資源簡介
識別CIFAR數據集中的10類物體
一、 實驗目標
熟悉使用深度學習工具tensorflow,基于該平臺對Cifar-10 中的圖像數據進行分類識別,在這個過程中掌握卷積神經網絡的基本思想。

代碼片段和文件信息
#?Copyright?2015?Google?Inc.?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.
#?==============================================================================
“““Builds?the?CIFAR-10?network.
Summary?of?available?functions:
?#?Compute?input?images?and?labels?for?training.?If?you?would?like?to?run
?#?evaluations?use?input()?instead.
?inputs?labels?=?distorted_inputs()
?#?Compute?inference?on?the?model?inputs?to?make?a?prediction.
?predictions?=?inference(inputs)
?#?Compute?the?total?loss?of?the?prediction?with?respect?to?the?labels.
?loss?=?loss(predictions?labels)
?#?Create?a?graph?to?run?one?step?of?training?with?respect?to?the?loss.
?train_op?=?train(loss?global_step)
“““
#?pylint:?disable=missing-docstring
from?__future__?import?absolute_import
from?__future__?import?division
from?__future__?import?print_function
import?gzip
import?os
import?re
import?sys
import?tarfile
import?tensorflow.python.platform
from?six.moves?import?urllib
import?tensorflow?as?tf
#from?tensorflow.models.image.cifar10?import?cifar10_input
import?cifar10_input
FLAGS?=?tf.app.flags.FLAGS
#?Basic?model?parameters.
tf.app.flags.DEFINE_integer(‘batch_size‘?128
????????????????????????????“““Number?of?images?to?process?in?a?batch.“““)
tf.app.flags.DEFINE_string(‘data_dir‘?‘cifar10_data/‘
???????????????????????????“““Path?to?the?CIFAR-10?data?directory.“““)
#?Global?constants?describing?the?CIFAR-10?data?set.
IMAGE_SIZE?=?cifar10_input.IMAGE_SIZE
NUM_CLASSES?=?cifar10_input.NUM_CLASSES
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN?=?cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
NUM_EXAMPLES_PER_EPOCH_FOR_EVAL?=?cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_EVAL
#?Constants?describing?the?training?process.
MOVING_AVERAGE_DECAY?=?0.9999?????#?The?decay?to?use?for?the?moving?average.
NUM_EPOCHS_PER_DECAY?=?350.0??????#?Epochs?after?which?learning?rate?decays.
LEARNING_RATE_DECAY_FACTOR?=?0.1??#?Learning?rate?decay?factor.
INITIAL_LEARNING_RATE?=?0.1???????#?Initial?learning?rate.
#?If?a?model?is?trained?with?multiple?GPU‘s?prefix?all?Op?names?with?tower_name
#?to?differentiate?the?operations.?Note?that?this?prefix?is?removed?from?the
#?names?of?the?summaries?when?visualizing?a?model.
TOWER_NAME?=?‘tower‘
DATA_URL?=?‘http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz‘
def?_activation_summary(x):
??“““Helper?to?create?summaries?for?activations.
??Creates?a?summary?that?provides?a?histogram?of?activations.
??Creates?a?summary?that?measure?the?sparsity?of?activ
?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件?????602251??2018-07-07?10:05??報告.docx
?????文件??????14151??2017-06-12?18:47??cifar10\cifar10.py
?????文件???????5675??2017-06-12?18:47??cifar10\cifar10_eval.py
?????文件???????9289??2017-06-12?18:47??cifar10\cifar10_input.py
?????文件???????4706??2017-06-12?18:47??cifar10\cifar10_train.py
?????文件?????????63??2018-06-14?19:43??cifar10\readme.txt
?????目錄??????????0??2018-06-14?19:42??cifar10
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???????????????636135????????????????????7
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