text_classification_train.py 文件源码

def model(self, input_text_begin, input_text_end, gene, variation, expected_labels, batch_size,
              vocabulary_size=VOCABULARY_SIZE, embeddings_size=EMBEDDINGS_SIZE, output_classes=9):
        # embeddings
        embeddings = _load_embeddings(vocabulary_size, embeddings_size)

        # global step
        self.global_step = training_util.get_or_create_global_step()

        # model
        with slim.arg_scope(self.text_classification_model.model_arg_scope()):
            outputs = self.text_classification_model.model(input_text_begin, input_text_end,
                                                           gene, variation, output_classes,
                                                           embeddings=embeddings,
                                                           batch_size=batch_size)

        # loss
        targets = self.text_classification_model.targets(expected_labels, output_classes)
        self.loss = self.text_classification_model.loss(targets, outputs)
        tf.summary.scalar('loss', self.loss)

        # learning rate
        self.optimizer, self.learning_rate = \
            self.text_classification_model.optimize(self.loss, self.global_step)
        if self.learning_rate is not None:
            tf.summary.scalar('learning_rate', self.learning_rate)

        # metrics
        self.metrics = metrics.single_label(outputs['prediction'], targets)

        # saver to save the model
        self.saver = tf.train.Saver()
        # check a nan value in the loss
        self.loss = tf.check_numerics(self.loss, 'loss is nan')

        return None