最近閱讀了有關文本分類的文章，其中有一篇名為《Adversarail Training for Semi-supervised Text Classification》, 其主要思路實在文本訓練時增加了一個擾動因子，即在embedding層加入一個小的擾動，發現訓練的結果比不加要好很多。
模型的網絡結構如下圖：
下面就介紹一下這個對抗因子r的生成過程：
在進入lstm網絡前先進行從w到v的計算，即將wordembedding 歸一化:
然后定義模型的損失函數，令輸入為x，參數為θ，Radv為對抗訓練因子，損失函數為：
其中一個細節，雖然θˆ 是θ的復制，但是它是計算擾動的過程，不會參與到計算梯度的反向傳播算法中。
然后就是求擾動：
先對表達式求導得到倒數g，然后對倒數g進行l2正則化的線性變換。
至此擾動則計算完成然后加入之前的wordembedding中參與模型訓練。
下面則是模型的代碼部分：
#構建adversarailLSTM模型
class AdversarailLSTM(object):
def __init__(self, config, wordEmbedding, indexFreqs):
#定義輸入
self.inputX = tf.placeholder(tf.int32, [None, config.sequenceLength], name="inputX")
self.inputY = tf.placeholder(tf.float32, [None, 1], name="inputY")
self.dropoutKeepProb = tf.placeholder(tf.float32, name="dropoutKeepProb")
#根據詞頻計算權重
indexFreqs[0], indexFreqs[1] = 20000, 10000
weights = tf.cast(tf.reshape(indexFreqs / tf.reduce_sum(indexFreqs), [1, len(indexFreqs)]), dtype=tf.float32)
#詞嵌入層
with tf.name_scope("wordEmbedding"):
#利用預訓練的詞向量初始化詞嵌入矩陣
normWordEmbedding = self._normalize(tf.cast(wordEmbedding, dtype=tf.float32, name="word2vec"), weights)
#self.W = tf.Variable(tf.cast(wordEmbedding, dtype=tf.float32, name="word2vec"), name="W")
self.embeddedWords = tf.nn.embedding_lookup(normWordEmbedding, self.inputX)
#計算二元交叉熵損失
with tf.name_scope("loss"):
with tf.variable_scope("Bi-LSTM", reuse=None):
self.predictions = self._Bi_LSTMAttention(self.embeddedWords)
self.binaryPreds = tf.cast(tf.greater_equal(self.predictions, 0.5), tf.float32, name="binaryPreds")
losses = tf.nn.sigmoid_cross_entropy_with_logits(logits=self.predictions, labels=self.inputY)
loss = tf.reduce_mean(losses)
with tf.name_scope("perturloss"):
with tf.variable_scope("Bi-LSTM", reuse=True):
perturWordEmbedding = self._addPerturbation(self.embeddedWords, loss)
print("perturbSize:{}".format(perturWordEmbedding))
perturPredictions = self._Bi_LSTMAttention(perturWordEmbedding)
perturLosses = tf.nn.sigmoid_cross_entropy_with_logits(logits=perturPredictions, labels=self.inputY)
perturLoss = tf.reduce_mean(perturLosses)
self.loss = loss + perturLoss
def _Bi_LSTMAttention(self, embeddedWords):
#定義兩層雙向LSTM的模型結構
with tf.name_scope("Bi-LSTM"):
fwHiddenLayers = []
bwHiddenLayers = []
for idx, hiddenSize in enumerate(config.model.hiddenSizes):
with tf.name_scope("Bi-LSTM" + str(idx)):
#定義前向網絡結構
lstmFwCell = tf.nn.rnn_cell.DropoutWrapper(tf.nn.rnn_cell.LSTMCell(num_units=hiddenSize, state_is_tuple=True),
output_keep_prob=self.dropoutKeepProb)
#定義反向網絡結構
lstmBwCell = tf.nn.rnn_cell.DropoutWrapper(tf.nn.rnn_cell.LSTMCell(num_units=hiddenSize, state_is_tuple=True),
output_keep_prob=self.dropoutKeepProb)
fwHiddenLayers.append(lstmFwCell)
bwHiddenLayers.append(lstmBwCell)
# 實現多層的LSTM結構， state_is_tuple=True，則狀態會以元祖的形式組合(h, c)，否則列向拼接
fwMultiLstm = tf.nn.rnn_cell.MultiRNNCell(cells=fwHiddenLayers, state_is_tuple=True)
bwMultiLstm = tf.nn.rnn_cell.MultiRNNCell(cells=bwHiddenLayers, state_is_tuple=True)
#采用動態rnn，可以動態地輸入序列的長度，若沒有輸入，則取序列的全長
