Inta badan oo naga mid ah waa la yaqaan AI sawir- dhaliyaasha sida Faafidda Deggan. Horay ayay u bedeshay warshadaha waxaana lagu daray nolosheena.
Si kastaba ha noqotee, moodooyinka fidsan ee xasilloon ayaa aad uga badan jiilka sawirka.
Waxaa jira meelo badan oo aan ka shaqaaleysiin karno.
Moodooyinka Faafinta Xasilooni waa moodooyin xisaabeed. Iyo, waxay kaa caawin karaan inaad baarto dhaqdhaqaaqyada isbeddelka nidaamyada waqti ka dib.
Waxay ku salaysan yihiin fikradaha habka fidinta. Sidaa darteed, waxaad baari kartaa dhacdooyin kala duwan oo kala duwan. Tusaale ahaan; gudbinta kulaylka, falcelinta kiimikada, iyo faafinta macluumaadka ee suuqyada maaliyadeed.
Moodooyinkani aad bay ula qabsan karaan. Markaa, waxaad odorosi kartaa xaaladda mustaqbalka ee nidaamka oo ku saleysan xaaladdiisa hadda.
Ka sokow, waxaad arki kartaa mabaadi'da jirka ama maaliyadeed ee hoose ee xukuma. Fikraddan ayaa aad faa'iido u leh dhinacyo badan. Kuwaas waxaa ka mid ah fiisigiska, kimisteriga, iyo maaliyadda.
Tani waa sababta aan u dooneyno inaan sii baarno. Iyo, waxaan rabnaa inaan ku siino cashar ku saabsan sida loo tababaro moodooyinkan Stable Diffusion.
Sidee Ku Yimid Moodooyinka Faafinta Deggan?
Tani waxay asal ahaan ka soo bilaabatay qarnigii 19aad.
Baaritaanka xisaabeed ee hababka fidinta ee arrimaha waa halka ay moodooyinka Stable Diffusion ka bilaabeen. Mid ka mid ah noocyada ugu caansan ee Stable Diffusion waa isla'egta Fokker-Planck.
Waxaa markii ugu horreysay la soo bandhigay 1906. Moodooyinkan ayaa horumaray oo wax laga beddelay waqti. Sidaa darteed, waxaan hadda u isticmaalnaa warshado kala duwan.
Waa maxay macquulka ka dambeeya?
Si fudud, sida aan sheegnay, waa moodooyinka xisaabeed. Ka sokow, waxay naga caawiyaan inaan baarno sida hantida ama tirada ay ugu faafto waqti ka dib nidaamka.
Waxay ku salaysan yihiin mabaadi'da habka fidinta. Markaa, waxay naga caawiyaan inaan baarno sida tiradu ugu faafto nidaamka. Fiditaankani waa natiijada kala duwanaanshaha u-fiirsashada, cadaadiska, ama cabbirrada kale.
Aan bixino tusaale fudud. Bal qiyaas in aad haysato weel dareere ka buuxo oo aad ku dartay dheeh. Fiditaanka ayaa lagu arkaa halkan marka dheehu bilaabo inuu kala firdhiyo oo uu ku ekaado dareeraha. Iyada oo ku saleysan sifooyinka dareeraha iyo dheeha, moodooyinka Faafinta Deggan ayaa laga yaabaa in loo isticmaalo si loo saadaaliyo sida dheehu u kala firdhi doono oo uu isku qasmi doono wakhti ka dib.
Nidaamyada kakan, sida suuqyada maaliyadeed ama falcelinta kiimikaad, moodooyinkani waxay saadaalin karaan sida macluumaadka ama sifooyinku u fidi doonaan oo saamayn ugu yeelan doonaan nidaamka waqti ka dib. Ka sokow, xog badan ayaa laga yaabaa in la qabsado tabobar moodooyinkan in la sameeyo saadaal sax ah. Waxay ku dhismeen qaabab xisaabeed oo qeexaya horumarka muddada fog ee nidaamka.
