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acn_mindspore's Introduction

基于胶囊网络的图像描述生成算法

硬件

ModelArts平台,硬件配置为1*Ascend 910 CPU24核 内存96GiB

环境

  • MindSpore 1.7
  • numpy
  • argparse
  • pickle
  • json

数据集

使用的数据集:MS COCO 使用Faster RCNN提取的图片特征进行训练,数据集获取直接执行data.ipynb即可。

训练

用法:python train.py

选项:
    --device               代码实现设备,可选项为Ascend或GPU。默认为Ascend
    --device_id            设备ID,默认为0
    --works                线程数,默认为4
    --lr                   学习率,默认为0.0005
    --batch_size           默认为8
    --result_folder        结果文件存储位置,默认为./ACN_RESULT
    --dataset_path         数据集路径,默认为./mscoco
    --epochs               训练论述,默认为50
    --seq_per_img          每张图片对应句子数量,默认为5
    --encode_layer_num     编码器层数,默认为3
    --decode_layer_num     解码器层数,默认为3

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