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paddlepaddle/README.md
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# Image Classification based on NAS-Searched Models
This directory contains 10 image classification models.
Nine of them are automatically searched models from different Neural Architecture Search (NAS) algorithms. The other is the residual network.
Nine of them are automatically searched models using different Neural Architecture Search (NAS) algorithms, and the other is the residual network.
We provide codes and scripts to train these models on both CIFAR-10 and CIFAR-100.
We use the standard data augmentation, i.e., random crop, random flip, and normalization.
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This project has the following requirements:
- Python = 3.6
- PadddlePaddle Fluid >= v0.15.0
- numpy, tarfile, cPickle, PIL
### Data Preparation
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### Training Models
After setting up the environment and preparing the data, one can train the model. The main function entrance is `train_cifar.py`. We also provide some scripts for easy usage.
After setting up the environment and preparing the data, you can train the model. The main function entrance is `train_cifar.py`. We also provide some scripts for easy usage.
```
bash ./scripts/base-train.sh 0 cifar-10 ResNet110
bash ./scripts/train-nas.sh 0 cifar-10 GDAS_V1
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bash ./scripts/train-nas.sh 0 cifar-10 PNASNet
bash ./scripts/train-nas.sh 0 cifar-100 SETN
```
The first argument is the GPU-ID to train your program, the second argument is the dataset name, and the last one is the model name.
The first argument is the GPU-ID to train your program, the second argument is the dataset name (`cifar-10` or `cifar-100`), and the last one is the model name.
Please use `./scripts/base-train.sh` for ResNet and use `./scripts/train-nas.sh` for NAS-searched models.

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paddlepaddle/lib/utils/meter.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import time, sys
import numpy as np

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paddlepaddle/lib/utils/time_utils.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import time, sys
import numpy as np