Fahamka iyo saadaalinta faafinta sifooyinka qaarkood ee nidaamka waqti ka dib ayaa ah fikradda ugu weyn ee salka ku haysa moodooyinkan. Waxaa muhiim ah in la xasuusto in khubarada ku takhasusay meelaha gaarka ah ay caadi ahaan adeegsadaan moodooyinkan.
Sidee loo tababaraa moodooyinka?
Ururi oo diyaari xogtaada:
Waa inaad marka hore ururisaa oo diyaarisaa xogtaada ka hor inta aanad bilaabin tababarka moodelkaaga. Xogtaadu waxay u baahan kartaa in la nadiifiyo oo la qaabeeyo. Sidoo kale, tirooyinka maqan ayaa sidoo kale loo baahan karaa in la tirtiro.
Dooro qaab-dhismeedka qaab-dhismeedka
Moodooyinka fidsan ee deggan waxay ku yimaadaan qaabab kala duwan. Waxay inta badan ku salaysan tahay isla'egta Fokker-Planck, isla'egta Schrödinger, iyo isla'egta Masterka. Qaabka ugu fiican ee xaaladaada gaarka ah waa in la doortaa. Sidaa darteed, mid kasta oo ka mid ah moodooyinkan ayaa leh faa'iidooyin iyo faa'iido darrooyin.
Dejinta shaqada lumiskaaga
Waa muhiim maadaama ay saameynayso sida ugu wanaagsan ee moodeelkaagu uu u waafajin karo xogta. Moodooyinka Faafinta Xasilloonida ah, celceliska khaladka labajibaaran yahay iyo kala duwanaanshaha Kullback-Leibler waa hawlo khasaare badan leh.
Tababar modelkaaga
Isticmaalka farcanka stochastic gradient ama hab la mid ah hagaajinta, waxaad bilaabi kartaa tababarka moodeelkaaga ka dib markaad qeexdo shaqadaada lumis.
Baadhi guud ahaan qaabkaaga
Waa inaad hubisaa xogta cusub tababarka ka dib adigoo barbar dhigaya xogta xogta.
Dheji cabbirada sare ee modelkaaga
Si kor loogu qaado waxqabadka moodeelkaaga, ku tijaabi qiimayaal kala duwan oo hyperparameters ah sida heerka waxbarashada, cabbirka dufcadda, iyo tirada lakabyada qarsoon ee shabakadda.
Ku celi falkii hore
Waxaa laga yaabaa inaad u baahato inaad ku celiso hababkan wax ka badan hal mar si aad u hesho natiijooyinka ugu fiican. Waxay ku xirnaan doontaa dhibka dhibka iyo cabbirka xogta.
Casharka Codaynta
Luqadaha barnaamijka sida Python, MATLAB, C++, iyo R ayaa laga yaabaa in dhamaantood loo isticmaalo si loo abuuro moodooyinka Faafinta Deggan. Luuqadda la isticmaalo waxay ku tiirsanaan doontaa codsiga gaarka ah. Sidoo kale, waxay ku xirnaan kartaa qalabka iyo maktabadaha loo diyaariyay luqaddaas.
Python waa doorashada ugu fiican kiiskan. Waxay leedahay maktabado adag sida NumPy iyo SciPy ee xisaabinta nambarada. Sidoo kale, waxay taageertaa TensorFlow iyo PyTorch abuurista iyo tababarida shabakadaha neerfaha. Sidaa darteed, waxay noqonaysaa ikhtiyaar weyn oo loogu talagalay qorista moodooyinka Diffusion Stable.
Tusaale:
Aynu isticmaalno isla'egta faafinta, qaacido xisaabeed oo qeexaysa sida tayada ama tirada, sida kulaylka ama xoogga saaridda walaxda, ay isu beddelaan muddo ka dib nidaamka. Isla'egta guud ahaan waxay u egtahay sidan:
∂u/∂t = α ∇²u
Isku xidhka faafinta () waa cabbiraadda sida fudud ee hantida ama tirada ay ugu faafto nidaamka.
