示例#1
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# -*- coding: utf-8 -*-
# @Time    : 2018/4/11 10:08
# @Author  : jiaopan
# @Email   : [email protected]
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
import tensorflow as tf
import utils
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer(
    'batch_size', int(utils.configUtil("global.conf", "dataset",
                                       "batch_size")), """每个batch样本总数""")
IMAGE_SIZE = int(utils.configUtil("global.conf", "dataset", "image_mat_size"))
NUM_CLASSES = int(utils.configUtil("global.conf", "dataset", "num_class"))
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = int(
    utils.configUtil("global.conf", "train", "train_data_count"))
NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = int(
    utils.configUtil("global.conf", "eval", "eval_data_count"))

# 超参数设置
MOVING_AVERAGE_DECAY = 0.9999
NUM_EPOCHS_PER_DECAY = 350.0
LEARNING_RATE_DECAY_FACTOR = 0.1
INITIAL_LEARNING_RATE = 0.1

TOWER_NAME = 'tower'


def activation_summary(x):
示例#2
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import utils

print(utils.configUtil("global.conf", "data", "train_data_dir"))
示例#3
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import math
import time
from tensorflow.python.platform import gfile
import numpy as np
import tensorflow as tf
import network
import tfrecord
import utils

FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string(
    'eval_dir', utils.configUtil("global.conf", "eval", "eval_log_dir"),
    """验证日志目录.""")
tf.app.flags.DEFINE_string(
    'eval_data', utils.configUtil("global.conf", "eval", "eval_tfrecord_dir"),
    """验证数据集目录""")
tf.app.flags.DEFINE_string(
    'checkpoint_dir', utils.configUtil("global.conf", "model", "model_dir"),
    """保存的模型.""")
tf.app.flags.DEFINE_integer('eval_interval_secs', 60 * 3, """设置每隔多长时间做一侧评估""")
tf.app.flags.DEFINE_integer(
    'num_examples',
    int(utils.configUtil("global.conf", "eval", "eval_data_count")),
    """验证数据集样本总数""")
tf.app.flags.DEFINE_boolean('run_once', False, """仅验证一次.""")
示例#4
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# 训练模块
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import os.path
import time
import numpy as np
from six.moves import xrange
import tensorflow as tf
import network
import tfrecord
import utils

FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('train_dir', utils.configUtil("global.conf","model","model_dir"),
                           """模型保存目录"""
                           """检查点存储目录.(tensorboard查看)""")

tf.app.flags.DEFINE_integer('max_steps', int(utils.configUtil("global.conf","train","max_steps")),
                            """最大训练/迭代次数.""")

tf.app.flags.DEFINE_boolean('log_device_placement', False,"""""")

tf.app.flags.DEFINE_string('train_data',utils.configUtil("global.conf","train","train_tfrecord_dir"),
                           '训练集目录（tfrecord）')
tf.app.flags.DEFINE_integer('train_num',int(utils.configUtil("global.conf","train","train_data_count")),
                           '训练集样本总数')
def train():

  with tf.Graph().as_default():
示例#5
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# -*- coding: utf-8 -*-
# @Time    : 2018/4/15 2:14
# @Author  : jiaopan
# @Email   : [email protected]
from PIL import Image
import tensorflow as tf
import network
import utils
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string(
    'checkpoint_dir', utils.configUtil("global.conf", "model", "model_dir"),
    """保存的模型.""")


def inputs(input, count=1, batch_size=1):
    network.FLAGS.batch_size = batch_size
    img = Image.open(input)
    img = img.resize((32, 32))
    img = img.tobytes()
    img = tf.decode_raw(img, tf.uint8)
    img = tf.reshape(img, [3, 32, 32])
    img = tf.transpose(img, [1, 2, 0])
    img = tf.cast(img, tf.float32)
    float_image = tf.image.per_image_standardization(img)
    capacity = int(count * 0.4 + 3 * batch_size)
    min_after_dequeue = int(batch_size * 0.4)
    images, label_batch = tf.train.shuffle_batch(
        [float_image, '?'],
        batch_size=batch_size,
        capacity=capacity,
        min_after_dequeue=min_after_dequeue,
示例#6
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#coding:utf-8
# -*- coding: utf-8 -*-
# @Time    : 2018/4/11 10:08
# @Author  : jiaopan
# @Email   : [email protected]

import tensorflow as tf
import os,sys,time
from PIL import Image
import utils

IMAGE_SIZE = int(utils.configUtil("global.conf","dataset","resize_image_size")) # 裁剪大小
IMAGE_MAT_SIZE = int(utils.configUtil("global.conf","dataset","image_mat_size")) # reshape参数
CHANNELS = int(utils.configUtil("global.conf","dataset","chnnels")) # 通道
TRAIN_DATASET = utils.configUtil("global.conf","dataset","train_data_dir") # 训练原始数据
EVAL_DATASET = utils.configUtil("global.conf","dataset","eval_data_dir") # 验证集原始数据
BATCH_SIZE = int(utils.configUtil("global.conf","dataset","batch_size"))

def create(dataset_dir,tfrecord_path,tfrecord_name="train_tfrecord",width=IMAGE_SIZE,height=IMAGE_SIZE):
    """
    #构建图片TFrecord文件
    param dataset_dir:原始图片的根目录,目录下包含多个子目录,每个子目录下为同一类别的图片
    param tfrecord_name:存储的TFreord文件名
    param tfrecord_path:存储的TFreord文件的目录路径
    param width:图片裁剪宽度
    param height:图片裁剪高度
    param channels:通道
    """
    if not os.path.exists(dataset_dir):
        print('创建TFRECORD文件时出错,文件目录或文件不存在,请检查路径名..\n')
        exit()