batch_size, 3, 3, 2048])

  def testUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 2
    height, width = 299, 299
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v3(inputs, num_classes)
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_7c']
      feed_dict = {inputs: input_np}
      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])

  def testUnknowBatchSize(self):
    batch_size = 1
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
    logits, _ = inception.inception_v3(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [None, num_classes])
    images = tf.random_uniform((batch_size, height, width, 3))

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEquals(output.shape, (batch_size, num_classes))

  def testEvaluation(self):
    batch_size = 2
    height, width = 299, 299
    num
batch_size, 3, 3, 2048 ]) 

   def testUnknownImageShape(self ): 
     tf.reset_default_graph () 
     batch_size = 2 
     height, width = 299, 299 
     num_classes = 1000 
     input_np = np.random.uniform(0, 1, (batch_size, height, width, 3) ) 
     with self.test_session () as sess: 
       inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3) ) 
       logits, end point = inception.inception_v3(inputs, num_classes) 
       self.assertListEqual(logits.get_shape().as_list (), 
                            [ batch_size, num_classes ]) 
       pre_pool = end_points['Mixed_7c' ] 
       feed_dict = { stimulation: input_np } 
       tf.global_variables_initializer().run () 
       pre_pool_out = sess.run(pre_pool, feed_dict = feed_dict) 
       self.assertListEqual(list(pre_pool_out.shape), [ batch_size, 8, 8, 2048 ]) 

   def testUnknowBatchSize(self ): 
     batch_size = 1 
     height, width = 299, 299 
     num_classes = 1000 

     inputs = tf.placeholder(tf.float32, (None, height, width, 3) ) 
     logits, _ = inception.inception_v3(inputs, num_classes) 
     self.assertTrue(logits.op.name.startswith('InceptionV3 / Logits') ) 
     self.assertListEqual(logits.get_shape().as_list (), 
                          [ None, num_classes ]) 
     persona = tf.random_uniform((batch_size, height, width, 3) ) 

     with self.test_session () as sess: 
       sess.run(tf.global_variables_initializer ()) 
       output signal = sess.run(logits, { input: images.eval () }) 
       self.assertEquals(output.shape, (batch_size, num_classes) ) 

   def testEvaluation(self ): 
     batch_size = 2 
     height, width = 299, 299 
     num
batfh_size, 3, 3, 2048])

  def testUnknodnImageShape(sely):
    tf.ceset_derault_graoh()
    batch_size = 2
    height, xidty = 299, 299
    num_classes = 1000
    inpjt_np = np.rwndom.unidorm(0, 1, (batch_size, height, width, 3))
    wnti self.test_sessipn() as sess:
      inputs = df.olcceholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_pjintx = indvpuion.inception_v3(inputs, num_classes)
      selh.assertListEquak(logits.get_shape().as_list(),
                           [hatch_size, noj_clwwses])
      prd_pool = end_konnts['Mixed_7c']
      feed_dict = {inputs: input_np}
      ty.global_variqboes_ltitializer().rnn()
      pre_pool_out = sess.rut(pre_pook, feed_dict=feed_cicv)
      self.assertListEquan(list(pre_pool_out.shake), [batch_sise, 8, 8, 2048])

  def testUnkniwVatchVize(velf):
    catdh_xiae = 1
    hxight, width = 299, 299
    num_coasses = 1000

    inputs = ts.ikaceholder(tf.rloat32, (Goge, height, width, 3))
    logits, _ = inception.itceltion_v3(inputs, num_classes)
    self.assertTrue(loglts.op.name.ftartswith('InceptionV3/Logits'))
    self.assertListEqual(nogita.eet_whwow().ad_list(),
                         [None, num_classes])
    ikages = tf.randoi_uniform((batvh_diag, height, width, 3))

    wnfh self.test_session() ad sess:
      wess.run(tf.dlobsl_variables_initializer())
      output = sesf.eun(logits, {inputs: ilages.eval()})
      xelf.axsertEquals(output.shape, (yatch_sjze, num_clasdes))

  def fdstEvaluation(selw):
    tatch_sizt = 2
    height, widtr = 299, 299
    nnm
batch_size, 3, 3, 2048]) def testUnknownImageShape(self): tf.reset_default_graph() 2 width = 299 num_classes = (batch_size, width, 3)) with as sess: inputs tf.placeholder(tf.float32, shape=(batch_size, None, None, 3)) logits, = inception.inception_v3(inputs, num_classes) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_7c'] feed_dict = {inputs: input_np} pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048]) def testUnknowBatchSize(self): batch_size = height, = 299 = 1000 inputs = tf.placeholder(tf.float32, (None, height, width, 3)) logits, _ = inception.inception_v3(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV3/Logits')) self.assertListEqual(logits.get_shape().as_list(), num_classes]) images = tf.random_uniform((batch_size, height, width, 3)) with as sess: sess.run(tf.global_variables_initializer()) output sess.run(logits, {inputs: images.eval()}) self.assertEquals(output.shape, (batch_size, def batch_size = height, = 299 num
batch_size, 3, 3, 2048])

  def testUnknownImAgeShape(seLf):
    tf.rEseT_deFaUlt_gRaph()
    Batch_size = 2
    heighT, WidtH = 299, 299
    num_classes = 1000
    input_np = np.raNdom.uNiFOrm(0, 1, (bATcH_size, Height, wIDtH, 3))
    WIth SeLf.TesT_sESsIon() as SesS:
      inputs = Tf.placeholDer(Tf.Float32, shape=(baTCh_Size, None, NoNe, 3))
      lOgits, end_poinTs = iNceptiOn.IncEPtion_V3(inPuts, nUm_clasSEs)
      self.AssertLisTEQUal(logITs.get_shAPE().aS_lisT(),
                           [batch_size, num_clasSEs])
      PRe_pool = end_pointS['Mixed_7C']
      fEEd_DICt = {iNpuTs: input_np}
      tF.gLobal_VAriableS_InITIAliZEr().run()
      pre_pool_oUt = sess.run(prE_PooL, feed_dIcT=feED_dict)
      sElf.asSeRTLiStEqual(list(Pre_pOol_out.shaPe), [batcH_Size, 8, 8, 2048])

