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anzhengqi 008b91b2a1 inject epoch ctrl op in the execution tree and send eos at the end of epoch		2020-07-20 13:02:47 +08:00
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src	GPU Lstm network	2020-07-01 09:33:42 +08:00
README.md	add pretrain for lstm & vgg16 and remove lstm/vgg16/googlenet from directory 'mindspore/model_zoo'	2020-06-22 17:17:10 +08:00
eval.py	change lstm import pacakge location	2020-06-23 11:28:27 +08:00
train.py	inject epoch ctrl op in the execution tree and send eos at the end of epoch	2020-07-20 13:02:47 +08:00

README.md

LSTM Example

Description

This example is for LSTM model training and evaluation.

Requirements

Install MindSpore.
Download the dataset aclImdb_v1.

Unzip the aclImdb_v1 dataset to any path you want and the folder structure should be as follows:
.
├── train  # train dataset
└── test   # infer dataset

Download the GloVe file.

Unzip the glove.6B.zip to any path you want and the folder structure should be as follows:
.
├── glove.6B.100d.txt
├── glove.6B.200d.txt
├── glove.6B.300d.txt    # we will use this one later.
└── glove.6B.50d.txt

Adding a new line at the beginning of the file which named glove.6B.300d.txt. It means reading a total of 400,000 words, each represented by a 300-latitude word vector.
400000    300

Running the Example

Training

python train.py --preprocess=true --aclimdb_path=your_imdb_path --glove_path=your_glove_path  > out.train.log 2>&1 &

The python command above will run in the background, you can view the results through the file out.train.log.

After training, you'll get some checkpoint files under the script folder by default.

You will get the loss value as following:

# grep "loss is " out.train.log
epoch: 1 step: 390, loss is 0.6003723
epcoh: 2 step: 390, loss is 0.35312173
...

Evaluation

python eval.py --ckpt_path=./lstm-20-390.ckpt > out.eval.log 2>&1 &

The above python command will run in the background, you can view the results through the file out.eval.log.

You will get the accuracy as following:

# grep "acc" out.eval.log
result: {'acc': 0.83}

Usage:

Training

usage: train.py [--preprocess {true,false}] [--aclimdb_path ACLIMDB_PATH]
                [--glove_path GLOVE_PATH] [--preprocess_path PREPROCESS_PATH]
                [--ckpt_path CKPT_PATH] [--pre_trained PRE_TRAINED]
                [--device_target {GPU,CPU}]

parameters/options:
  --preprocess          whether to preprocess data.
  --aclimdb_path        path where the dataset is stored.
  --glove_path          path where the GloVe is stored.
  --preprocess_path     path where the pre-process data is stored.
  --ckpt_path           the path to save the checkpoint file.
  --pre_trained         the pretrained checkpoint file path.
  --device_target       the target device to run, support "GPU", "CPU".

Evaluation

usage: eval.py [--preprocess {true,false}] [--aclimdb_path ACLIMDB_PATH]
               [--glove_path GLOVE_PATH] [--preprocess_path PREPROCESS_PATH]
               [--ckpt_path CKPT_PATH] [--device_target {GPU,CPU}]

parameters/options:
  --preprocess          whether to preprocess data.
  --aclimdb_path        path where the dataset is stored.
  --glove_path          path where the GloVe is stored.
  --preprocess_path     path where the pre-process data is stored.
  --ckpt_path           the checkpoint file path used to evaluate model.
  --device_target       the target device to run, support "GPU", "CPU".