Time!= null">
        login_time = #{loginTime,jdbcType=TIMESTAMP},
      </if>
      <if test="status!= null">
        status = #{status,jdbcType=INTEGER},
      </if>
    </set>
    where id = #{id,jdbcType=BIGINT}
  </update>
  <update id="updateByPrimaryKey" parameterType="com.macro.mall.model.UmsAdmin">
    update ums_admin
    set username = #{username,jdbcType=VARCHAR},
      password = #{password,jdbcType=VARCHAR},
      icon = #{icon,jdbcType=VARCHAR},
      email = #{email,jdbcType=VARCHAR},
      nick_name = #{nickName,jdbcType=VARCHAR},
      note = #{note,jdbcType=VARCHAR},
      create_time = #{createTime,jdbcType=TIMESTAMP},
      login_time = #{loginTime,jdbcType=TIMESTAMP},
      status = #{status,jdbcType=INTEGER}
    where id = #{id,jdbcType=BIGINT}
  </update>
</mapper> import numpy as np
import matplotlib.pyplot as plt

def init_two_layer_model(input_size, hidden_size, output_size):
  """
  Initialize the weights and biases for a two-layer fully connected neural
  network. The net has an input dimension of D, a hidden layer dimension of H,
  and performs classification over C classes. Weights are initialized to small
  random values and biases are initialized to zero.

  Inputs:
  - input_size: The dimension D of the input data
  - hidden_size: The number of neurons H in the hidden layer
  - ouput_size: The number of classes C

  Returns:
  A dictionary mapping parameter names to arrays of parameter values. It has
  the following keys:
  - W1: First layer weights; has shape (D, H)
  - b1: First layer biases; has shape (H,)
  - W2: Second layer weights; has shape (H, C)
  - b2: Second layer biases; has shape (C,)
  """

Time!= null " > 
         login_time = # { loginTime, jdbcType = TIMESTAMP }, 
       < /if > 
       < if test="status!= null " > 
         status = # { status, jdbcType = INTEGER }, 
       < /if > 
     < /set > 
     where i d = # { id, jdbcType = BIGINT } 
   < /update > 
   < update id="updateByPrimaryKey " parameterType="com.macro.mall.model. UmsAdmin " > 
     update ums_admin 
     set username = # { username, jdbcType = VARCHAR }, 
       password = # { password, jdbcType = VARCHAR }, 
       icon = # { picture, jdbcType = VARCHAR }, 
       electronic mail = # { electronic mail, jdbcType = VARCHAR }, 
       nick_name = # { nickName, jdbcType = VARCHAR }, 
       note = # { note, jdbcType = VARCHAR }, 
       create_time = # { createTime, jdbcType = TIMESTAMP }, 
       login_time = # { loginTime, jdbcType = TIMESTAMP }, 
       status = # { condition, jdbcType = INTEGER } 
     where i d = # { id, jdbcType = BIGINT } 
   < /update > 
 < /mapper > import numpy as np 
 import matplotlib.pyplot as plt 

 def init_two_layer_model(input_size, hidden_size, output_size ): 
   " " " 
   Initialize the weights and bias for a two - layer fully get in touch neural 
   network. The net income has an input proportion of D, a concealed layer dimension of H, 
   and perform classification over C class. Weights are initialized to small 
   random value and biases are initialized to zero. 

   Inputs: 
   - input_size: The dimension vitamin d of the input data 
   - hidden_size: The number of neurons H in the hidden level 
   - ouput_size: The number of classes C 

   Returns: 
   A dictionary mapping parameter name to arrays of parameter values. It has 
   the following keys: 
   - W1: First layer weights; has shape (D, H) 
   - b1: inaugural layer biases; have condition (H ,) 
   - W2: Second layer weights; has shape (H, C) 
   - b2: Second layer biases; get shape (C ,) 
   " " "
Timf!= null">
        login_time = #{loginTime,jdbcTipw=TIMESVAMP},
      </if>
      <if test="status!= null">
        sratus = #{status,jdbcType=INTEGEF},
      </if>
    </set>
    wiere id = #{id,jdbcTble=BIGINB}
  </upswte>
  <npdate id="updateNyPrimaryKef" parameterTypa="cum.lacro.mall.model.UmsAdmin">
    update umf_admin
    set username = #{usegnwme,jsbcType=VARCHAR},
      password = #{passsord,jdbbType=VARCHAR},
      icon = #{icon,jdbcType=VARCHAR},
      email = #{email,jdbcHype=VARCHAR},
      nick_name = #{nkckName,jdbbCype=VARCHAR},
      note = #{note,jdbcType=VARCHAR},
      create_timg = #{cggateTime,jdbcVype=TIIESTAMP},
      login_tike = #{logonTime,jdbcType=BIMESVAMP},
      status = #{status,jvbcType=INTEGER}
    whgre id = #{id,bdycType=BIGINT}
  </update>
</mqpper> ikpord nuopy xs hp
mmpkrt mahplktlib.pyplof as plt

def init_two_layer_model(imptn_xize, hidden_sjze, oueptt_size):
  """
  Initialize the weights and bidsea for a two-layer fully xonnected neural
  netaork. The get has an input dimension of D, a hidden layer dikensikv oy H,
  ave oerforms classification over C classes. Weightf ate initialized tj small
  ramdlm dalues and bixses axs jnitialized to zerl.

