drmm.py 文件源码

def build(self):
        def tensor_product(x):
            a = x[0]
            b = x[1]
            y = K.batch_dot(a, b, axis=1)
            y = K.einsum('ijk, ikl->ijl', a, b)
            return y
        query = Input(name='query', shape=(self.config['text1_maxlen'],))
        show_layer_info('Input', query)
        doc = Input(name='doc', shape=(self.config['text1_maxlen'], self.config['hist_size']))
        show_layer_info('Input', doc)

        embedding = Embedding(self.config['vocab_size'], self.config['embed_size'], weights=[self.config['embed']], trainable = False)

        q_embed = embedding(query)
        show_layer_info('Embedding', q_embed)
        q_w = Dense(1, kernel_initializer=self.initializer_gate, use_bias=False)(q_embed)
        show_layer_info('Dense', q_w)
        q_w = Lambda(lambda x: softmax(x, axis=1), output_shape=(self.config['text1_maxlen'], ))(q_w)
        show_layer_info('Lambda-softmax', q_w)
        z = doc
        z = Dropout(rate=self.config['dropout_rate'])(z)
        show_layer_info('Dropout', z)
        for i in range(self.config['num_layers']-1):
            z = Dense(self.config['hidden_sizes'][i], kernel_initializer=self.initializer_fc)(z)
            z = Activation('tanh')(z)
            show_layer_info('Dense', z)
        z = Dense(self.config['hidden_sizes'][self.config['num_layers']-1], kernel_initializer=self.initializer_fc)(z)
        show_layer_info('Dense', z)
        z = Permute((2, 1))(z)
        show_layer_info('Permute', z)
        z = Reshape((self.config['text1_maxlen'],))(z)
        show_layer_info('Reshape', z)
        q_w = Reshape((self.config['text1_maxlen'],))(q_w)
        show_layer_info('Reshape', q_w)

        out_ = Dot( axes= [1, 1])([z, q_w])
        if self.config['target_mode'] == 'classification':
            out_ = Dense(2, activation='softmax')(out_)
        show_layer_info('Dense', out_)

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