendleft(…)
| Add an element to the left side of the deque.
tf.nn.embedding_lookup
embedding_lookup(params, ids, partition_strategy=’mod’, name=None, validate_indices=True, max_norm=None)
Looks up `ids` in a list of embedding tensors. 查找输入ids在params嵌入向量列表中的位置
norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings),1,keep_dims=True))
axis=1
keep_dims: If true, retains reduced dimensions with length 1.
Input_tensor：被降维的张量必须其数据类型必须被预先指定。 reduction_indices：降维的维度如果为None(default)，则所有维度都要降维。 keep_dims：如果keep_dims为true，则降维的尺寸将保留为1 name：降维操作的名字。 返回一个降维后的张量。
计算L2范数
</div>
根据自己的理解写的读书笔记。
import collections import math import os import random import zipfile import urllib import numpy as np import tensorflow as tf #定义下载文本数据的函数 # url = 'http://mattmahoney.net/dc/' # # def maybe_download(filename,expected_bytes): # if not os.path.exists(filename): # filename,_ = urllib.request.urlretrieve(url + filename,filename) # statinfo = os.stat(filename) #访问一个文件的详细信息。 # if statinfo.st_size == expected_bytes: #文件大小(以字节为单位) # print('Found and verified(验证）',filename) # else: # print(statinfo.st_size) # raise Exception('Failed to verify(验证）' + filename + 'Can you get to it with a browser(浏览器)?') # return filename # # filename = maybe_download('text8.zip',31344016) filename = './text8.zip' #解压文件，并将数据转化成单词的列表 def read_data(filename): with zipfile.ZipFile(filename) as f: #获得名字列表，读取成字符串，编码成'utf-8'，最后进行分割 data = tf.compat.as_str(f.read(f.namelist()[0])).split() return data words = read_data(filename) # print('Data size',len(words)) # print(words) #创建词汇表，将出现最多的50000个单词作为词汇表，放入字典中。 vocabulary_size = 50000 def build_dataset(words): count = [['UNK',-1]] count.extend(collections.Counter(words).most_common(vocabulary_size - 1)) # c=collections.Counter(words).most_common(10) # print(c) # count.extend(c) # print(count) #[['UNK', -1], ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764), ('in', 372201), ('a', 325873), ('to', 316376), ('zero', 264975), ('nine', 250430), ('two', 192644)] dictionary = dict()#新建空字典 for word,_ in count: dictionary[word] = len(dictionary) # print(dictionary) #{'UNK': 0, 'the': 1, 'of': 2, 'and': 3, 'one': 4, 'in': 5, 'a': 6, 'to': 7, 'zero': 8, 'nine': 9, 'two': 10} data = list() unk_count = 0#未知单词数量 for word in words:#单词索引，不在字典中，则索引为0 if word in dictionary: index = dictionary[word] else: index = 0 unk_count += 1 data.append(index) count[0][1] = unk_count reverse_dictionary = dict(zip(dictionary.values(),dictionary.keys())) return data,count,dictionary,reverse_dictionary data,count,dictionary,reverse_dictionary = build_dataset(words) #删除原始单词列表，节约内存。打印词汇表，了解词频 del words # print('Most common words (+UNK)',count[:5]) # print('Sample data',data[:10],[reverse_dictionary[i] for i in data[:10]]) #以上代码为数据处理，得到单词的词频和在字典中的索引 #skip-gram模式：从目标单词反推语境 data_index = 0 #生成训练用的batch数据 #batch_size为batch大小，num_skips为对每个单词生成样本数，skip_window为单词最远可以联系的距离 def generate_batch(batch_size,num_skips,skip_window): global data_index #声明全局变量 assert batch_size % num_skips == 0#断言batch_size是num_skips的整倍数 assert num_skips <= 2 * skip_window#断言num_skips不大于skip_window的两倍 batch = np.ndarray(shape=(batch_size),dtype=np.int32)#初始化为数组 labels = np.ndarray(shape=(batch_size,1),dtype=np.int32) span = 2 * skip_window + 1 #对某个单词创建相关样本时会使用到的单词数量 buffer = collections.deque(maxlen=span) #创建最大容量为span的队列，即双向队列 for _ in range(span): buffer.append(data[data_index]) data_index = (data_index + 1) % len(data) for i in range(batch_size // num_skips):#'//'取商的整数部分 target = skip_window targets_to_avoid = [skip_window]#因为要预测语境单词，不包括目标单词本身。