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cifar10_Climate_dist.py
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#!D:/workplace/python
# -*- coding: utf-8 -*-
# @File : cifar10_atrous_conv2d.py
# @Author: WangYe
# @Date : 2019/3/26
# @Software: PyCharm
# coding:utf-8
# 导入官方cifar10模块
#from tensorflow.image.cifar10 import cifar10
# import cifar10
# import tensorflow as tf
#
# # tf.app.flags.FLAGS是tensorflow的一个内部全局变量存储器
# FLAGS = tf.app.flags.FLAGS
# # cifar10模块中预定义下载路径的变量data_dir为'/tmp/cifar10_eval',预定义如下：
# # tf.app.flags.DEFINE_string('data_dir', './cifar10_data',
# # """Path to the CIFAR-10 data directory.""")
# # 为了方便，我们将这个路径改为当前位置
# FLAGS.data_dir = './cifar10_data'
#
# # 如果不存在数据文件则下载，并且解压
# cifar10.maybe_download_and_extract()
import cifar10,cifar10_input
import tensorflow as tf
import numpy as np
import os
import time
FLAGS = tf.app.flags.FLAGS
#cifar10.maybe_download_and_extract()
max_steps = 1000
batch_size = 500
data_dir = r'./cifar10_data/cifar-10-batches-bin'
# For distributed
tf.app.flags.DEFINE_string("ps_hosts","localhost:11111",
"Comma-separated list of hostname:port pairs")
#,localhost:111113,localhost:111114
tf.app.flags.DEFINE_string("worker_hosts", "localhost:111112",
"Comma-separated list of hostname:port pairs")
tf.app.flags.DEFINE_string("job_name", "ps", "One of 'ps', 'worker'")
tf.app.flags.DEFINE_integer("task_index", 0, "Index of task within the job")
tf.app.flags.DEFINE_integer("issync", 1, "是否采用分布式的同步模式，1表示同步模式，0表示异步模式")
tf.app.flags.DEFINE_string("cuda", "0", "specify gpu")
#FLAGS = tf.app.flags.FLAGS
if FLAGS.cuda:
os.environ["CUDA_VISIBLE_DEVICES"] = FLAGS.cuda
def variable_with_weight_loss(shape, stddev, w1):
var = tf.Variable(tf.truncated_normal(shape, stddev=stddev))
if w1 is not None:
weight_loss = tf.multiply(tf.nn.l2_loss(var), w1, name='weight_loss')
tf.add_to_collection('losses', weight_loss)
return var
start = time.clock() #计算开始时间
def main(_):
ps_hosts = FLAGS.ps_hosts.split(",")
worker_hosts = FLAGS.worker_hosts.split(",")
cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
server = tf.train.Server(cluster, job_name=FLAGS.job_name, task_index=FLAGS.task_index)
issync = FLAGS.issync
if FLAGS.job_name == "ps":
server.join()
elif FLAGS.job_name == "worker":
with tf.device(tf.train.replica_device_setter(
worker_device="/job:worker/task:%d" % FLAGS.task_index,
cluster=cluster)):
global_step = tf.Variable(0, name='global_step', trainable=False)
images_train, labels_train = cifar10_input.distorted_inputs(data_dir=data_dir, batch_size=batch_size)
images_test, labels_test = cifar10_input.inputs(eval_data=True,
data_dir=data_dir,
batch_size=batch_size)
image_holder = tf.placeholder(tf.float32, [batch_size, 24, 24, 3])
label_holder = tf.placeholder(tf.float32, [batch_size])
#第一层
#输入为 24*24*3
weight1 = variable_with_weight_loss(shape=[7,7, 3, 64], stddev=0.05, w1=0)