Laplacian of u (2u) waa sharaxaad ku saabsan sida hantida ama tirada ay isu beddesho marka loo eego booska. Halka aad tahay hantida ama tirada la faafiyay (tusaale, heerkulka ama xooga saarida), t waa socodka wakhtiga, waa isku xidhka faafinta, waana faafinta joogtada ah ().
Waxaan ku hirgelin karnaa inagoo adeegsanayna habka Euler ee Python.
import numpy as np
# Define the diffusion coefficient
alpha = 0.1
# Define the initial condition (e.g. initial temperature or concentration)
u = np.ones(100)
# Time step
dt = 0.01
# Time-stepping loop
for t in range(1000):
# Compute the spatial derivative
du = np.diff(u)
# Update the value of u
u[1:] = u[1:] + alpha * du * dt
Koodhkani wuxuu isticmaalaa farsamada Euler si uu u hirgeliyo isla'egta fidinta. Waxay ku sifaynaysaa xaalada bilawga ah xaalad bilow ah oo lebis ah oo ay ka muuqato soo diyaarsan kuwa qaabkoodu yahay (100). 0.01 waxaa loo isticmaalaa sida waqtiga tallaabada.
1000 ku celcelin ee loop-ka-waqtiga-qaadista ayaa la dhammaystiray.
Waxay isticmaashaa shaqada np.diff, taas oo go'aamisa farqiga u dhexeeya walxaha deriska ah. Sidaa darteed, waxay xisaabinaysaa meesha ka soo jeeda hantida ama tirada la faafiyay. Iyo, waxaa lagu matalay du, mar kasta oo dib-u-eegis ah.
Kadibna waxaan ku dhufanay kala soocida boosaska isku xidhka faafinta alfa iyo wakhtiga talaabada lagu cusboonaysiinayo qiimaha u.
Tusaalaha Kakan
Sidee buu u ekaan karaa qaabka fidinta fidsan ee cabbiraya fidinta kulaylka deggan? Sidee buu koodkaas u shaqeeyaa?
Xallinta isla'egyada kala duwanaanta qaybeed (PDEs) oo sharxaya sida kulaylku ugu faafo nidaamka waqti ka dib waa lama huraan. Markaa, waxaanu tababbari karnaa qaabka fidinta Xasilloonida ah ee soo noqnoqda faafinta joogtada ah ee kulaylka.
Halkan waxaa ah sawir ku saabsan sida isla'egta kulaylka, PDE oo sharxaysa Kala-duwanaanta Kulaylka ee usheeda hal-cabbir ah, lagu xalin karo iyadoo la adeegsanayo habka farqiga u dambeeya:
import numpy as np
import matplotlib.pyplot as plt
# Define the initial conditions
L = 1 # length of the rod
Nx = 10 # number of spatial grid points
dx = L / (Nx - 1) # spatial grid spacing
dt = 0.01 # time step
T = 1 # total time
# Set up the spatial grid
x = np.linspace(0, L, Nx)
# Set up the initial temperature field
T0 = np.zeros(Nx)
T0[0] = 100 # left boundary condition
T0[-1] = 0 # right boundary condition
# Set up the time loop
Tn = T0
for n in range(int(T / dt)):
Tnp1 = np.zeros(Nx)
Tnp1[0] = 100 # left boundary condition
Tnp1[-1] = 0 # right boundary condition
for i in range(1, Nx - 1):
Tnp1[i] = Tn[i] + dt * (Tn[i+1] - 2*Tn[i] + Tn[i-1]) / dx**2
Tn = Tnp1
# Plot the final temperature field
plt.plot(x, Tn)
plt.xlabel('x')
plt.ylabel('T(x)')
plt.show()
Sidee buu u shaqeeyaa Jiilka Sawirka?
Maadaama uu caan ku yahay internetka, waxaan hubin karnaa sida jiilka sawirku u shaqeeyo sidoo kale.
