p9_twoCNNTextRelation_model.py 文件源码

def inference(self):
        """main computation graph here: 1. embeddding layers, 2.convolutional layer, 3.max-pooling, 4.softmax layer."""
        # 1.=====>get emebedding of words in the sentence
        self.embedded_words1 = tf.nn.embedding_lookup(self.Embedding,self.input_x)#[None,sentence_length,embed_size]
        self.sentence_embeddings_expanded1=tf.expand_dims(self.embedded_words1,-1) #[None,sentence_length,embed_size,1). expand dimension so meet input requirement of 2d-conv
        self.embedded_words2 = tf.nn.embedding_lookup(self.Embedding,self.input_x2)#[None,sentence_length,embed_size]
        self.sentence_embeddings_expanded2=tf.expand_dims(self.embedded_words2,-1) #[None,sentence_length,embed_size,1). expand dimension so meet input requirement of 2d-conv
        #2.1 get features of sentence1
        h1=self.conv_relu_pool_dropout(self.sentence_embeddings_expanded1,name_scope_prefix="s1") #[None,num_filters_total]
        #2.2 get features of sentence2
        h2 =self.conv_relu_pool_dropout(self.sentence_embeddings_expanded2,name_scope_prefix="s2")  # [None,num_filters_total]
        #3. concat features
        h=tf.concat([h1,h2],axis=1) #[None,num_filters_total*2]
        #4. logits(use linear layer)and predictions(argmax)
        with tf.name_scope("output"):
            logits = tf.matmul(h,self.W_projection) + self.b_projection  #shape:[None, self.num_classes]==tf.matmul([None,self.num_filters_total*2],[self.num_filters_total*2,self.num_classes])
        return logits