kernel1 = tf.nn.conv2d(image_holder, weight1, [1,2, 2, 1], padding='SAME')
bias1 = tf.Variable(tf.constant(0.0, shape=[64]))
# conv1 = tf.nn.relu(tf.nn.bias_add(kernel1,bias1))
conv1 = tf.nn.relu(kernel1 + bias1) #12*12*64
pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75) #6*6*64
#第二层 输入6*6*64
weight2 = variable_with_weight_loss(shape=[1, 1, 64, 64], stddev=0.05, w1=0.0)
kernel2 = tf.nn.conv2d(norm1, weight2, [1, 1, 1, 1], padding='SAME')
bias2 = tf.Variable(tf.constant(0.1, shape=[64]))
conv2 = tf.nn.relu(kernel2 + bias2) #6*6*64
#第三层 输入6*6*128
weight3 = variable_with_weight_loss(shape=[3,3 , 64, 64], stddev=0.05, w1=0.0)
kernel3 = tf.nn.conv2d(conv2, weight3, [1, 1, 1, 1], padding='SAME')
bias3 = tf.Variable(tf.constant(0.1, shape=[64]))
conv3= tf.nn.relu(kernel3 + bias3) #6*6*64
#第四层
weight4 = variable_with_weight_loss(shape=[1,1 , 64, 256], stddev=0.05, w1=0.0)
kernel4 = tf.nn.conv2d(conv3, weight4, [1, 1, 1, 1], padding='SAME')
bias4 = tf.Variable(tf.constant(0.1, shape=[256]))
conv4= tf.nn.relu(kernel4+ bias4) #6*6*256
#第五层 第二次第二层
weight5 = variable_with_weight_loss(shape=[1, 1, 256, 64], stddev=0.05, w1=0.0)
kernel5 = tf.nn.conv2d(conv4, weight5, [1, 1, 1, 1], padding='SAME')
bias5 = tf.Variable(tf.constant(0.1, shape=[64]))
conv5 = tf.nn.relu(kernel5 + bias5) #6*6*64
#第六层 第二次第三层
weight6 = variable_with_weight_loss(shape=[3, 3, 64, 64], stddev=0.05, w1=0.0)
kernel6 = tf.nn.conv2d(conv5, weight6, [1, 1, 1, 1], padding='SAME')
bias6 = tf.Variable(tf.constant(0.1, shape=[64]))
conv6 = tf.nn.relu(kernel6 + bias6) #6*6*64
#第七层 第二次第四层
weight7 = variable_with_weight_loss(shape=[1,1 , 64, 256], stddev=0.05, w1=0.0)
kernel7 = tf.nn.conv2d(conv6, weight7, [1, 1, 1, 1], padding='SAME')
bias7 = tf.Variable(tf.constant(0.1, shape=[256]))
conv7= tf.nn.relu(kernel7+ bias7) #6*6*256
#第八层 第三次第二层
weight8 = variable_with_weight_loss(shape=[1, 1, 256, 64], stddev=0.05, w1=0.0)
kernel8 = tf.nn.conv2d(conv7, weight8, [1, 1, 1, 1], padding='SAME')
bias8 = tf.Variable(tf.constant(0.1, shape=[64]))
conv8 = tf.nn.relu(kernel8 + bias8) #6*6*64
#第八层 第三次第三层
weight9 = variable_with_weight_loss(shape=[3, 3, 64, 64], stddev=0.05, w1=0.0)
kernel9 = tf.nn.conv2d(conv8, weight9, [1, 1, 1, 1], padding='SAME')
bias9 = tf.Variable(tf.constant(0.1, shape=[64]))
conv9 = tf.nn.relu(kernel9 + bias9) #6*6*64
#第八层 第三次第四层
weight10 = variable_with_weight_loss(shape=[1,1 , 64, 256], stddev=0.05, w1=0.0)
kernel10 = tf.nn.conv2d(conv9, weight10, [1, 1, 1, 1], padding='SAME')
bias10 = tf.Variable(tf.constant(0.1, shape=[256]))
conv10= tf.nn.relu(kernel10+ bias10) #6*6*256
#常规卷积
def con_wy(input,shape_weight,shape_bias,strides):
weight = variable_with_weight_loss(shape_weight, stddev=0.05, w1=0.0)
kernel = tf.nn.conv2d(input, weight, strides, padding='SAME')
bias = tf.Variable(tf.constant(0.1, shape=[shape_bias]))
