- 数据获取 ####使用spider.py文件爬取网站上的常见疾病图片,我们爬取了大概165种疾病.
- 数据增强
# 指定参数
rotation_range=40, #rotation_range 旋转
shear_range=0.2, #剪切强度(逆时针方向的剪切变换角度)。是用来进行剪切变换的程度。
zoom_range=0.2, #zoom_range 随机放大或缩小
width_shift_range=0.2, #width_shift_range 左右平移
height_shift_range=0.2,#height_shift_range 上下平移
channel_shift_range=0, #随机通道偏移的幅度。
horizontal_flip=True, #随机水平翻转
vertical_flip=True, #随机垂直翻转
fill_mode='nearest' #当进行变换时超出边界的点将根据本参数给定的方法进行处理
- 生成tfrecode文件
illness_new.py文件
# Copyright 2016 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.
# ==============================================================================
Provides data for the flowers dataset.
The dataset scripts used to create the dataset can be found at:
tensorflow/models/research/slim/datasets/download_and_convert_flowers.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
from datasets import dataset_utils
slim = tf.contrib.slim
_FILE_PATTERN = 'illness_new_%s_*.tfrecord'
SPLITS_TO_SIZES = {'train': 4000, 'validation': 1000}#填写的是验证和训练的数据比例
_NUM_CLASSES = 5 ##网络类别
_ITEMS_TO_DESCRIPTIONS = {
'image': 'A color image of varying size.',
'label': 'A single integer between 0 and 4',
}
def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
"""Gets a dataset tuple with instructions for reading flowers.
Args:
split_name: A train/validation split name.
dataset_dir: The base directory of the dataset sources.
file_pattern: The file pattern to use when matching the dataset sources.
It is assumed that the pattern contains a '%s' string so that the split
name can be inserted.
reader: The TensorFlow reader type.
Returns:
A `Dataset` namedtuple.
Raises:
ValueError: if `split_name` is not a valid train/validation split.
"""
if split_name not in SPLITS_TO_SIZES:
raise ValueError('split name %s was not recognized.' % split_name)
if not file_pattern:
file_pattern = _FILE_PATTERN
file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
# Allowing None in the signature so that dataset_factory can use the default.
if reader is None:
reader = tf.TFRecordReader
keys_to_features = {
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),
'image/class/label': tf.FixedLenFeature(
[], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),
}
items_to_handlers = {
'image': slim.tfexample_decoder.Image(),
'label': slim.tfexample_decoder.Tensor('image/class/label'),
}
decoder = slim.tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handlers)
labels_to_names = None
if dataset_utils.has_labels(dataset_dir):
labels_to_names = dataset_utils.read_label_file(dataset_dir)
return slim.dataset.Dataset(
data_sources=file_pattern,
reader=reader,
decoder=decoder,
num_samples=SPLITS_TO_SIZES[split_name],
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
num_classes=_NUM_CLASSES,
labels_to_names=labels_to_names)
在./src/src/slim/datasets/datafactory.py中添加
datasets_map = {
'cifar10': cifar10,
'flowers': flowers,
'imagenet': imagenet,
'mnist': mnist,
'illness':illness,
'illness_new':illness_new
}
对应的数据集的字典在./src/src/slim/文件夹中,运行
python create_tfrecord.py \
--dataset_dir=../../../dataset_dir \ #数据集的位置
--tfrecord_filename=illness_new\ ##生成的tfrecord的文件名
--validation_size=0.2生成的文件如图所示
我基于mobilenet_v2进行了迁移,同时模型是基于预训练模型的基础上进行的 预训练模型是官方给的训练模型，在./src/src/slim/mobinet_v1_1.0.224/上 cd进入src/src/slim/，运行
python train_image_classifier.py \
--train_dir=./train_dir \
--dataset_dir=../../../dataset_dir\ ##tfrecord文件所在位置
--dataset_name=illness_new \ ##数据集的名字
--dataset_split_name=train \
--model_name=mobilenet_v1 \
--train_image_size=224 \
--checkpoint_path=./mobilenet_v1_1.0_224/mobilenet_v1_1.0_224.ckpt \
--max_number_of_steps=30000 \
--checkpoint_exclude_scopes=MobilenetV1/Logits在train_dir中可以看到生成的
测试模型
python eval_image_classifier.py \
--alsologtostderr \
--checkpoint_path=./train_dir/model.ckpt-30000 \ ##从checkpoint最后一个开始作为基准点
--dataset_dir=../../../dataset_dir \
--dataset_name=hp_plants \
--dataset_split_name=validation \
--model_name=mobilenet_v1 \
--eval_image_size=224整体的模型还可以，准确率大概有90%+
生成pb文件
python export_inference_graph.py \
--alsologtostderr \
--model_name=mobilenet_v1 \
--output_file=./inference_graph_mobilenet.pb \
--dataset_name=illness_new固定pb文件
python freeze_graph.py \
--input_graph=./inference_graph_mobilenet.pb \
--input_binary=true \
--input_checkpoint=./train_dir/model.ckpt-3000 \
--output_graph=./frozen_mobilenet.pb \
--output_node_names=MobilenetV1/Predictions/Reshape_1这里有两个地方容易搞错。第一个地方，input_checkpoint参数实际上用到的文件应该是model.ckpt.data-00000-of-00001，但是在指定文件名的时候只需要指定model.ckpt即可。 第二个地方，是output_node_names参数，此处指定的是神经网络图中的输出节点的名字，是在训练阶段的Python脚本中定义的。
- 安装bazel，可以参考官方教程
- git clone tensorflow/tensorflow到本地文件夹中
- 我的服务器支持GPU,所以运行
$ bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package - 然后
~/tensorflow-master/bazel-bin/tensorflow/lite/toco/toco --input_file=mobilenet_v1_1.0_224_frozen.pb --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE --output_file=model.lite --inference_type=FLOAT --input_type=FLOAT --input_arrays=input --output_arrays=MobilenetV1/Predictions/Reshape_1 --input_shapes=1,224,224,3- 最后得到如图所示的
样例1
样例2