Hababka habaynta luqadda dabiiciga ah (NLP) iyo shabakadaha neerfaha. Iyo, inta badan waxaa loo isticmaalaa in lagu bixiyo qaabka Faafinta Deggan ee qoraal-u-beddelka sawirka. Sharaxaad ballaadhan oo ku saabsan sida loo dhammeeyo ayaa lagu bixiyaa hoos:
1- Calaamadee erayada ku jira xogta qoraalka, meeshana ka saara ereyada joojinta iyo xarakaynta. Erayada u beddel qiimayaal tiro. Waa qayb ka mid ah diyaarinta (ereyada ku dhejinta).
import nltk
from nltk.tokenize import word_tokenize
nltk.download('punkt')
# Pre-processing the text data
text = "a bird sitting on a flower. "
words = word_tokenize(text)
words = [word.lower() for word in words if word.isalpha()]
2- Baro sida loo xidhiidhiyo qoraalka iyo sawirada adoo isticmaalaya shabakad neural ah oo isku daraysa cod-bixiye iyo cod-bixiye. Shabakadda codeeyaha waxay helaysaa koodka daahsoon sida gelinta. Kadibna, waxay abuurtaa sawirka laxiriira ka dib markii shabakada encoder-ku u beddesho xogta qoraalka matalaad is haysta (koodka qarsoon).
import tensorflow as tf
# Define the encoder model
encoder = tf.keras.Sequential()
encoder.add(tf.keras.layers.Embedding(input_dim=vocab_size,
output_dim=latent_dim))
encoder.add(tf.keras.layers.GRU(latent_dim))
encoder.add(tf.keras.layers.Dense(latent_dim))
# Define the decoder model
decoder = tf.keras.Sequential()
decoder.add(tf.keras.layers.Dense(latent_dim,
input_shape=(latent_dim,)))
decoder.add(tf.keras.layers.GRU(latent_dim))
decoder.add(tf.keras.layers.Dense(vocab_size))
# Combine the encoder and decoder into an end-to-end model
model = tf.keras.Sequential([encoder, decoder])
3-Iyadoo la siinayo sawiro tiro badan iyo tilmaanta qoraalka ee la socota. Ka dib, waxaad tababari kartaa shabakadda encoder-decoder.
# Compile the model
model.compile(optimizer='adam',
loss='categorical_crossentropy')
# Train the model on the dataset
model.fit(X_train, y_train, epochs=10, batch_size=32)
4- Ka dib markii shabakadda la tababaro, waxaad u isticmaali kartaa inaad ka soo saarto sawirro qoraallo cusub ah. Iyo, waa iyada oo la quudinayo qoraalka shabakada codeerka. Kadib, waxaad soo saari kartaa koodka daahsoon, ka dibna waxaad ku quudin kartaa koodka qarsoon ee shabakada furaha si aad u soo saarto sawirka la xidhiidha.
# Encode the text input
latent_code = encoder.predict(text)
# Generate an image from the latent code
image = decoder.predict(latent_code)
5-Xulashada xogta ku habboon iyo hawlaha luminta waa mid ka mid ah tallaabooyinka ugu muhiimsan. Xog-ururinta waa kala duwan tahay waxayna ka kooban tahay sawirro iyo sharraxaadyo qoraal oo ballaaran. Waxaan rabnaa inaan hubinno in sawiradu ay yihiin kuwo xaqiiqo ah. Sidoo kale, waxaan u baahannahay inaan hubinno in sharraxaadaha qoraalka ay yihiin kuwo macquul ah si aan u naqshadeyno shaqada lumista.
# Define the loss function
loss = tf.losses.mean_squared_error(y_true, y_pred)
# Compile the model
model.compile(optimizer='adam', loss=loss)
# use diverse dataset
from sklearn.utils import shuffle
X_train, y_train = shuffle(X_train, y_train)
Ugu dambeyntii, waxaad tijaabin kartaa naqshadaha kale iyo hababka. Markaa, in aad kor u qaadi karto waxqabadka modelka, sida hababka dareenka, GANs, ama VAEs.
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