Vazhinji vedu tinoziva neAI mifananidzo jenareta senge Yakagadzikana Diffusion. Yakatoshandura iyo indasitiri uye yakaverengerwa muhupenyu hwedu.
Nekudaro, maStable Diffusion modhi akawandisa kupfuura chizvarwa chemifananidzo.
Kune nzvimbo dzakawanda dzatinogona kuvashandisa.
Yakagadzikana Diffusion modhi imhando yemasvomhu. Uye, ivo vanogona kukubatsira kuti uongorore masisitimu ekuchinja masisitimu nekufamba kwenguva.
Dzinobva pamashoko ekupararira kwemaitiro. Nokudaro, iwe unogona kuongorora zvakasiyana-siyana zvezviitiko. Semuyenzaniso; kutapurirana kupisa, kuita kwemakemikari, uye kuparadzira ruzivo mumisika yemari.
Aya mamodheru anochinjika zvakanyanya. Saka, iwe unogona kutarisira mamiriro emangwana ehurongwa zvichienderana nemamiriro ayo aripo.
Kunze kwezvo, iwe unogona kuona zviri pasi pemuviri kana misimboti yemari inoitonga. Iyi pfungwa yakabatsira zvikuru munzvimbo dzakawanda. Izvi zvinosanganisira fizikisi, chemistry, uye mari.
Ndosaka tichida kuiongorora zvakanyanya. Uye, isu tinoda kukupa iwe chidzidzo chekuti ungadzidzisa sei aya Akagadzikana Diffusion modhi.
Ko Yakagadzikana Diffusion Models Akauya Sei?
Izvi zvakatangira kumashure kwekupedzisira kwezana remakore rechi19.
Kuferefeta kwemasvomhu kwemaitiro ekuparadzira muzvinhu ndiko kwakatanga modhi yeStable Diffusion. Imwe yeanonyanya kufarirwa Stable Diffusion modhi ndeye Fokker-Planck equation.
Yakatanga kuratidzwa muna 1906. Iyi mienzaniso yakashanduka uye yakagadziridzwa kuburikidza nenguva. Nokudaro, isu zvino tavashandisa mumaindasitiri akasiyana-siyana.
Chii chinonzi Logic Behind It?
Mumashoko akapfava, sezvatakataura, iwo masvomhu modhi. Kunze kwezvo, ivo vanotibatsira kuongorora kuti chivakwa kana huwandu hunopararira sei nekufamba kwenguva muhurongwa.
Dzinobva pane diffusion process misimboti. Saka, ivo vanotibatsira kuongorora kuti huwandu hunopararira sei muhurongwa. Kupararira uku kunokonzerwa nekusiyana kwekutarisa, kudzvanywa, kana mamwe ma parameter.
Ngatipei muenzaniso wakapfava. Fungidzira une mudziyo uzere nemvura wawaisa dhayi. Kupararira kunoonekwa pano apo dhayi inotanga kupararira uye emulsify mumvura. Zvichienderana nehunhu hwemvura uye dhayi, maStable Diffusion modhi anogona kushandiswa kufanotaura kuti dhayi ichapararira sei nekusanganiswa nekufamba kwenguva.
Mune masisitimu akaomarara, semisika yemari kana kuita kwemakemikari, mamodheru aya anogona kufanotaura kuti ruzivo kana hunhu huchapararira sei uye huchakanganisa sisitimu nekufamba kwenguva. Kunze kwezvo, data hombe inogona kujaira dzidzisa mienzaniso iyi kuita fungidziro dzechokwadi. Iwo anovakwa achishandisa masvomhu mafomula anotsanangura kushanduka kwehurongwa kwenguva refu.
Kunzwisisa uye kufanotaura kuparadzirwa kwehumwe hunhu muhurongwa kuburikidza nenguva ndiyo pfungwa huru iri pasi pemhando idzi. Zvakakosha kuyeuka kuti nyanzvi mune zvehunyanzvi minda dzinowanzo shandisa mhando idzi.