所以需要一个避免列表 for j in range(num_skips): while target in targets_to_avoid: target = random.randint(0, span - 1) targets_to_avoid.append(target) batch[i * num_skips + j] = buffer[skip_window] labels[i * num_skips + j, 0] = buffer[target] buffer.append(data[data_index]) data_index = (data_index + 1) % len(data) return batch,labels # batch,labels = generate_batch(batch_size=8,num_skips=2,skip_window=1) # print(batch)#[3081 3081 12 12 6 6 195 195] # print(labels)#[[5234] # # [ 12] # # [3081] # # [ 6] # # [ 12] # # [ 195] # # [ 6] # # [ 2]] # for i in range(8): # print(batch[i],reverse_dictionary[batch[i]],'->',labels[i,0],reverse_dictionary[labels[i,0]]) batch_size = 128 embedding_size = 128#将单词转为稠密向量的维度，一般在50~1000范围 skip_window = 1 num_skips = 2 valid_size = 16 valid_window = 100 valid_examples = np.random.choice(valid_window,valid_size,replace=False)#生成验证数据，随机抽取词频最高（前valid_window）的valid_size个单词 num_sampled = 64#做负样本的噪声单词数量 #定义skip-gram网络结构 graph = tf.Graph() with graph.as_default(): train_inputs = tf.placeholder(tf.int32,shape=[batch_size]) train_labels = tf.placeholder(tf.int32,shape=[batch_size,1]) valid_dataset = tf.constant(valid_examples,dtype=tf.int32) #限定所有计算都在cpu上执行，因为接下来一些计算操作在GPU上可能还没有实现 with tf.device('/cpu:0'): embeddings = tf.Variable(tf.random_uniform([vocabulary_size,embedding_size],-1.0,1.0))#随机生成所有单词的词向量，单词表大小50000，维度128 embed = tf.nn.embedding_lookup(embeddings,train_inputs)#查找输入train_inputs在embeddings里对应的向量 #用截断正态分布truncated_normal初始化NCE Loss中的权重参数nce_weights，并将其初始化为0 nce_weights = tf.Variable(tf.truncated_normal([vocabulary_size,embedding_size],stddev=1.0/math.sqrt(embedding_size))) nce_biases = tf.Variable(tf.zeros([vocabulary_size])) loss = tf.reduce_mean(tf.nn.nce_loss(weights=nce_weights,biases=nce_biases,labels=train_labels,inputs=embed,num_sampled=num_sampled,num_classes=vocabulary_size)) #优化器SGD，学习率1.0 optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss) #先计算embeddings的平方，并按第二维降维到1，计算嵌入向量embeddings的L2范数 norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings),1,keep_dims=True)) #标准化embeddings normalized_embeddings = embeddings/norm #查询单词的嵌入向量，并计算验证单词的嵌入向量与词汇表中所有单词的相似性 valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings,valid_dataset) #transpose_b=True 将b转置 similarity = tf.matmul(valid_embeddings,normalized_embeddings,transpose_b=True) #初始化所有模型参数 init = tf.global_variables_initializer() num_steps = 100001#迭代10万次 with tf.Session(graph=graph) as session: init.run() print('Initialized') average_loss = 0 for step in range(num_steps): batch_inputs,batch_labels = generate_batch(batch_size,num_skips,skip_window) feed_dict = {train_inputs : batch_inputs,train_labels : batch_labels} _,loss_val = session.run([optimizer,loss],feed_dict=feed_dict) average_loss += loss_val if step % 2000 == 0: if step > 0: average_loss /= 2000 print('Average loss at step ',step,': ',average_loss) average_loss = 0 if step % 10000 == 0: sim = similarity.eval() for i in range(valid_size): valid_word = reverse_dictionary[valid_examples[i]] top_k = 8 nearest = (-sim[i, :]).argsort()[1:top_k+1]#argsort将数组从小到大排列，并返回索引 log_str = 'Nearest to %s:' % valid_word for k in range(top_k): close_word = reverse_dictionary[nearest[k]] log_str = '%s %s,' % (log_str,close_word) print(log_str) final_embeddings = normalized_embeddings.eval() from sklearn.manifold import TSNE#此降维算法比PCA更高级，可视化 import matplotlib.pyplot as plt def plot_with_labels(low_dim_embs,labels,filename='tsne.png'): assert low_dim_embs.shape[0] >= len(labels),'More labels than embeddings' plt.figure(figsize=(18,18)) for i,label in enumerate(labels):#enumerate枚举可遍历、迭代（列表、字符串）对象，加上索引 x,y = low_dim_embs[i,:] plt.scatter(x,y)#显示散点图 #（工具书p242）annotate在图上添加注释，xy设置箭头所指处的坐标，xytext注释内容坐标，textcoords注释内容坐标的坐标变换方式。 #'offset points'以点为单位，相对于点xy的坐标 # ha='right'点在注释右边（right,center,left），va='bottom'点在注释底部('top', 'bottom', 'center', 'baseline') plt.annotate(label,xy=(x,y),xytext=(5,2),textcoords='offset points',ha='right',va='bottom') plt.savefig(filename) #perplexity（混乱，复杂）与最近邻数有关，一般在5~50，n_iter达到最优化所需的最大迭代次数，应当不少于250次 #init='pca'pca初始化比random稳定，n_components嵌入空间的维数（即降到2维，默认为2 tsne = TSNE(perplexity=30,n_components=2,init='pca',n_iter=5000) plot_only = 100#显示词频最高的一百个 low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only,:]) labels = [reverse_dictionary[i] for i in range(plot_only)] plot_with_labels(low_dim_embs,labels) # plt.show()
笔记
tf.compat(compat兼容性)
NAME
tensorflow.python.util.compat - Functions for Python 2 vs. 3 compatibility.
