TensorFlow中slim包的具体用法
本次使用的TensorFlow版本是1.13.0
地址：https://github.com/tensorflow/models/tree/r1.13.0
到tensorflow-models的GitHub下载research下面的slim这个包到本地
TensorFlow中slim包的目录结构：
python
-- slim
|-- BUILD
|-- README.md
|-- WORKSPACE
|-- __init__.py
|-- datasets
| |-- __init__.py
| |-- __pycache__
| | |-- __init__.cpython-37.pyc
| | |-- dataset_utils.cpython-37.pyc
| | |-- download_and_convert_cifar10.cpython-37.pyc
| | |-- download_and_convert_flowers.cpython-37.pyc
| | `-- download_and_convert_mnist.cpython-37.pyc
| |-- build_imagenet_data.py
| |-- cifar10.py
| |-- dataset_factory.py
| |-- dataset_utils.py
| |-- download_and_convert_cifar10.py
| |-- download_and_convert_flowers.py
| |-- download_and_convert_imagenet.sh
| |-- download_and_convert_mnist.py
| |-- download_imagenet.sh
| |-- flowers.py
| |-- imagenet.py
| |-- imagenet_2012_validation_synset_labels.txt
| |-- imagenet_lsvrc_2015_synsets.txt
| |-- imagenet_metadata.txt
| |-- mnist.py
| |-- preprocess_imagenet_validation_data.py
| `-- process_bounding_boxes.py
|-- deployment
| |-- __init__.py
| |-- model_deploy.py
| `-- model_deploy_test.py
|-- download_and_convert_data.py # 下载相应的数据集，并将数据打包成TF-record的格式
|-- eval_image_classifier.py # 测试模型分类效果
|-- export_inference_graph.py
|-- export_inference_graph_test.py
|-- nets
| |-- __init__.py
| |-- alexnet.py
| |-- alexnet_test.py
| |-- cifarnet.py
| |-- cyclegan.py
| |-- cyclegan_test.py
| |-- dcgan.py
| |-- dcgan_test.py
| |-- i3d.py
| |-- i3d_test.py
| |-- i3d_utils.py
| |-- inception.py
| |-- inception_resnet_v2.py
| |-- inception_resnet_v2_test.py
| |-- inception_utils.py
| |-- inception_v1.py
| |-- inception_v1_test.py
| |-- inception_v2.py
| |-- inception_v2_test.py
| |-- inception_v3.py
| |-- inception_v3_test.py
| |-- inception_v4.py
| |-- inception_v4_test.py
| |-- lenet.py
| |-- mobilenet
| | |-- README.md
| | |-- __init__.py
| | |-- conv_blocks.py
| | |-- madds_top1_accuracy.png
| | |-- mnet_v1_vs_v2_pixel1_latency.png
| | |-- mobilenet.py
| | |-- mobilenet_example.ipynb
| | |-- mobilenet_v2.py
| | `-- mobilenet_v2_test.py
| |-- mobilenet_v1.md
| |-- mobilenet_v1.png
| |-- mobilenet_v1.py
| |-- mobilenet_v1_eval.py
| |-- mobilenet_v1_test.py
| |-- mobilenet_v1_train.py
| |-- nasnet
| | |-- README.md
| | |-- __init__.py
| | |-- nasnet.py
| | |-- nasnet_test.py
| | |-- nasnet_utils.py
| | |-- nasnet_utils_test.py
| | |-- pnasnet.py
| | `-- pnasnet_test.py
| |-- nets_factory.py
| |-- nets_factory_test.py
| |-- overfeat.py
| |-- overfeat_test.py
| |-- pix2pix.py
| |-- pix2pix_test.py
| |-- resnet_utils.py
| |-- resnet_v1.py
| |-- resnet_v1_test.py
| |-- resnet_v2.py
| |-- resnet_v2_test.py
| |-- s3dg.py
| |-- s3dg_test.py
| |-- vgg.py
| `-- vgg_test.py
|-- preprocessing
| |-- __init__.py
| |-- cifarnet_preprocessing.py
| |-- inception_preprocessing.py
| |-- lenet_preprocessing.py
| |-- preprocessing_factory.py
| `-- vgg_preprocessing.py
|-- scripts # gqr:存储的是相关的模型训练脚本
| |-- export_mobilenet.sh
| |-- finetune_inception_resnet_v2_on_flowers.sh
| |-- finetune_inception_v1_on_flowers.sh
| |-- finetune_inception_v3_on_flowers.sh
| |-- finetune_resnet_v1_50_on_flowers.sh
| |-- train_cifarnet_on_cifar10.sh
| `-- train_lenet_on_mnist.sh
|-- setup.py
|-- slim_walkthrough.ipynb
`-- train_image_classifier.py # 训练模型的脚本1、训练脚本文件（该文件包含数据下载打包、模型训练，模型评估流程）
scripts/finetune_resnet_v1_50_on_flowers.sh
python
#!/bin/bash
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
#
# This script performs the following operations:
# 1. Downloads the Flowers dataset
# 2. Fine-tunes a ResNetV1-50 model on the Flowers training set.
# 3. Evaluates the model on the Flowers validation set.
#
# Usage:
# cd slim
# ./slim/scripts/finetune_resnet_v1_50_on_flowers.sh
set -e
