from __future__ import print_function #表示不管哪個python版本，使用最新的print語法  import collections  import math  import numpy as np  import random  import tensorflow as tf  import zipfile  from matplotlib import pylab
from sklearn.manifold import TSNE  %matplotlib inline

filename='text8.zip'  def read_data(filename):    """Extract the first file enclosed in a zip file as a list of words"""    with zipfile.ZipFile(filename) as f:  #     裡面只有一個文件text8，包含了多個單詞  #     f.read返回位元組，tf.compat.as_str將位元組轉為字元  #     data包含了所有單詞      data = tf.compat.as_str(f.read(f.namelist()[0])).split()    return data    #words裡面包含了所有的單詞  words = read_data(filename)  print('Data size %d' % len(words))

#辭彙表大小  vocabulary_size = 50000    def build_dataset(words):  #     表示未知，即不在辭彙表裡的單詞，注意這裡用的是列表形式而非元組形式，因為後面未知的數量需要賦值    count = [['UNK', -1]]    count.extend(collections.Counter(words).most_common(vocabulary_size - 1))      #詞-索引哈希    dictionary = dict()    for word, _ in count:  #     每增加一個-->len+1，索引從0開始      dictionary[word] = len(dictionary)      #用索引表示的整個text8文本    data = list()    unk_count = 0    for word in words:      if word in dictionary:        index = dictionary[word]      else:        index = 0  # dictionary['UNK']        unk_count = unk_count + 1      data.append(index)      count[0][1] = unk_count    # 索引-詞哈希      reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))    return data, count, dictionary, reverse_dictionary    data, count, dictionary, reverse_dictionary = build_dataset(words)  print('Most common words (+UNK)', count[:5])  print('Sample data', data[:10])  # 刪除，減少記憶體  del words  # Hint to reduce memory.

data_index = 0    # num_skips表示在兩側窗口內總共取多少個詞，數量可以小於2*skip_window  # span窗口為[ skip_window target skip_window ]  # num_skips=2*skip_window  def generate_batch(batch_size, num_skips, skip_window):    global data_index      #這裡兩個斷言    assert batch_size % num_skips == 0    assert num_skips <= 2 * skip_window      #初始化batch和labels，都是整形    batch = np.ndarray(shape=(batch_size), dtype=np.int32)    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) #注意labels的形狀      span = 2 * skip_window + 1 # [ skip_window target skip_window ]    #buffer這個隊列太有用了，不斷地保存span個單詞在裡面，然後不斷往後滑動，而且buffer[skip_window]就是中心詞    buffer = collections.deque(maxlen=span)      for _ in range(span):      buffer.append(data[data_index])      data_index = (data_index + 1) % len(data)      #需要多少個中心詞，因為一個target對應num_skips個的單詞，即一個目標單詞w在num_skips=2時形成2個樣本(w,left_w),(w,right_w)  #     這樣描述了目標單詞w的上下文    center_words_count=batch_size // num_skips    for i in range(center_words_count):      #skip_window在buffer里正好是中心詞所在位置      target = skip_window  # target label at the center of the buffer      targets_to_avoid = [ skip_window ]      for j in range(num_skips):    #     選取span窗口中不包含target的，且不包含已選過的        target=random.choice([i for i in range(0,span) if i not in targets_to_avoid])        targets_to_avoid.append(target)  #         batch中重複num_skips次        batch[i * num_skips + j] = buffer[skip_window]  #         同一個target對應num_skips個上下文單詞        labels[i * num_skips + j, 0] = buffer[target]  #     buffer滑動一格      buffer.append(data[data_index])      data_index = (data_index + 1) % len(data)    return batch, labels    # 列印前8個單詞  print('data:', [reverse_dictionary[di] for di in data[:10]])  for num_skips, skip_window in [(2, 1), (4, 2)]:      data_index = 0      batch, labels = generate_batch(batch_size=16, num_skips=num_skips, skip_window=skip_window)      print('nwith num_skips = %d and skip_window = %d:' % (num_skips, skip_window))      print('    batch:', [reverse_dictionary[bi] for bi in batch])      print('    labels:', [reverse_dictionary[li] for li in labels.reshape(16)])

data: ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against']    with num_skips = 2 and skip_window = 1:      batch: ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term', 'of', 'of', 'abuse', 'abuse', 'first', 'first', 'used', 'used']      labels: ['as', 'anarchism', 'originated', 'a', 'term', 'as', 'of', 'a', 'term', 'abuse', 'of', 'first', 'abuse', 'used', 'against', 'first']    with num_skips = 4 and skip_window = 2:      batch: ['as', 'as', 'as', 'as', 'a', 'a', 'a', 'a', 'term', 'term', 'term', 'term', 'of', 'of', 'of', 'of']      labels: ['anarchism', 'originated', 'a', 'term', 'originated', 'of', 'as', 'term', 'of', 'a', 'abuse', 'as', 'a', 'term', 'first', 'abuse']

