from model import *
import sys

# start the training process some arguments
embednet = EmbedNet(tf.Session())
restart = True

def train_model(embednet, epoch=3000):

  if restart:
    embednet.initialize()
  else:
    embednet.initialize()
    embednet.load_model('./models/embednet.ckpt')

  for i in xrange(epoch):
    embednet.train(rand_datas(N_BATCH))

    if i % 20 == 0:
      embednet.save()

train_model(embednet, 10000)


b5129e93d058277c684f7c9a3e9058ff379c6e4a
<mask token>


def train_model(embednet, epoch=3000):
    if restart:
        embednet.initialize()
    else:
        embednet.initialize()
        embednet.load_model('./models/embednet.ckpt')
    for i in xrange(epoch):
        embednet.train(rand_datas(N_BATCH))
        if i % 20 == 0:
            embednet.save()


<mask token>

<mask token>


def train_model(embednet, epoch=3000):
    if restart:
        embednet.initialize()
    else:
        embednet.initialize()
        embednet.load_model('./models/embednet.ckpt')
    for i in xrange(epoch):
        embednet.train(rand_datas(N_BATCH))
        if i % 20 == 0:
            embednet.save()


train_model(embednet, 10000)

<mask token>
embednet = EmbedNet(tf.Session())
restart = True


def train_model(embednet, epoch=3000):
    if restart:
        embednet.initialize()
    else:
        embednet.initialize()
        embednet.load_model('./models/embednet.ckpt')
    for i in xrange(epoch):
        embednet.train(rand_datas(N_BATCH))
        if i % 20 == 0:
            embednet.save()


train_model(embednet, 10000)

from model import *
import sys
embednet = EmbedNet(tf.Session())
restart = True


def train_model(embednet, epoch=3000):
    if restart:
        embednet.initialize()
    else:
        embednet.initialize()
        embednet.load_model('./models/embednet.ckpt')
    for i in xrange(epoch):
        embednet.train(rand_datas(N_BATCH))
        if i % 20 == 0:
            embednet.save()


train_model(embednet, 10000)

from model import *
import sys

# start the training process some arguments
embednet = EmbedNet(tf.Session())
restart = True

def train_model(embednet, epoch=3000):

  if restart:
    embednet.initialize()
  else:
    embednet.initialize()
    embednet.load_model('./models/embednet.ckpt')

  for i in xrange(epoch):
    embednet.train(rand_datas(N_BATCH))

    if i % 20 == 0:
      embednet.save()

train_model(embednet, 10000)