DESCRIPTION
## Conversion routines
In addition to the functions below, `as_str` converts an object to a `str`.
@@as_bytes
@@as_text
@@as_str_any
## Types
The compatibility module also provides the following types:
* `bytes_or_text_types`
* `complex_types`
* `integral_types`
* `real_types`
FUNCTIONS
as_bytes(bytes_or_text, encoding=’utf-8’)
Converts either bytes or unicode to `bytes`, using utf-8 encoding for text.
Args:
bytes_or_text: A `bytes`, `str`, or `unicode` object.
encoding: A string indicating the charset for encoding unicode.
Returns:
A `bytes` object.
Raises:
TypeError: If `bytes_or_text` is not a binary or unicode string.
as_str = as_text(bytes_or_text, encoding=’utf-8’)
Returns the given argument as a unicode string.
Args:
bytes_or_text: A `bytes`, `str`, or `unicode` object.
encoding: A string indicating the charset for decoding unicode.
Returns:
A `unicode` (Python 2) or `str` (Python 3) object.
Raises:
TypeError: If `bytes_or_text` is not a binary or unicode string.
as_str_any(value)
Converts to `str` as `str(value)`, but use `as_str` for `bytes`.
Args:
value: A object that can be converted to `str`.
Returns:
A `str` object.
as_text(bytes_or_text, encoding=’utf-8’)
Returns the given argument as a unicode string.
Args:
bytes_or_text: A `bytes`, `str`, or `unicode` object.
encoding: A string indicating the charset for decoding unicode.
Returns:
A `unicode` (Python 2) or `str` (Python 3) object.
Raises:
TypeError: If `bytes_or_text` is not a binary or unicode string.
DATA
bytes_or_text_types = (<class ‘bytes’>, <class ‘str’>)
complex_types = (<class ‘numbers.Complex’>, <class ‘numpy.number’>)
integral_types = (<class ‘numbers.Integral’>, <class ‘numpy.integer’>)
real_types = (<class ‘numbers.Real’>, <class ‘numpy.integer’>, <class …
zipfile.ZipFile.namelist(self)
Return a list of file names in the archive(档案文件).
zipfile.ZipFile.read
read(self, name, pwd=None)
Return file bytes (as a string) for name.
split()通过指定分隔符对字符串进行切片，如果参数num 有指定值，则仅分隔 num 个子字符串
str.split(str=’’,num=string.count(str))
str – 分隔符，默认为所有的空字符，包括空格、换行(\n)、制表符(\t)等。
num – 分割次数。
collections.Counter
跟踪值出现的次数，以字典形势储存，元素做key，其计数做value.
>>> c = collections.Counter(‘abcdeabcdabcaba’)
>>> c
Counter({‘a’: 5, ‘b’: 4, ‘c’: 3, ‘d’: 2, ‘e’: 1})
>>> c.most_common(3)
[(‘a’, 5), (‘b’, 4), (‘c’, 3)]
most_common (List the n most common elements) 从多到少返回一个有前n多的元素的列表（list），如果n被忽略或者为none，返回所有元素，相同数量的元素次序任意。
collections.deque
使用list存储数据时，按索引访问元素很快，但是插入和删除元素就很慢了，因为list是线性存储，数据量大的时候，插入和删除效率很低。
deque是为了高效实现插入和删除操作的双向列表，适合用于队列和栈：
append(…)
| Add an element to the right side of the deque.
|
appendleft(…)
| Add an element to the left side of the deque.
tf.nn.embedding_lookup
embedding_lookup(params, ids, partition_strategy=’mod’, name=None, validate_indices=True, max_norm=None)
Looks up `ids` in a list of embedding tensors. 查找输入ids在params嵌入向量列表中的位置
norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings),1,keep_dims=True))
axis=1
keep_dims: If true, retains reduced dimensions with length 1.
Input_tensor：被降维的张量必须其数据类型必须被预先指定。 reduction_indices：降维的维度如果为None(default)，则所有维度都要降维。 keep_dims：如果keep_dims为true，则降维的尺寸将保留为1 name：降维操作的名字。 返回一个降维后的张量。
计算L2范数
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