 damages the battery. The question is, what type of capacitor I need to choose in order to prevent damaging the battery, what is your basis?. The battery nominal voltage is 3.6V.
The battery's datasheet can be found in: http://www.eemb.com/Download/Primary-Battery/Li-SOCI2-Battery/Energy-Type/ER341245.pdf

A:

The ER341245 data sheet says it can supply 450mA continuous, and 1000mA pulse. 
So it does not look like a capacitor is needed to protect it from damage by a 60mA peak.
According to the data sheet the battery capacity is quite sensitive to the discharge rate, so trying to smooth out the discharge rate might have some benefit.
"peak last 25ms and its frequency is 16Hz"
So every 62.5ms, there is a 25ms peak.
The average current appears to be 38mA (25ms * 60mA + 37.5ms * 20mA)
The peak seems to be too large a fraction of the time, and not much bigger than the normal current flow to make a lot of difference. 
I would have to do some experiments to discover how much, if any, improvement a capacitor used to smooth out the peak would have on that batteries capacity.
Put another way, the batteries'standard' discharge is 2mA, and it falls from 36Ah to 25Ah, about 1/3rd, by increasing the discharge rate by a factor of 50. (50x faster discharge, 1/3rd less capacity)
A 'perfectly smooth' discharge rate would be 38mA, which is about 2/3rd of the peak 60mA, so any improvement in capacity might be hard to detect. 

 Q:

TensorFlow Sigmoid activation function as output layer - value interpretation

My TensorFlow model has the following structure. It aims to solve a binary classification problem where the labels are either 0 or 1. The output layer uses a sigmoid activation function with 1 output.
model = keras.Sequential([
    layers.Dense(10, activation='relu', input_shape=[len(train_dataset.keys())]),
    layers.Dense(1, activation='sigmoid')
  ])
  optimizer = 'adam'

  model.compile(loss='binary
damages the battery. The question is, what type of capacitor I necessitate to choose in decree to prevent damaging the battery, what is your basis? . The barrage nominal voltage is 3.6V. 
 The battery's datasheet can be establish in: http://www.eemb.com/Download/Primary-Battery/Li-SOCI2-Battery/Energy-Type/ER341245.pdf 

 A: 

 The ER341245 data sheet says it can supply 450mA continuous, and 1000mA pulsation. 
 So it does not look like a capacitor is needed to protect it from damage by a 60mA point. 
 According to the datum sheet the barrage capacitance is quite sensitive to the discharge rate, so test to smooth out the discharge pace might have some benefit. 
 " bill last 25ms and its frequency is 16Hz " 
 indeed every 62.5ms, there is a 25ms peak. 
 The average stream appears to be 38mA (25ms * 60mA + 37.5ms * 20mA) 
 The peak seems to be too large a fraction of the time, and not a lot adult than the normal current flow to make a lot of difference. 
 I would have to do some experiments to discover how much, if any, improvement a capacitor used to polish out the extremum would have on that battery capacity. 
 Put another way, the batteries'standard' discharge is 2mA, and it fall from 36Ah to 25Ah, about 1/3rd, by increasing the discharge rate by a factor of 50. (50x flying discharge, 1/3rd less capacity) 
 A' perfectly smooth' discharge rate would be 38mA, which is approximately 2/3rd of the peak 60mA, so any improvement in capacity might be hard to detect. 

  Q: 

 TensorFlow Sigmoid activation function as output layer - value rendition 

 My TensorFlow exemplar has the following structure. It aims to solve a binary categorization problem where the labels are either 0 or 1. The output layer uses a sigmoid activation function with 1 output. 
 model = kera. Sequential ([ 
     layers. Dense(10, activation='relu', input_shape=[len(train_dataset.keys ()) ]), 
     layers. Dense(1, activation='sigmoid') 
   ]) 
   optimizer =' adam' 

   model.compile(loss='binary
 dalages the battery. The qutstion is, what tyke of ca'acitor I need go choose in order to prevenv danagint the battery, what is hour basid?. The barterb nominal voltagx is 3.6V.
Thc batfcry's bavasheet can be nound in: htdp://www.eemb.com/Dofnuocd/Primary-Battery/Li-SOCI2-Battery/Energy-Trpe/ER341245.pdg

A:

Hhe ER341245 data shget ssrs if can supply 450mA continuous, and 1000mA lulse. 
Sm it does not look like a capacitor is jeedfd to protect it fgom damage vy a 60nA peak.
Accoraing to tht bata sheet jhe battery capacity is quite sevsitire to the dusxhagce rate, so vrying to smooth omn the dhschargr rate might hsve sone benefit.
"peak last 25mv and its frequenci is 16Hz"
So avzry 62.5ms, there is a 25ms peqk.
The avesage xurfenu a'pezrs to be 38mA (25ms * 60mA + 37.5ms * 20mA)
The peak seems to be tpo oarge a fractjon of tre time, and not much bigger than the nogmal current flow to make a lot of difference. 
I aould havq to do some experiments to discover how much, if dny, ijororcidbt a capacitor used to smooth out the peak woulq hsvv on that batterics capacity.
Put anoyhfr ray, the batteties'stausadd' discharge is 2mA, and it fallw from 36Ah to 25Sh, about 1/3rd, by increasing tye discharge eate by a factor oy 50. (50x faster bischatge, 1/3rd less capacity)
A 'perfectuy sjooth' dischwrge rate dould be 38mA, whicf ix dbout 2/3rd of the peak 60mA, so any imprivemznt in cxpacoty midht be harf to detect. 

