最近在做問答系統，用CNN深度學習模型分別對問題、正答案和負答案提取特徵，得到各自的特徵向量。正答案與問題之間的距離比負答案與問題之間的距離要近，距離用夾角的cos值來表示，目標函式是由此確定的。模型出自《Applying Deep Learning To Answer Selection: A Study And An Open Task》，如圖1所示。

圖 1
用tensorflow架構實現該模型，然而做模型優化時，5步以內模型的loss就為0了，精度達到了1，如圖2所示。‘

圖 2
迷糊了一個星期，才找到問題點。
原來是因為在用CNN做特徵提取時，我對三個輸入（問題、正答案、負答案）分別做了模型weight初始化，相當於建立了三個CNN分別對問題、正答案、負答案做特徵抽取。程式碼如下，呼叫了conv函式三遍，初始化了W三次，建立了三個CNN模型。

# define a convolution function 
    def conv(input_data):
        pooled_outputs = []
        for i, filter_size in enumerate(filter_sizes):
                with tf.name_scope("conv-maxpool-%s" % filter_size):
                    # Convolution Layer         
            filter_shape = [filter_size, embedding_size, 1
 
, num_filters]
                W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
                b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
                conv = tf.nn.conv2d(input_data, W, strides=[1, 1, 1, 1],
                padding="VALID", name="conv"
 
)
            h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")    
                pooled = tf.nn.max_pool(
                h,
                        ksize=[1, sequence_length - filter_size + 1, 1, 1],
                        strides=[1, 1, 1, 1],
                        padding='VALID',
                        name="pool")    # shape of pooled is [batch_size,1,1,num_filters]
                pooled_outputs.append(pooled)
        return pooled_outputs
    pooled_outputs1=conv(self.embedded_chars1_expanded) # conv-pool outputs
    pooled_outputs2=conv(self.embedded_chars2_expanded)
    pooled_outputs3=conv(self.embedded_chars3_expanded) 

為什麼用三個CNN模型做QA系統的答案選擇不行呢？此時的模型相當於如下圖3的結構。

圖 3
三個CNN模型分別做特徵提取，導致模型對負答案失去約束力，也就是說負答案的卷積神經網路模型使它始終離問題很遠（夾角的cos值小），即使此負答案CNN模型的輸入是正答案，距離仍然會遠（夾角仍然會小）。打個形象的比喻，就像是讓負答案乘以0，無論輸入怎麼變，輸出都是零，這樣的約束沒有了意義。
對程式碼進行如下修改。

pooled_outputs1 = []
        pooled_outputs2 = []
        pooled_outputs3 = []
        for i, filter_size in enumerate(filter_sizes):
            with tf.name_scope("conv-maxpool-%s" % filter_size):
                filter_shape = [filter_size, embedding_size, 1, num_filters]
                W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
                b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
                conv = tf.nn.conv2d(self.embedded_chars1_expanded, W, strides=[1, 1, 1, 1],
                                    padding="VALID", name="conv")
                h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
                pooled = tf.nn.max_pool(
                                h,
                                ksize=[1, sequence_length - filter_size + 1, 1, 1],
                                strides=[1, 1, 1, 1],
                                padding='VALID',
                                name="pool")  # shape of pooled is [batch_size,1,1,num_filters]
                pooled_outputs1.append(pooled)
                conv = tf.nn.conv2d(self.embedded_chars2_expanded, W, strides=[1, 1, 1, 1],
                                    padding="VALID", name="conv")
                h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
                        pooled = tf.nn.max_pool(
                                h,
                                ksize=[1, sequence_length - filter_size + 1, 1, 1],
                                strides=[1, 1, 1, 1],
                                padding='VALID',
                                name="pool")  # shape of pooled is [batch_size,1,1,num_filters]
                        pooled_outputs2.append(pooled)
                        conv = tf.nn.conv2d(self.embedded_chars3_expanded, W, strides=[1, 1, 1, 1],
                                    padding="VALID", name="conv")
        # print('\n--- shape of cov is {}'.format(conv.get_shape()))
        # Apply nonlinearity
                        h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
        # Max-pooling over the outputs
                        pooled = tf.nn.max_pool(
                                h,
                                ksize=[1, sequence_length - filter_size + 1, 1, 1],
                                strides=[1, 1, 1, 1],
                                padding='VALID',
                                name="pool")  # shape of pooled is [batch_size,1,1,num_filters]
                        pooled_outputs3.append(pooled)

改過的程式可以正常運行了，準確率和論文描述的相當。