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# Image Classification based on NAS-Searched Models
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This directory contains 10 image classification models.
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Nine of them are automatically searched models from different Neural Architecture Search (NAS) algorithms. The other is the residual network.
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Nine of them are automatically searched models using different Neural Architecture Search (NAS) algorithms, and the other is the residual network.
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We provide codes and scripts to train these models on both CIFAR-10 and CIFAR-100.
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We use the standard data augmentation, i.e., random crop, random flip, and normalization.
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This project has the following requirements:
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- Python = 3.6
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- PadddlePaddle Fluid >= v0.15.0
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- numpy, tarfile, cPickle, PIL
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### Data Preparation
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### Training Models
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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.
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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.
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```
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bash ./scripts/base-train.sh 0 cifar-10 ResNet110
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bash ./scripts/train-nas.sh 0 cifar-10 GDAS_V1
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bash ./scripts/train-nas.sh 0 cifar-10 PNASNet
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bash ./scripts/train-nas.sh 0 cifar-100 SETN
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```
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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.
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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.
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Please use `./scripts/base-train.sh` for ResNet and use `./scripts/train-nas.sh` for NAS-searched models.
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import time, sys
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import numpy as np
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# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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#
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import time, sys
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import numpy as np
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