# Where the pre-trained ResNetV1-50 checkpoint is saved to.
PRETRAINED_CHECKPOINT_DIR=/tmp/checkpoints # gqr:预训练模型存放路径
# Where the training (fine-tuned) checkpoint and logs will be saved to.
TRAIN_DIR=/tmp/flowers-models/resnet_v1_50
# Where the dataset is saved to.
DATASET_DIR=/tmp/flowers # gqr:数据集存放路径
# Download the pre-trained checkpoint.
if [ ! -d "$PRETRAINED_CHECKPOINT_DIR" ]; then
mkdir ${PRETRAINED_CHECKPOINT_DIR}
fi
if [ ! -f ${PRETRAINED_CHECKPOINT_DIR}/resnet_v1_50.ckpt ]; then
wget http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz
tar -xvf resnet_v1_50_2016_08_28.tar.gz
mv resnet_v1_50.ckpt ${PRETRAINED_CHECKPOINT_DIR}/resnet_v1_50.ckpt
rm resnet_v1_50_2016_08_28.tar.gz
fi
# Download the dataset
python download_and_convert_data.py \
--dataset_name=flowers \
--dataset_dir=${DATASET_DIR}
# Fine-tune only the new layers for 3000 steps.
python train_image_classifier.py \
--train_dir=${TRAIN_DIR} \
--dataset_name=flowers \
--dataset_split_name=train \
--dataset_dir=${DATASET_DIR} \
--model_name=resnet_v1_50 \
--checkpoint_path=${PRETRAINED_CHECKPOINT_DIR}/resnet_v1_50.ckpt \
--checkpoint_exclude_scopes=resnet_v1_50/logits \
--trainable_scopes=resnet_v1_50/logits \
--max_number_of_steps=3000 \
--batch_size=32 \
--learning_rate=0.01 \
--save_interval_secs=60 \
--save_summaries_secs=60 \
--log_every_n_steps=100 \
--optimizer=rmsprop \
--weight_decay=0.00004
# Run evaluation.
python eval_image_classifier.py \
--checkpoint_path=${TRAIN_DIR} \
--eval_dir=${TRAIN_DIR} \
--dataset_name=flowers \
--dataset_split_name=validation \
--dataset_dir=${DATASET_DIR} \
--model_name=resnet_v1_50
# Fine-tune all the new layers for 1000 steps.
python train_image_classifier.py \
--train_dir=${TRAIN_DIR}/all \
--dataset_name=flowers \
--dataset_split_name=train \
--dataset_dir=${DATASET_DIR} \
--checkpoint_path=${TRAIN_DIR} \
--model_name=resnet_v1_50 \
--max_number_of_steps=1000 \
--batch_size=32 \
--learning_rate=0.001 \
--save_interval_secs=60 \
--save_summaries_secs=60 \
--log_every_n_steps=100 \
--optimizer=rmsprop \
--weight_decay=0.00004
# Run evaluation.
python eval_image_classifier.py \
--checkpoint_path=${TRAIN_DIR}/all \
--eval_dir=${TRAIN_DIR}/all \
--dataset_name=flowers \
--dataset_split_name=validation \
--dataset_dir=${DATASET_DIR} \
--model_name=resnet_v1_50以上文件以下载并打包flowers数据集为例会调用slim/datasets下的****download_and_convert_flowers.py
代码43行：_NUM_VALIDATION = 350 值的意思的测试数据集的数量,我们一般2,8分数据集,这里只用填写测试集的数据代码会自动吧总数据集分成2部分
代码48行：_NUM_SHARDS = 1 这个的意思是生成几个tfrecord文件,这个数量是根据你数据量来划分
代码190行：dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir) 函数为下载数据集函数，如果本地已经存在数据集，可将将其注释掉
代码210行：_clean_up_temporary_files(dataset_dir) 函数为打包完毕后删除下载的数据集文件，如果需要下载的数据集可以将其注释掉
上述文件执行完毕后，会得到以下文件
3、模型训练
模型训练文件为
以下是该文件中各个模块相关内容
1、数据集相关模块：
2、设置网络模型模块
3、数据预处理模块
4、定义损失loss
5、定义优化器模块
运行训练指令：
python
python train_image_classifier.py \
--train_dir=./data/flowers-models/resnet_v1_50\
--dataset_name=flowers \
--dataset_split_name=train \
--dataset_dir=./data/flowers \
--model_name=resnet_v1_50 \
--checkpoint_path=./data/checkpoints/resnet_v1_50.ckpt \
--checkpoint_exclude_scopes=resnet_v1_50/logits \
--trainable_scopes=resnet_v1_50/logits \
--max_number_of_steps=3000 \
--batch_size=32 \
--learning_rate=0.01 \
--save_interval_secs=60 \
--save_summaries_secs=60 \
--log_every_n_steps=100 \
--optimizer=rmsprop \
--weight_decay=0.00004--dataset_name=指定模板
--model_name=指定预训练模板
--dataset_dir=指定训练集目录
--checkpoint_exclude_scopes=指定忘记那几层的参数,不带进训练里面,记住提取特征的部分
--train_dir=训练参数存放地址
--trainable_scopes=设定只对那几层变量进行调整,其他层都不进行调整,不设定就会对所有层训练(所以是必须要给定的)
--learning_rate=学习率
--optimizer=优化器
--max_number_of_steps=训练步数
--batch_size=一次训练所选取的样本数。 (Batch Size的大小影响模型的优化程度和速度。同时其直接影响到GPU内存的使用情况，假如你GPU内存不大，该数值最好设置小一点。)
--weight_decay=即模型中所有参数的二次正则化超参数(这个的加入就是为了防止过拟合加入正则项,weight_decay 是乘在正则项的前面，控制正则化项在损失函数中所占权重的)