conv= tf.nn.relu(kernel + bias)
return conv
#空洞卷积
def atrous_conv_wy(input,shape_weight,shape_bias,rate):
weight = variable_with_weight_loss(shape_weight, stddev=0.05, w1=0.0)
kernel = tf.nn.atrous_conv2d(input, weight, rate=rate, padding='SAME') # 空洞卷积会把上面的卷积核编程 10*10的大小
bias = tf.Variable(tf.constant(0.1, shape=[shape_bias])) # 输出维度128
conv = tf.nn.relu(kernel + bias) # 输出为b*7*7*128
return conv
#第九到十七层
conv11 = con_wy(input=conv10,shape_weight=[1,1,256,128],shape_bias=128,strides=[1,1,1,1]) #6*6*128
conv12 = con_wy(input=conv11,shape_weight=[3,3,128,128],shape_bias=128,strides=[1,1,1,1]) #6*6*128
conv13 = con_wy(input=conv12,shape_weight=[1,1,128,512],shape_bias=512,strides=[1,1,1,1]) #6*6*512
conv14 = con_wy(input=conv13,shape_weight=[1,1,512,128],shape_bias=128,strides=[1,1,1,1]) #6*6*128
conv15 = con_wy(input=conv14,shape_weight=[3,3,128,128],shape_bias=128,strides=[1,1,1,1]) #6*6*128
conv16 = con_wy(input=conv15,shape_weight=[1,1,128,512],shape_bias=512,strides=[1,1,1,1]) #6*6*512
conv17 = con_wy(input=conv16,shape_weight=[1,1,512,128],shape_bias=128,strides=[1,1,1,1]) #6*6*128
conv18 = con_wy(input=conv17,shape_weight=[3,3,128,128],shape_bias=128,strides=[1,1,1,1]) #6*6*128
conv19 = con_wy(input=conv18,shape_weight=[1,1,128,512],shape_bias=512,strides=[1,1,1,1]) #6*6*512
conv20 = con_wy(input=conv19,shape_weight=[1,1,512,128],shape_bias=128,strides=[1,1,1,1]) #6*6*128
conv21 = con_wy(input=conv20,shape_weight=[3,3,128,128],shape_bias=128,strides=[1,1,1,1]) #6*6*128
conv22 = con_wy(input=conv21,shape_weight=[1,1,128,512],shape_bias=512,strides=[1,1,1,1]) #6*6*512
conv23 = con_wy(input=conv22,shape_weight=[1,1,512,128],shape_bias=128,strides=[1,1,1,1]) #6*6*128
conv24 = con_wy(input=conv23,shape_weight=[3,3,128,128],shape_bias=128,strides=[1,1,1,1]) #6*6*128
conv25 = con_wy(input=conv24,shape_weight=[1,1,128,512],shape_bias=512,strides=[1,1,1,1]) #6*6*512
#第十八到
# def atrous_conv_wy(input,shape_weight,shape_bias,rate):
# weight = variable_with_weight_loss(shape_weight, stddev=0.05, w1=0.0)
# kernel = tf.nn.atrous_conv2d(input, weight, rate=rate, padding='SAME') # 空洞卷积会把上面的卷积核编程 10*10的大小
# bias = tf.Variable(tf.constant(0.1, shape=[shape_bias])) # 输出维度128
# conv = tf.nn.relu(kernel + bias) # 输出为b*7*7*128
# return conv
#第十八层到三十七层
conv26 = con_wy(input=conv25,shape_weight=[1,1,512,256],shape_bias=256,strides=[1,1,1,1])#6*6*256
conv27 = atrous_conv_wy(input=conv26,shape_weight=[3,3,256,256],shape_bias=256,rate=2) #6*6*256
conv28 = con_wy(input=conv27,shape_weight=[1,1,256,1024],shape_bias=1024,strides=[1,1,1,1])#6*6*1024
conv29 = con_wy(input=conv28,shape_weight=[1,1,1024,256],shape_bias=256,strides=[1,1,1,1])#6*6*256
conv30 = atrous_conv_wy(input=conv29,shape_weight=[3,3,256,256],shape_bias=256,rate=2) #6*6*256
conv31 = con_wy(input=conv30,shape_weight=[1,1,256,1024],shape_bias=1024,strides=[1,1,1,1])#6*6*1024
