doc2vec_eval_doc_prediction.py 文件源码

def model(self, input_vectors, input_gene, input_variation, output_label, batch_size,
              embedding_size=EMBEDDINGS_SIZE,
              output_classes=9):
        logits, targets = doc2vec_prediction_model(input_vectors, input_gene, input_variation,
                                                   output_label, batch_size,
                                                   is_training=False, embedding_size=embedding_size,
                                                   output_classes=output_classes)

        loss = tf.nn.softmax_cross_entropy_with_logits(labels=targets, logits=logits)
        self.global_step = training_util.get_or_create_global_step()
        global_step_increase = tf.assign_add(self.global_step, 1)
        self.accumulated_loss = tf.Variable(0.0, dtype=tf.float32, name='accumulated_loss',
                                            trainable=False)
        self.accumulated_loss = tf.assign_add(self.accumulated_loss, tf.reduce_sum(loss))
        self.prediction = tf.nn.softmax(logits)
        self.metrics = metrics.single_label(self.prediction, targets, moving_average=False)
        steps = tf.cast(global_step_increase, dtype=tf.float32)
        tf.summary.scalar('loss', self.accumulated_loss / (steps * batch_size))
        return None