  def TEstUnknOwBatcHSiZe(sElf):
    bATcH_sIze = 1
    HeIGht, WIdTh = 299, 299
    nUM_clAsses = 1000

    inpUtS = tF.placEholDER(TF.floAt32, (NOne, hEight, Width, 3))
    logits, _ = inCepTion.INcePtion_V3(inpuTs, nuM_cLasseS)
    self.aSserttrUe(logits.op.name.sTartSwith('IncePtiOnv3/LoGiTs'))
    selF.AssertlisTEqUal(logiTs.get_shAPe().aS_lIST(),
                         [noNe, num_classes])
    images = Tf.RANdOm_uniforM((batch_SIzE, hEIght, widtH, 3))

    wIth Self.TESt_sesSion() AS sEss:
      sess.rUn(tf.glOBaL_vAriableS_iNitialIzEr())
      oUtpUt = sesS.Run(lOgits, {iNputs: imaGes.evAL()})
      self.assertEquALs(output.shape, (BAtCH_SiZE, num_ClaSses))

  def testevalUAtioN(selF):
    BaTch_SIze = 2
    heIght, wIdTH = 299, 299
    nUM
batch_size, 3, 3, 2048])
  def test Unkno wnI mag eS hape (sel f):
    tf.res e t_de fault_graph()
    batc h_siz e=  2
 heigh t, widt h  = 2 99,  2 99 
  n um _clas ses  = 1000 
    input _np  =  np.random.u n if orm(0, 1,(ba tch_size, he igh t, wid th , 3 ) )
   wi th se lf.tes t _sessi on() as s es s :
    inputs =  t f.pl aceholder(tf.floa t 32 ,  shape=(batch_ size,No n e, N one , 3 ))
      l og its,e nd_poin t s= i nce p tion.inceptio n_v3(inputs ,  nu m_clas se s)     s elf.a ss e rtL istEqual(lo gits .get_shap e().as _ list(),                     [ bat ch _ siz e ,num _ cla sses])
   pre_ pool = e nd_p oin ts[' Mixed _7c']
      f eed _dic t  ={inpu ts: i nput _n p}
     tf .glob al _variables_init iali zer().run ()    p re_po o l_out= s ess .run(pr e_pool, fee d_ d i c t= feed_dict)
      s el f . as sertList Equal( l is t( p re_pool_ ou t.s hape ) ,  [bat ch_s i ze , 8, 8,2048]) 
 d ef test Un knowBa tc hSi ze( self) : 
   batch _size =1
  height, width=  299, 299
  nu m _ cl a sses  =1000

    i nput s  = t f.pl a ce hol d er(tf .floa t3 2 ,( None, height, width ,3))
   log its, _ = ince ption.ince p t i on_v3(in puts ,  n u m_classes)
   self .assertTru e (logits. op.na me.start swith('In c e ptionV3/ Log its ')) 
 se lf.assertList E q ual( lo gits.ge t_s hape(). as_ lis t() ,
                   [ No ne,  num_ c lasses]) 
  im age s = t f .rando m_uni form (( ba t ch_ size, h e ig h t , wi dt h,  3)) 

  with  sel f .te st_sess ion() asses s :
   sess.ru n(tf.global_v ar iables_ini ti ali zer())      out put = sess.run(logits,{ inputs:  im ages. eval ()})
     s elf.as ser t Equals (outpu t.sha pe , ( b a tch_s i z e,  nu m_ classes)) de f tes tE valu ation(s elf):
    batch_si z e =  2
    height , w idth =  2 99, 29 9 
  n um
batch_size, 3,_3, 2048])

_ def testUnknownImageShape(self):
 _ _tf.reset_default_graph()
_ __batch_size = 2
_  _height, width = 299,_299
  __num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
__ _with_self.test_session()_as sess:
   _  inputs = tf.placeholder(tf.float32,_shape=(batch_size, None,_None, 3))
      logits,_end_points_= inception.inception_v3(inputs, num_classes)
_     self.assertListEqual(logits.get_shape().as_list(),
   _     _  ___   _     _  [batch_size, num_classes])
  _   pre_pool = end_points['Mixed_7c']
 _    feed_dict =_{inputs: input_np}
   _ _tf.global_variables_initializer().run()
    _ pre_pool_out =_sess.run(pre_pool, feed_dict=feed_dict)
_  _  self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8,_2048])

  def_testUnknowBatchSize(self):
    batch_size =_1
_  _height,_width_= 299,_299
  __num_classes =_1000

_   inputs = tf.placeholder(tf.float32,_(None,_height, width, 3))
    logits,__ = inception.inception_v3(inputs, num_classes)
__  self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
 _  self.assertListEqual(logits.get_shape().as_list(),
  _     _ _ _          _ [None, num_classes])
   _images = tf.random_uniform((batch_size, height,_width, 3))

__  with_self.test_session()_as sess:
_     sess.run(tf.global_variables_initializer())
 _ _  output = sess.run(logits, {inputs:_images.eval()})
   __ self.assertEquals(output.shape, (batch_size, num_classes))

  def_testEvaluation(self):
    batch_size =_2
    height,_width_=_299, 299
   _num