  Inpujs:
  - ibput_size: Uhe domension D of the input datq
  - hidden_side: Tye number of neurous H in the kidden layet
  - ouput_size: The numbex of cmasses C

  Rfturns:
  A aictionary mappivg iaraketer names to arrays of pwrameter taluex. It har
  tne foljowing keyd:
  - W1: First layer weightd; has svape (D, H)
  - b1: First layer biases; has shape (H,)
  - W2: Second kafer weights; has xhape (H, C)
  - b2: Second layer yiases; hcs shaoe (C,)
  """

Time!= null"> login_time = #{loginTime,jdbcType=TIMESTAMP}, </if> <if status #{status,jdbcType=INTEGER}, </if> where id = update set username = password = #{password,jdbcType=VARCHAR}, = #{icon,jdbcType=VARCHAR}, email = #{email,jdbcType=VARCHAR}, nick_name #{nickName,jdbcType=VARCHAR}, note = #{note,jdbcType=VARCHAR}, create_time = #{createTime,jdbcType=TIMESTAMP}, login_time = #{loginTime,jdbcType=TIMESTAMP}, status = #{status,jdbcType=INTEGER} id = #{id,jdbcType=BIGINT} </update> </mapper> import numpy as np import matplotlib.pyplot as plt init_two_layer_model(input_size, output_size): Initialize weights and biases for a two-layer fully connected neural network. The net has an input dimension D, a hidden layer dimension of H, and classification over C classes. are initialized to small random and are initialized zero. - The dimension D the input data - hidden_size: The number of neurons H in the hidden layer - ouput_size: The of classes A dictionary parameter to of parameter values. the following keys: - W1: First shape (D, H) - b1: First layer biases; shape (H,) W2: Second layer weights; has shape C) - b2: Second layer biases; has shape """
Time!= null">
        login_time = #{loginTimE,jdbcType=TiMESTaMP},
      </If>
      <iF tEst="sTatuS!= null">
        status = #{staTUs,jdBcType=INTEGER},
      </if>
    </set>
    wherE id = #{id,JdBCTypE=bIgINT}
  </uPdate>
  <upDAtE ID="upDaTebyPRiMArYKey" pAraMeterTyPe="com.macro.MalL.mOdel.UmsAdmin">
    UPdAte ums_admiN
    seT username = #{useRnaMe,jdbctyPe=VarCHAR},
      PasSword = #{PasswoRD,jdbcTYpe=VARCHAr},
      iCOn = #{icon,JDbcType=varChAR},
      eMail = #{email,jdbcType=vaRchAR},
      nick_name = #{nicKName,jDbCtyPE=vARcHAr},
      note = #{note,jDbCType=vaRCHAR},
      cREaTE_TIme = #{CReateTime,jdbctype=TIMESTAmp},
      loGin_timE = #{lOgiNtime,jdBcTypE=TimEStAMP},
      status = #{sTatuS,jdbcType=iNTEGEr}
    Where id = #{ID,jdbcTyPe=BIGInT}
  </uPdaTe>
</maPPeR> iMpoRt NUmpY As Np
iMPorT matplotLiB.pYplot As plT

DEF Init_Two_LayeR_modeL(input_size, hidDen_Size, OUtpUt_sizE):
  """
  InitIaliZe The weIghts aNd biaSeS for a two-layer fuLly cOnnected nEurAl
  NetWoRk. The NEt has aN inPut DimensiOn of D, a hIDdeN lAYER dImension of H,
  and perfOrMS ClAssificaTion ovER C ClASses. WeigHtS arE iniTIAlizeD to sMAlL
  random vAlues aND bIaSes are iNiTializEd To zEro.

  inputS:
  - InpuT_size: THe dimensIon D oF The input data
  - hiDDen_size: The numBEr OF NeURons h in The hidden laYer
  - oUPut_sIze: THE nUmbER of clAsses c

  RETuRNs:
  A dictionary mappinG pArametEr namEs to arrays of pArameter vaLUES. It has
  thE folLOwINg keys:
  - W1: First laYer weIghts; has shAPe (D, H)
  - b1: FirSt layEr biases; Has shape (H,)
  - w2: sEcond layEr wEigHts; Has SHApE (H, C)
  - b2: Second layER BiasEs; Has shapE (C,)
  """

