tensorflow 学习之 cifar_10 模型定义 4000

# -*- coding: utf-8 -*-
import  os
import  tensorflow as  tf
import  new_cifar10_input

FLAGS=tf.app.flags.FLAGS  #解析命令行传递的参数

#设置模型参数
tf.app.flags.DEFINE_integer('batch_size',128,"""Number of images to process in a batch.""")
tf.app.flags.DEFINE_string('data_dir','/tmp/cifar10_data',"""Path to the CIFAR-10 data directory.""")
tf.app.flags.DEFINE_boolean('use_fp16',False,"""Train the model using fp16.""")

#数据集的全局常量
IMAGE_SIZE =new_cifar10_input.IMAGE_SISE
NUM_CLASSES =new_cifar10_input.NUM_CLASSES
NUM_EXAMOLES_PER_EPOCH_FOR_TRAIN =new_cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
NUM_EXAMOLES_PER_EPOCH_FOR_EVAL = new_cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_EVAL

#训练的常量
MOVING_AVERAGE_DEVAY=0.999  #移动平均衰减率
NUM_EPOCHS_PER_DECAY=350.0   #衰减呈阶梯函数，控制衰减周期（阶梯宽度)  每350epoch衰减一次
LEARNING_RATE_DECAY_FACTOR=0.1 #学习率衰减因子
INITIAL_LEARNING_RATE=0.1      #初始化学习率

TOWER_NAME='tower'

DATA_URL='http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'

#创建直方图，以及衡量稀疏度的量，在tensorboard展现出来
def _activation_summary(x):
tensor_name=re.sub('%s_[0-9]*/'%TOWER_NAME,'',x.op.name)
tf.summary.histogram(tensor_name+'/activations',x)
tf.summary.scalar(tensor_name+'/sparity',tf.nn.zero_fraction(x))

def _variable_on_cpu(name,shape,initializer):
with tf.float16('/cup:0'):  # #一个 context manager,用于为新的op指定要使用的硬件
dtype=tf.float16 if FLAGS.use_fp16 else tf.float32
var=tf.get_variable(name,shape,initializer=initializer,dtype=dtype)
return  var

def _variable_with_weight_decay(name,shape,stddev,wd):
dtype=tf.float16 if FLAGS.use_fp16 else tf.float32
var=_variable_on_cpu(name,shape,tf.truncated_normal_initializer(stddev=stddev,dtype=dtype))

if wd is not None:
weight_decay=tf.multiply(tf.nn.l2_loss(var),wd,name='weight_loss')
tf.add_to_collection('losses',weight_decay)
return var

def distorted_inputs():
if not FLAGS.data_dir:
raise ValueError('Please supply a data_dir')
data_dir =os.path.join(FLAGS.data_dir,'cifar-10-batches-bin')
images,lables=new_cifar10_input.distorted_inputs(data_dir=data_dir,batch_size=FLAGS.batch_size)

if FLAGS.use_fp16:
images=tf.cast(images,tf.float16)
lables=tf.cast(lables,tf.float16)
return  images,lables

def inputs(eval_data):
if not FLAGS.data_dir:
raise ValueError('Please supply a data_dir')
data_dir =os.path.join(FLAGS.data_dir,'cifar-10-batches-bin')
images,labels=new_cifar10_input.inputs(eval_data=eval_data,data_dir=data_dir,batch_size=batch_size)

if FLAGS.use_fp16:
images=tf.cast(images,tf.float16)
labels=tf.cast(labels,tf.float16)
return images,labels

def inference(images):
#卷积和池化第一层
with tf.variable_scope('conv1') as scope:
kernel=_variable_with_weight_decay('weights',shape=[5,5,3,64],stddev=5e-2,wd=0.0)
conv=tf.nn.conv2d(images,kernel,[1,1,1,1],padding='SAME')
biases=_variable_on_cpu('biases',[64],tf.constant_initializer(0.0))
pre_activation=tf.nn.bias_add(conv,biases)
conv1=tf.nn.relu(pre_activation,name=scope.name)
_activation_summary(conv1)

pool1=tf.nn.max_pool(conv1,ksize=[1,3,3,1],strides=[1,2,2,1],padding='SAME',name='pool1')

norm1=tf.nn.lrn(pool1,4,bias=1.0,alpha=0.001/9.0,beta=0.75,name='norm1')

#卷积和池化第二层
with tf.variable_scope('conv2') as  scope:
kernel=_variable_with_weight_decay('weights',shape=[5,5,64,64],stddev=5e-2,wd=0.0)
conv=tf.nn.conv2d(norm1,kernel,[1,1,1,1],padding='SAME')
biases=_variable_on_cpu('biases',[64],tf.constant_initializer(0.1))
pre_activation=tf.nn.bias_add(conv,biases)
conv2=tf.nn.relu(pre_activation,name=scope.name)
_activation_summary(conv2)

norm2=tf.nn.lrn(conv2,4,bias=1.0,alpha=0.001/9.0,beta=0.75,name='norm2')
pool2=tf.nn.max_pool(norm2,ksize=[1,3,3,1],strides=[1,2,2,1],padding='SAME',name='pool2')
#全连接层
with tf.variable_scope('fc1') as  scope:
reshape=tf.reshape(pool2,[FLAGS.batch_size,-1])
dim=reshape.get_shape()[1].value
weights=_variable_with_weight_decay('weights',shape=[dim,384],stddev=0.04,wd=0.004)
biases=_variable_on_cpu('biases',[384],tf.constant_initializer(0.1))
fc1=tf.nn.relu(tf.matmul(reshape,weights)+biases,name=scope.name)
_activation_summary(fc1)

with tf.variable_scope('fc2') as  scope:
weights=_variable_with_weight_decay('weights',shape=[384,192],stddev=0.04,wd=0.004)
biases=_variable_on_cpu('biased',[192],tf.constant_initializer(0.1))
fc2=tf.nn.relu(tf.matmul(fc1,weights)+biases,name=scope.name)
_activation_summary(fc2)

#进行线性变换输出logistics模型
with tf.variable_scope('sotfmax_linear') as  scope:
weights=_variable_with_weight_decay('weights',[192,NUM_CLASSES],stddev=1/192.0,wd=0.0)
biases=_variable_on_cpu('biases',[NUM_CLASSES],tf.constant_initializer(0.0))
softmax_linear=tf.add(tf.matmul(fc2,weights),biases,name=scope.name)
_activation_summary(softmax_linear)
return softmax_linear