Nzira yekudzidzisa sei Models?
Unganidza uye gadzira yako data:
Iwe unofanirwa kutanga waunganidza uye kugadzirira data rako usati watanga kudzidzisa modhi yako. Yako data ingangoda kucheneswa uye kufomatidzwa. Zvakare, nhamba dzisipo dzingadawo kubviswa.
Sarudza chivakwa chemuenzaniso
Yakagadzika Diffusion modhi inouya nenzira dzakasiyana siyana. Iyo inonyanya kuenderana neFokker-Planck equation, Schrödinger equation, uye Master equation. Iyo modhi inonyatsoenderana nemamiriro ako chaiwo inofanira kusarudzwa. Nokudaro, imwe neimwe yemhando idzi ine zvakanakira uye zvisingabatsiri.
Kugadzira basa rako rekurasikirwa
Izvo zvakakosha nekuti zvinokanganisa kuti modhi yako inogona kuenzanisa sei data. Kune Yakagadzikana Diffusion modhi, iyo inoreva squared kukanganisa uye Kullback-Leibler divergence inogara ichirasikirwa mabasa.
Dzidzisa muenzaniso wako
Uchishandisa stochastic gradient descent kana yakafanana optimization maitiro, unogona kutanga kudzidzisa modhi yako mushure mekutsanangura kurasikirwa kwako basa.
Ongorora kugona kwemodhi yako
Iwe unofanirwa kutarisa nyowani data mushure mekudzidziswa nekuienzanisa neyeyedzo seti yedata.
Rongedza ma hyperparameter emodhi yako
Kuti uwedzere kuita kwemodhi yako, edza nemhando dzakasiyana dze hyperparameter seyero yekudzidza, saizi yebatch, uye huwandu hwematanho akavanzika munetiweki.
Dzokorora zviito zvekare
Ungangoda kudzokorora maitiro aya kanopfuura kamwe kuti uwane mibairo yakanaka. Izvo zvichave zvichienderana nekuoma kwedambudziko uye caliber yedata.
Coding Tutorial
Programming languages sePython, MATLAB, C++, uye R zvese zvinogona kushandiswa kugadzira Modhi Yakagadzika Diffusion. Mutauro unoshandiswa uchatsamira pane chaiwo mashandisirwo. Zvakare, zvinogona kutsamira pamaturusi nemaraibhurari akaitwa kuti awanikwe pamutauro iwoyo.
Python ndiyo yakanakisa sarudzo mune iyi kesi. Iyo ine maraibhurari akasimba seNumPy uye SciPy yekuverengera nhamba. Zvakare, inotsigira TensorFlow uye PyTorch yekugadzira uye kudzidzisa neural network. Nekudaro, inova sarudzo huru yekunyora Yakagadzika Diffusion modhi.
muenzaniso:
Ngatishandisei diffusion equation, fomula yemasvomhu inotsanangura kuti mhando kana uwandu, senge kupisa kana kusangana kwechinhu, zvinoshanduka sei nekufamba kwenguva muhurongwa. Iyo equation inotaridzika seizvi:
∂u/∂t = α ∇²u
Diffusion coefficient () chiyero chekuti chivakwa kana huwandu hunopararira zviri nyore sei kuburikidza nehurongwa.
Iyo Laplacian yeu (2u) irondedzero yekuti chivakwa kana huwandu hunochinja sei maererano nenzvimbo. Ipo iwe uri chivakwa kana huwandu huri kuparadzirwa (semuenzaniso, tembiricha kana kutarisisa), t ndiko kufamba kwenguva, ndiko kugovanisa coefficient, uye ndiko kupararira kunoramba kuripo ().
Tinogona kuishandisa tichishandisa nzira yeEuler muPython.
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
Iyi kodhi inoshandisa nzira yeEuler kuita iyo diffusion equation. Inotsanangura mamiriro ekutanga seyakafanana mamiriro ekutanga anomiririrwa neruzhinji rwevane chimiro che (100). 0.01 inoshandiswa sedanho renguva.