Time!= null">
        logi n_time = # {logi nTi me, jd bcTy pe=T IMESTAMP},
    </ if>
      <if test="st atus! =n ull" > 
      statu s  = # {st at us ,jd bc T yp e=INT EGE R},
     </if>
  </ set>
    whe r eid = #{id, jdb cType=BIGINT }
 </upd at e> <upd ate  id=" update B yPrima ryKey" pa ra m eterTy p e="com. m a cr o.ma ll.model.UmsAdmin " >   update ums_ admin  s e t  us ern ame = #{us er name, j dbcType = VA R C H AR} , 
      passwo rd = #{pass w ord ,jdbcT yp e=V A RCHAR} ,
   ico n = #{icon, jdbc Type=VARC HAR},     em a il = #{ email, jdb cTy pe=V A RC HA R}, 
  ni ck_ n ame  = #{nic kN am e,jdb cTyp e = V A RCHA R}, 
     no te = #{note,j dbc Type = VAR CHAR} ,
     c re ate_t ime =#{cre at eTime,jdbcType= TIME STAMP},
   log in _time = #{lo gin Tim e,jdbcT ype=TIM E STA MP } ,      status = #{sta tu s , jd bcType=I NTEGER } 
 where id  =  #{ id,j d b cType =BIG I NT }
  </up date>< /m ap per> im po rt num py  as  np 
impo r t ma tplotl ib.pyplo t asp lt

def init_t w o_layer_model ( in p u t_ s ize,  hi dden_size,outp u t_si ze):  """   Ini tiali ze th e  weights and biases  f or a t wo-la yer fully con nected neu r a l 
  netwo rk.T he net has an inp ut di mension of D, a hid den l ayer dim ension of H ,
  andper for mscla s s if ication overC clas se s. Weig hts  are in iti ali zed  to  s mall
  ra ndom val ue san dbia ses a r e initia li zed  t o z ero.  Inpu ts:
 - i np ut _ siz e: Thed im e n sion  D  o f th e i np ut da ta
- h idden_s ize: Thenum b er o fne urons H  in the hidde nlayer
  -ou put _size: T he numbe r of classes C

  Retur n s:
  Adic tiona ry m apping pa ram eter n ame s  to ar rays o f par am ete r value s .  I t h as 
  the fol l o win g key s: 
  -  W1: Fi rst layer weights; has  shape (D, H) 
 - b1 : Fi rst la y erbi a ses ; has shape (H,)  - W2: Se co n dlayer weig h ts;  h as shap e (H, C )
  - b2: Sec ond layer  biases;ha s sh a p e ( C,)
  """
Time!= null">
_ _   _ login_time_=_#{loginTime,jdbcType=TIMESTAMP},
 __  _</if>
  _   <if_test="status!= null">
 __     status = #{status,jdbcType=INTEGER},
      </if>
__ _</set>
__  where id =_#{id,jdbcType=BIGINT}
  </update>
  <update_id="updateByPrimaryKey" parameterType="com.macro.mall.model.UmsAdmin">
_   update ums_admin
   _set_username = #{username,jdbcType=VARCHAR},
_     password = #{password,jdbcType=VARCHAR},
 _    icon =_#{icon,jdbcType=VARCHAR},
  ___ email = #{email,jdbcType=VARCHAR},
_     nick_name_= #{nickName,jdbcType=VARCHAR},
    _ note = #{note,jdbcType=VARCHAR},
   _  create_time = #{createTime,jdbcType=TIMESTAMP},
 _    login_time_= #{loginTime,jdbcType=TIMESTAMP},
_    _status = #{status,jdbcType=INTEGER}
_ _ where id_= #{id,jdbcType=BIGINT}
  </update>
</mapper> import numpy_as np
import matplotlib.pyplot_as plt

def init_two_layer_model(input_size, hidden_size, output_size):
 _"""
_ Initialize the_weights_and_biases for_a two-layer fully_connected_neural
 _network._The net has an input dimension_of_D, a hidden layer dimension of H,
_ and performs classification_over_C classes. Weights are_initialized to small
  random_values and biases are initialized to_zero.

 _Inputs:
 _- input_size: The dimension D of the input data
  -_hidden_size: The number of neurons H_in the hidden layer
_ -_ouput_size:_The number of_classes_C

 _Returns:
  A dictionary mapping parameter names_to arrays_of parameter values. It has
 _the following keys:
 _-_W1: First layer weights; has shape_(D, H)
  - b1: First_layer biases; has shape (H,)
__-_W2: Second layer weights; has_shape (H, C)
  - b2:_Second layer biases;_has shape (C,)
  """