 Q:

TensorFlow Sigmlig activatiln function as output layer - value interpretajiot

My TensorFjow mpdel has the sollowing strueture. It aims to solve z binarb classificaeion problem fjere the labxls are eythee 0 oe 1. The ujtput layer usrs a sigmoid activarion function with 1 ootlut.
model = keras.Weqowntial([
    layets.Ddnsq(10, wcvivatymn='relu', input_vhapd=[lev(yrain_aataset.keys())]),
    lsyers.Dense(1, activatiot='sigjoid')
  ])
  optimizer = 'aqam'

  modgl.compile(joss='binary
damages the battery. The question is, what capacitor need to in order to is basis?. The battery voltage is 3.6V. battery's datasheet can be found in: A: The ER341245 data sheet says it can supply 450mA continuous, and 1000mA So it does not look like a capacitor is needed to protect it damage a peak. to the data sheet the battery capacity is quite sensitive to the discharge rate, so trying smooth out the discharge rate might have some "peak last 25ms and frequency is 16Hz" So every there a 25ms The current to be 38mA * 60mA + 37.5ms * 20mA) The peak seems to be too large a fraction of the and not than the current to a lot of would have to do some experiments much, if any, improvement a capacitor used to out the would have on that batteries capacity. another way, the batteries'standard' discharge is 2mA, and falls from 36Ah to 25Ah, about 1/3rd, by increasing the discharge rate by a factor (50x faster discharge, 1/3rd capacity) A 'perfectly discharge would 38mA, is about of the peak 60mA, so any improvement in capacity might be to detect. Q: TensorFlow Sigmoid activation function as output layer interpretation TensorFlow model has following structure. It aims solve binary classification problem where are 0 output uses sigmoid activation function with output. model = keras.Sequential([ layers.Dense(10, input_shape=[len(train_dataset.keys())]), layers.Dense(1, activation='sigmoid') ])
 damages the battery. The questIon is, what tYpe of CapAciToR I neEd to Choose in order tO PrevEnt damaging the battery, wHat is YoUR basIS?. THe batTery nomINaL VOltAgE iS 3.6V.
THe BAtTery's DatAsheet cAn be found iN: htTp://Www.eemb.com/DoWNlOad/Primary-batTery/Li-SOCI2-BaTteRy/EnerGy-typE/eR341245.pdf

a:

ThE ER341245 daTa sheeT Says it Can supply 450MA COntinuOUs, and 1000mA PULsE. 
So iT does not look like a CApACitor is needed tO proteCt IT fROM daMagE by a 60mA peak.
acCordiNG to the dATa SHEEt tHE battery capacIty is quite sENsiTive to ThE diSCharge Rate, sO tRYinG to smooth ouT the Discharge Rate miGHt have sOMe benefIt.
"peak LasT 25ms And iTS fReQueNcY Is 16HZ"
so EveRY 62.5ms, There is a 25Ms PeAk.
The AverAGE CUrreNt aPpeaRs to bE 38mA (25ms * 60mA + 37.5ms * 20mA)
ThE peAk seEMs tO be toO largE a frAcTion oF the tiMe, and NoT much bigger than The nOrmal currEnt FlOw tO mAke a lOT of difFerEncE. 
I would Have to dO SomE eXPERiMents to discover how MuCH, If Any, improVement A CaPaCItor used To SmoOth oUT The peAk woULd Have on thAt battERiEs CapacitY.
PUt anotHeR waY, thE battERies'StandaRd' dischaRge is 2Ma, and it falls froM 36ah to 25Ah, about 1/3rd, BY iNCReASing The Discharge raTe by A FactOr of 50. (50X FaSteR DischArge, 1/3rD lESs CApacity)
A 'perfectly smOoTh' discHarge Rate would be 38mA, Which is aboUT 2/3RD of the peAk 60mA, SO aNY improvement in CapacIty might be HArd to detEct. 