#outputs是一個元組(output_fw, output_bw), 其中兩個元素的維度都是[batch_size, max_time, hidden_size], fw和bw的hiddensize一樣
#self.current_state是最終的狀態，二元組(state_fw, state_bw), state_fw=[batch_size, s], s是一個元組(h, c)
outputs, self.current_state = tf.nn.bidirectional_dynamic_rnn(fwMultiLstm, bwMultiLstm,
self.embeddedWords, dtype=tf.float32,
scope="bi-lstm" + str(idx))
#在bi-lstm+attention論文中，將前向和后向的輸出相加
with tf.name_scope("Attention"):
H = outputs[0] + outputs[1]
#得到attention的輸出
output = self.attention(H)
outputSize = config.model.hiddenSizes[-1]
print("outputSize:{}".format(outputSize))
#全連接層的輸出
with tf.name_scope("output"):
outputW = tf.get_variable(
"outputW",
shape=[outputSize, 1],
initializer=tf.contrib.layers.xavier_initializer())
outputB = tf.Variable(tf.constant(0.1, shape=[1]), name="outputB")
predictions = tf.nn.xw_plus_b(output, outputW, outputB, name="predictions")
return predictions
def attention(self, H):
"""
利用Attention機制得到句子的向量表示
"""
#獲得最后一層lstm神經元的數量
hiddenSize = config.model.hiddenSizes[-1]
#初始化一個權重向量，是可訓練的參數
W = tf.Variable(tf.random_normal([hiddenSize], stddev=0.1))
#對bi-lstm的輸出用激活函數做非線性轉換
M = tf.tanh(H)
#對W和M做矩陣運算，W=[batch_size, time_step, hidden_size], 計算前做維度轉換成[batch_size * time_step, hidden_size]
#newM = [batch_size, time_step, 1], 每一個時間步的輸出由向量轉換成一個數字
newM = tf.matmul(tf.reshape(M, [-1, hiddenSize]), tf.reshape(W, [-1, 1]))
#對newM做維度轉換成[batch_size, time_step]
restoreM = tf.reshape(newM, [-1, config.sequenceLength])
#用softmax做歸一化處理[batch_size, time_step]
self.alpha = tf.nn.softmax(restoreM)
#利用求得的alpha的值對H進行加權求和，用矩陣運算直接操作
r = tf.matmul(tf.transpose(H, [0, 2, 1]), tf.reshape(self.alpha, [-1, config.sequenceLength, 1]))
#將三維壓縮成二維sequeezeR = [batch_size, hissen_size]
sequeezeR = tf.squeeze(r)
sentenceRepren = tf.tanh(sequeezeR)
#對attention的輸出可以做dropout處理
output = tf.nn.dropout(sentenceRepren, self.dropoutKeepProb)
return output
def _normalize(self, wordEmbedding, weights):
"""
對word embedding 結合權重做標准化處理
"""
mean = tf.matmul(weights, wordEmbedding)
powWordEmbedding = tf.pow(wordEmbedding -mean, 2.)
var = tf.matmul(weights, powWordEmbedding)
stddev = tf.sqrt(1e-6 + var)
return (wordEmbedding - mean) / stddev
def _addPerturbation(self, embedded, loss):
"""
添加波動到word embedding
"""
grad, =tf.gradients(
loss,
embedded,
aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)
grad = tf.stop_gradient(grad)
perturb = self._scaleL2(grad, config.model.epsilon)
#print("perturbSize:{}".format(embedded+perturb))
return embedded + perturb
def _scaleL2(self, x, norm_length):
#shape(x) = [batch, num_step, d]
#divide x by max(abs(x)) for a numerically stable L2 norm
#2norm(x) = a * 2norm(x/a)
#scale over the full sequence, dim(1, 2)
alpha = tf.reduce_max(tf.abs(x), (1, 2), keep_dims=True) + 1e-12
l2_norm = alpha * tf.sqrt(tf.reduce_sum(tf.pow(x/alpha, 2), (1, 2), keep_dims=True) + 1e-6)
x_unit = x / l2_norm
return norm_length * x_unit
代碼是在雙向lstm+attention的基礎上增加adversarial training，訓練數據為imdb電影評論數據，最后的結果發現確實很快就能達到最優值，但是訓練所占的空間比較大（電腦跑了幾十步就停止了），每一步的時間也稍微長一點。