batch_size = 128  embedding_size = 128 # Dimension of the embedding vector.  skip_window = 1 # How many words to consider left and right.  num_skips = 2 # How many times to reuse an input to generate a label.    valid_size = 16 # Random set of words to evaluate similarity on.  valid_window = 100 # Only pick dev samples in the head of the distribution.  #隨機挑選一組單詞作為驗證集，valid_examples也就是下面的valid_dataset，是一個一維的ndarray  valid_examples = np.array(random.sample(range(valid_window), valid_size))    #trick：負取樣數值  num_sampled = 64 # Number of negative examples to sample.    graph = tf.Graph()    with graph.as_default(), tf.device('/cpu:0'):      # 訓練集和標籤，以及驗證集（注意驗證集是一個常量集合）    train_dataset = 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)      # 定義Embedding層，初始化。    embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))    softmax_weights = tf.Variable(      tf.truncated_normal([vocabulary_size, embedding_size],stddev=1.0 / math.sqrt(embedding_size)))    softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))      # Model.    # train_dataset通過embeddings變為稠密向量，train_dataset是一個一維的ndarray    embed = tf.nn.embedding_lookup(embeddings, train_dataset)      # Compute the softmax loss, using a sample of the negative labels each time.    # 計算損失，tf.reduce_mean和tf.nn.sampled_softmax_loss    loss = tf.reduce_mean(tf.nn.sampled_softmax_loss(weights=softmax_weights, biases=softmax_biases, inputs=embed,                                 labels=train_labels, num_sampled=num_sampled, num_classes=vocabulary_size))      # Optimizer.優化器，這裡也會優化embeddings    # Note: The optimizer will optimize the softmax_weights AND the embeddings.    # This is because the embeddings are defined as a variable quantity and the    # optimizer's `minimize` method will by default modify all variable quantities     # that contribute to the tensor it is passed.    # See docs on `tf.train.Optimizer.minimize()` for more details.    optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)      # 模型其實到這裡就結束了，下面是在驗證集上做效果驗證    # Compute the similarity between minibatch examples and all embeddings.    # We use the cosine distance:先對embeddings做正則化    norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))    normalized_embeddings = embeddings / norm    valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)    #驗證集單詞與其他所有單詞的相似度計算    similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))

num_steps = 40001  with tf.Session(graph=graph) as session:    tf.initialize_all_variables().run()    print('Initialized')    average_loss = 0    for step in range(num_steps):      batch_data, batch_labels = generate_batch(batch_size, num_skips, skip_window)      feed_dict = {train_dataset : batch_data, train_labels : batch_labels}      _, this_loss = session.run([optimizer, loss], feed_dict=feed_dict)        average_loss += this_loss  #     每2000步計算一次平均loss      if step % 2000 == 0:        if step > 0:          average_loss = average_loss / 2000        # The average loss is an estimate of the loss over the last 2000 batches.        print('Average loss at step %d: %f' % (step, average_loss))        average_loss = 0        # note that this is expensive (~20% slowdown if computed every 500 steps)      if step % 10000 == 0:        sim = similarity.eval()        for i in range(valid_size):          valid_word = reverse_dictionary[valid_examples[i]]          top_k = 8 # number of nearest neighbors  #         nearest = (-sim[i, :]).argsort()[1:top_k+1]          nearest = (-sim[i, :]).argsort()[0:top_k+1]#包含自己試試          log = 'Nearest to %s:' % valid_word          for k in range(top_k):            close_word = reverse_dictionary[nearest[k]]            log = '%s %s,' % (log, close_word)          print(log)    #一直到訓練結束，再對所有embeddings做一次正則化，得到最後的embedding    final_embeddings = normalized_embeddings.eval()

Nearest to many: many, originator, jeddah, maxwell, laurent, distress, interpret, bucharest,

Nearest to many: many, some, several, jeddah, originator, neurath, distress, songs,

Nearest to many: many, some, several, these, various, such, other, most,

num_points = 400  # 降維度PCA  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)  two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :])  def plot(embeddings, labels):    assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'    pylab.figure(figsize=(15,15))  # in inches    for i, label in enumerate(labels):      x, y = embeddings[i,:]      pylab.scatter(x, y)      pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',                     ha='right', va='bottom')    pylab.show()    words = [reverse_dictionary[i] for i in range(1, num_points+1)]  plot(two_d_embeddings, words)