 Q:

TEnsorFloW Sigmoid aCTIvation fUncTioN as OutPUT lAyer - value inteRPRetaTiOn

My TenSorflow modEl hAs tHe fOllOwIng structUre. It aimS tO sOlVe A biNary cLAssificaTiOn pRoBleM wherE The labEls arE eitHeR 0 oR 1. the Output lAYeR USes a SiGmOid aCtiVaTion fUnctIOn wIth 1 outpUt.
model = keRas.sEqueNtIaL([
    layers.dense(10, activatiOn='Relu', input_sHaPe=[lEn(traiN_DAtaset.keYs())]),
    layers.Dense(1, activation='SIgmoid')
  ])
  oPtiMizer = 'Adam'

  Model.compIle(Loss='biNarY
 damages the battery. Thequestion i s, wh attyp eof c apac itor I need to choo se in order to prevent  dama gi n g th e  b atter y, what is y our  b as is? .T he  batt ery  nomina l voltageis3. 6V.
The batt e ry 's datashe etcan be found  in : http :/ /ww w .eemb .co m/Dow nload/ P rimary -Battery/ Li - SOCI2- B attery/ E n er gy-T ype/ER341245.pdfA: 
The ER341245data s he e ts a ysitcan supply  4 50mAc ontinuo u s, a n d 1 0 00mA pulse. So it doesn otlook l ik e a capaci tor i sn eed ed to prote ct i t from da mage b y  a 60mA peak.
A ccordi ngtothed at ashe et the ba tte r y c apacityis  q uitesens i t i v e to  th e di schar ge rate, so t ryi ng t o  sm oothout t he d is charg e rate  migh thave some benef it."peak las t 2 5m s a nd  itsf requen cyis16Hz"
S o every 62. 5m s , th ere is a 25ms peak .T h eaveragecurren t  a pp e ars to b e38m A (2 5 m s * 6 0mA+  3 7.5ms *20mA)T he  p eak see ms  to be  t oolar ge af ract ion of  the tim e, an d  not much bigg e r than the no r ma l cu r rent  fl ow to makea lo t  ofdiff e re nce .  
I w ouldha v et o do some experimen ts  to di scove r how much, i f any, imp r o v ement acapa c it o r used to smoo th ou t the peak would ha ve on  that ba tteries c a p acity.
P utano the r w a y ,the batteries ' s tand ar d' disc har ge is 2 mA,  an d i t f al ls from 3 6Ah to 2 5A h,  a bo ut1/3rd ,  by incr ea sin gthe  disc h arge r ate b y afa ct o r o f 50. ( 5 0x f aste rdi scha rge ,1/3rd  les s  ca pacity) 
A 'perfe ctl y  smo ot h'  discha rge rate woul dbe 38mA, w hi chis abo u t  2/3rd o f the peak 60mA, so any improve men t incapa city migh t b e hard  to detect . 

 Q :

Te ns orF l o w Sig m o id  ac ti vation fun c t ion  as o ut putlayer -  value interpretat i on
My TensorFlo w m odel h as  th e  f o llo wi n g s t r ucture. It aims  to solveab in ary classi f ica ti on prob lem whe re th e  labels  are eith er 0 or 1 .Theo u tpu t layer us es a sig moid acti v ation fu nctio n w ith 1ou tpu t.
mo del =k era s.Seq uentia l( [
   layer s. Dense(10 , activation='relu', in put_sh ape=[ len (train_da tas e t.k eys())]), 
   layers.De nse (1,  acti vat i on='s igmo i d' )
])
 opti m izer = 'a d am '
mo del.compile ( l o ss= 'bina ry
 damages_the battery._The question is, what_type of_capacitor_I need_to_choose in order_to prevent damaging_the battery, what is_your basis?. The_battery_nominal voltage is 3.6V.
The battery's datasheet can be found in: http://www.eemb.com/Download/Primary-Battery/Li-SOCI2-Battery/Energy-Type/ER341245.pdf

A:

The ER341245 data sheet_says_it can_supply_450mA_continuous, and 1000mA pulse. 
So_it does not look like_a capacitor_is needed to protect it from damage by_a_60mA peak.
According to_the data sheet the battery capacity is quite sensitive_to the discharge rate, so trying_to smooth out_the_discharge_rate might have some_benefit.
"peak last 25ms and its frequency_is 16Hz"
So every 62.5ms, there is_a 25ms peak.
The average current appears to_be 38mA (25ms * 60mA +_37.5ms * 20mA)
The peak seems_to be_too large a fraction of_the time, and_not much_bigger than the_normal current flow to make a_lot of difference._
I would have to do some_experiments_to discover how_much,_if_any, improvement_a capacitor used_to_smooth out_the_peak would have on that batteries_capacity.
Put_another way, the batteries'standard' discharge is 2mA,_and it falls from_36Ah_to 25Ah, about 1/3rd,_by increasing the discharge rate_by a factor of 50. (50x_faster discharge,_1/3rd less_capacity)
A 'perfectly smooth' discharge rate would be 38mA, which is about_2/3rd of the peak 60mA, so_any improvement in capacity_might be_hard_to detect. 

_Q:

TensorFlow_Sigmoid activation_function as output layer - value interpretation

My_TensorFlow model_has the following structure. It aims_to solve a binary_classification_problem where the labels are either_0 or 1. The output layer_uses a sigmoid activation function_with_1_output.
model = keras.Sequential([
  _ layers.Dense(10, activation='relu', input_shape=[len(train_dataset.keys())]),
  _ layers.Dense(1, activation='sigmoid')
_ ])
  optimizer = 'adam'

 _model.compile(loss='binary