conv32 = con_wy(input=conv31,shape_weight=[1,1,1024,256],shape_bias=256,strides=[1,1,1,1])#6*6*256
conv33 = atrous_conv_wy(input=conv32,shape_weight=[3,3,256,256],shape_bias=256,rate=2) #6*6*256
conv34 = con_wy(input=conv33,shape_weight=[1,1,256,1024],shape_bias=1024,strides=[1,1,1,1])#6*6*1024
conv35 = con_wy(input=conv34,shape_weight=[1,1,1024,256],shape_bias=256,strides=[1,1,1,1])#6*6*256
conv36 = atrous_conv_wy(input=conv35,shape_weight=[3,3,256,256],shape_bias=256,rate=2) #6*6*256
conv37 = con_wy(input=conv36,shape_weight=[1,1,256,1024],shape_bias=1024,strides=[1,1,1,1])#6*6*1024
conv38 = con_wy(input=conv37,shape_weight=[1,1,1024,256],shape_bias=256,strides=[1,1,1,1])#6*6*256
conv39 = atrous_conv_wy(input=conv38,shape_weight=[3,3,256,256],shape_bias=256,rate=2) #6*6*256
conv40 = con_wy(input=conv39,shape_weight=[1,1,256,1024],shape_bias=1024,strides=[1,1,1,1])#6*6*1024
conv41 = con_wy(input=conv40,shape_weight=[1,1,1024,256],shape_bias=256,strides=[1,1,1,1])#6*6*256
conv42 = atrous_conv_wy(input=conv41,shape_weight=[3,3,256,256],shape_bias=256,rate=2) #6*6*256
conv43 = con_wy(input=conv42,shape_weight=[1,1,256,1024],shape_bias=1024,strides=[1,1,1,1])#6*6*1024
conv44 = con_wy(input=conv43,shape_weight=[1,1,1024,256],shape_bias=256,strides=[1,1,1,1])#6*6*256
conv45 = atrous_conv_wy(input=conv44,shape_weight=[3,3,256,256],shape_bias=256,rate=2) #6*6*256
conv46 = con_wy(input=conv45,shape_weight=[1,1,256,1024],shape_bias=1024,strides=[1,1,1,1])#6*6*1024
#第三十八到四十八层
conv47 = con_wy(input=conv46,shape_weight=[1,1,1024,512],shape_bias=512,strides=[1,1,1,1]) #6*6*512
conv48 = atrous_conv_wy(input=conv47,shape_weight=[3,3,512,512],shape_bias=512,rate=4) #6*6*512
conv49 = con_wy(input=conv48,shape_weight=[1,1,512,2048],shape_bias=2048,strides=[1,1,1,1])#6*6*2048
conv50 = con_wy(input=conv49,shape_weight=[1,1,2048,512],shape_bias=512,strides=[1,1,1,1]) #6*6*512
conv51 = atrous_conv_wy(input=conv50,shape_weight=[3,3,512,512],shape_bias=512,rate=4) #6*6*512
conv52 = con_wy(input=conv51,shape_weight=[1,1,512,2048],shape_bias=2048,strides=[1,1,1,1])#6*6*2048
conv53 = con_wy(input=conv52,shape_weight=[1,1,2048,512],shape_bias=512,strides=[1,1,1,1]) #6*6*512
conv54 = atrous_conv_wy(input=conv53,shape_weight=[3,3,512,512],shape_bias=512,rate=4) #6*6*512
conv55 = con_wy(input=conv54,shape_weight=[1,1,512,2048],shape_bias=2048,strides=[1,1,1,1])#6*6*2048
#全链接
reshape = tf.reshape(conv11, [batch_size, -1]) #分开成为
dim = reshape.get_shape()[1].value #
weight3 = variable_with_weight_loss([dim, 384], stddev=0.04, w1=0.004)
bias3 = tf.Variable(tf.constant(0.1, shape=[384]))
local3 = tf.nn.relu(tf.matmul(reshape, weight3) + bias3)
#全连接层
weight4 = variable_with_weight_loss([384, 192], stddev=0.04, w1=0.004)
bias4 = tf.Variable(tf.constant(0.1, shape=[192]))
