matchpyramid.py 文件源码

def build(self):
        query = Input(name='query', shape=(self.config['text1_maxlen'],))
        show_layer_info('Input', query)
        doc = Input(name='doc', shape=(self.config['text2_maxlen'],))
        show_layer_info('Input', doc)
        dpool_index = Input(name='dpool_index', shape=[self.config['text1_maxlen'], self.config['text2_maxlen'], 3], dtype='int32')
        show_layer_info('Input', dpool_index)

        embedding = Embedding(self.config['vocab_size'], self.config['embed_size'], weights=[self.config['embed']], trainable = self.embed_trainable)
        q_embed = embedding(query)
        show_layer_info('Embedding', q_embed)
        d_embed = embedding(doc)
        show_layer_info('Embedding', d_embed)

        cross = Dot(axes=[2, 2], normalize=False)([q_embed, d_embed])
        show_layer_info('Dot', cross)
        cross_reshape = Reshape((self.config['text1_maxlen'], self.config['text2_maxlen'], 1))(cross)
        show_layer_info('Reshape', cross_reshape)

        conv2d = Conv2D(self.config['kernel_count'], self.config['kernel_size'], padding='same', activation='relu')
        dpool = DynamicMaxPooling(self.config['dpool_size'][0], self.config['dpool_size'][1])

        conv1 = conv2d(cross_reshape)
        show_layer_info('Conv2D', conv1)
        pool1 = dpool([conv1, dpool_index])
        show_layer_info('DynamicMaxPooling', pool1)
        pool1_flat = Flatten()(pool1)
        show_layer_info('Flatten', pool1_flat)
        pool1_flat_drop = Dropout(rate=self.config['dropout_rate'])(pool1_flat)
        show_layer_info('Dropout', pool1_flat_drop)
        if self.config['target_mode'] == 'classification':
            out_ = Dense(2, activation='softmax')(pool1_flat_drop)
        elif self.config['target_mode'] in ['regression', 'ranking']:
            out_ = Dense(1)(pool1_flat_drop)
        show_layer_info('Dense', out_)

        model = Model(inputs=[query, doc, dpool_index], outputs=out_)
        return model