1000 iterations yenguva-yekufamba-famba inopera.
Inoshandisa np.diff basa, rinotarisa musiyano pakati pezvinhu zvakavakidzana. Nekudaro, inoverengera kutorwa kwenzvimbo yepfuma kana huwandu huri kuparadzirwa. Uye, inomiririrwa na du, pane imwe neimwe iteration.
Tinobva tawedzera dhirivheti yenzvimbo neiyo diffusion coefficient alpha uye nhanho yenguva yekuvandudza kukosha kweu.
Mumwe Muenzaniso Wakaoma
Ko iyo yakagadzikana yekuparadzira modhi inongoyera yakagadzika kupisa kupararira kutaridzika sei? Kodhi iyoyo inoshanda sei?
Kugadzirisa seti ye partial differential equations (PDEs) inotsanangura kuti kupisa kunopararira sei muhurongwa nekufamba kwenguva kunodiwa. Saka, isu tinokwanisa kudzidzisa Yakagadzikana Diffusion modhi iyo inodzokorora yakadzikama kupararira kwekupisa.
Heino mufananidzo wekuti kupisa equation, PDE inotsanangura Yakagadzikana Diffusion yekupisa mune imwe-dimensional tsvimbo, inogona kugadziriswa uchishandisa inogumira mutsauko nzira:
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()
Chiitiko Chigadzirwa kubva kuChinyorwa Chinoshanda Sei?
Sezvo yakakurumbira paInternet, tinogona kutarisa kuti chizvarwa chemifananidzo chinoshanda sei zvakare.
Natural language processing (NLP) nzira uye neural networks. Uye, ivo vanowanzo shandiswa kupa iyo Yakagadzika Diffusion modhi yekushandurwa kwemavara-kune-mufananidzo. Tsanangudzo yakafara yekuti ungazviita sei inopiwa pazasi:
1- Tokenize mazwi ari muzvinyorwa data, uye bvisa mazwi ekumisa uye zviratidzo. Shandura mazwi kuti ave nhamba. Icho chikamu che preprocessing (izwi embeddings).
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- Dzidza maitiro ekubatanidza zvinyorwa nemifananidzo uchishandisa neural network inosanganisa encoder uye decoder. Iyo decoder network inogamuchira iyo yakadzika kodhi sekuisa. Zvadaro, inogadzira iyo yakabatana pikicha mushure mekunge encoder network yashandura iyo data data kuita compact inomiririra (latent kodhi).
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- Nekupa iyo yakakura muunganidzwa wemifananidzo uye tsananguro yemavara inoenda navo. Zvadaro, unogona kudzidzisa encoder-decoder network.
# 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- Mushure mekunge network yadzidziswa, unogona kuishandisa kugadzira mapikicha kubva kutsva zvinyorwa zvinyorwa. Uye, zviri nekudyisa zvinyorwa mu encoder network. Zvadaro, iwe unogona kuburitsa kodhi yakavanzika, uye wozodyisa iyo yakadzika kodhi mudhikodha network kuburitsa iyo yakabatana mufananidzo.
# Encode the text input
latent_code = encoder.predict(text)
# Generate an image from the latent code
image = decoder.predict(latent_code)
5-Kusarudzwa kweiyo yakakodzera dataset uye kurasikirwa mabasa ndeimwe yematanho akanyanya kukosha. Iyo dataset yakasiyana uye ine huwandu hwakawanda hwemifananidzo uye zvinyorwa zvinotsanangurwa. Tinoda kuve nechokwadi chekuti mifananidzo ndeyechokwadi. Zvakare, isu tinofanirwa kuve nechokwadi chekuti tsananguro yemavara inogoneka kuitira kuti tigone kugadzira basa rekurasikirwa.
# 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)
Chekupedzisira, unogona kuyedza nemamwe magadzirirwo uye nzira. Saka, kuti iwe unogona kusimudza kuita kwemuenzaniso, senge kutarisisa nzira, maGAN, kana maVAE.
Leave a Reply