local4 = tf.nn.relu(tf.matmul(local3, weight4) + bias4)
#输出层
weight5 = variable_with_weight_loss([192, 10], stddev=1 / 192, w1=0.0)
bias5 = tf.Variable(tf.constant(0.0, shape=[10]))
logits = tf.matmul(local4, weight5) + bias5
def loss(logits, labels):
labels = tf.cast(labels, tf.int64)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels,
name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
return tf.add_n(tf.get_collection('losses'), name='total_loss')
loss_value = loss(logits, label_holder)
optimizer = tf.train.AdamOptimizer()
grads_and_vars = optimizer.compute_gradients(loss_value)
top_k_op = tf.nn.in_top_k(logits, tf.cast(label_holder, tf.int64), 1)
if issync == 1:
# 同步模式计算更新梯度
rep_op = tf.train.SyncReplicasOptimizer(optimizer,
replicas_to_aggregate=1,
# replica_id=FLAGS.task_index,
total_num_replicas=1,
use_locking=True)
train_op = rep_op.apply_gradients(grads_and_vars,
global_step=global_step)
init_token_op = rep_op.get_init_tokens_op()
chief_queue_runner = rep_op.get_chief_queue_runner()
else:
# 异步模式计算更新梯度
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
init_op = tf.initialize_all_variables()
# saver = tf.train.Saver()
tf.summary.scalar('cost', loss_value)
summary_op = tf.summary.merge_all()
sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
# logdir="./checkpoint/",
init_op=init_op,
summary_op=None,
# saver=saver,
global_step=global_step,
# save_model_secs=60
)
with sv.prepare_or_wait_for_session(server.target) as sess:
# 如果是同步模式
if FLAGS.task_index == 0 and issync == 1:
sv.start_queue_runners(sess, [chief_queue_runner])
sess.run(init_token_op)
# sess = tf.InteractiveSession()
# tf.global_variables_initializer().run()
# tf.train.start_queue_runners()
for step in range(max_steps):
start_time = time.time()
image_batch, label_batch = sess.run([images_train, labels_train])
_, loss = sess.run([train_op, loss_value], feed_dict={image_holder: image_batch, label_holder: label_batch})
duration = time.time() - start_time
if step % 10 == 0:
examples_per_sec = batch_size / duration
sec_per_batch = float(duration)
format_str = ('step %d,loss=%.2f (%.1f examples/sec;%.3f sec/batch)')
print(format_str % (step, loss, examples_per_sec, sec_per_batch))
num_examples = 10000
import math
num_iter = int(math.ceil(num_examples / batch_size))
true_count = 0
total_sample_count = num_iter * batch_size
step = 0
# with tf.Session() as sess:
while step < num_iter:
image_batch, label_batch = sess.run([images_test, labels_test])
predictions = sess.run([top_k_op], feed_dict={image_holder: image_batch,
label_holder: label_batch})
true_count += np.sum(predictions)
step += 1
if step % 10 == 0:
print(true_count)
end = time.clock() # 计算程序结束时间
out = (end - start)
precision = float(true_count) / total_sample_count
print('precision @ 1 =%.3f' % precision)
print("running time is", out, "s")
if __name__ == "__main__":
tf.app.run()