寫在前面

• 態度決定高度！讓優秀成為一種習慣！
• 世界上沒有什么事兒是加一次班解決不了的，如果有，就加兩次?。? - -茂強）

TF-IDF

• 先看公式

TF-IDF

這個公式并不是一個很好的公式，一版的都用經過平滑的公司，避免分母為0的情況
本文采用的是sklearn的默認公司

  tf-idf(d, t) = tf(t) * idf(d, t)
  idf(d, t) = log [ n / (df(d, t) + 1) ])
  # t就是詞

• 數據準備
  從文檔中讀取數據
  讀取后的數據如下：

文本數據

標簽數據

• 聲明依賴以及靜態參數
  import tensorflow as tf
  import matplotlib.pyplot as plt
  import re
  import numpy as np
  from sklearn.feature_extraction.text import TfidfVectorizer
  sess = tf.Session()
  batch_size= 200
  max_featurtes = 10000

• 利用sklearn.feature_extraction.text中的TfidfVectorizer對文本進行向量化
  def tokenizer(text):
  words = text.split(" ")
  return words
  stop_words = set()
  tfidf = TfidfVectorizer(tokenizer=tokenizer,stop_words=stop_words,max_features=max_featurtes)
  sparse_tfidf_texts = tfidf.fit_transform(texts)

• 把數據分成訓練集和測試集
  train_indices = np.random.choice(sparse_tfidf_texts.shape[0],round(0.8*sparse_tfidf_texts.shape[0]), replace=False)
  test_indices = np.array(list(set(range(sparse_tfidf_texts.shape[0])) -set(train_indices)))
  texts_train = sparse_tfidf_texts[train_indices]
  texts_test = sparse_tfidf_texts[test_indices]
  target_train = np.array([x for ix, x in enumerate(target) if ix in train_indices])
  target_test = np.array([x for ix, x in enumerate(target) if ix in test_indices])

• 定義邏輯回歸模型的變量和placeholder
  A = tf.Variable(tf.random_normal(shape=[max_featurtes,1]))
  b = tf.Variable(tf.random_normal(shape=[1,1]))
  # Initialize placeholders
  x_data = tf.placeholder(shape=[None, max_featurtes], dtype=tf. float32)
  y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)

• 定義模型和損失函數
  model_output = tf.add(tf.matmul(x_data, A), b)
  loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=model_output, logits=y_target))

• 定義預測方程和精確度計算
  prediction = tf.round(tf.sigmoid(model_output))
  predictions_correct = tf.cast(tf.equal(prediction, y_target),tf.float32)
  accuracy = tf.reduce_mean(predictions_correct)

• 定義優化算法以及初始化變量
  my_opt = tf.train.GradientDescentOptimizer(0.05)
  train_step = my_opt.minimize(loss)
  # Intitialize Variables
  init = tf.initialize_all_variables()
  sess.run(init)

• 開始訓練模型
  train_loss = []
  test_loss = []
  train_acc = []
  test_acc = []
  i_data = []
  for i in range(10000):
  rand_index = np.random.choice(texts_train.shape[0],size=batch_size)
  rand_x = texts_train[rand_index].todense()
  rand_y = np.transpose([target_train[rand_index]])
  sess.run(train_step, feed_dict={x_data: rand_x, y_target:rand_y})
  # Only record loss and accuracy every 100 generations
  if (i+1)%100==0:
  i_data.append(i+1)
  train_loss_temp = sess.run(loss, feed_dict={x_data:rand_x, y_target: rand_y})
  train_loss.append(train_loss_temp)
  test_loss_temp = sess.run(loss, feed_dict={x_data: texts_test.todense(), y_target: np.transpose([target_test])})
  test_loss.append(test_loss_temp)
  train_acc_temp = sess.run(accuracy, feed_dict={x_data:rand_x, y_target: rand_y})
  train_acc.append(train_acc_temp)
  test_acc_temp = sess.run(accuracy, feed_dict={x_data:texts_test.todense(), y_target: np.transpose([target_test])})
  test_acc.append(test_acc_temp)
  if (i+1)%500==0:
  acc_and_loss = [i+1, train_loss_temp, test_loss_temp,train_acc_temp, test_acc_temp]
  acc_and_loss = [np.round(x,2) for x in acc_and_loss]
  print('Generation # {}. Train Loss (Test Loss): {:.2f}({:.2f}). Train Acc (Test Acc): {:.2f} ({:.2f})'.format(*acc_and_loss))
  其中每個批次喂給模型的數據如下圖

  
  
  rand_x

rand_y

• 最后就是畫出訓練時的損失函數的計算結果圖和精確度
  這里沒有去調整參數，請讀者自行調整參數進行訓練，以達到更好的效果

  plt.figure(1) # 創建圖表1
  x = [i for i in range(0, len(train_loss))]
  plt.plot(x, train_loss,"b-*")
  plt.plot(x, test_loss,"r-+")
  plt.figure(2) # 創建圖表2
  x = [i for i in range(0, len(train_acc))]
  plt.plot(x, train_acc,"b-*")
  plt.plot(x, test_acc,"r-+")
  plt.show()
  

loss

acc