Kuxazululiwe: i-plot neural network

Ukwakha imodeli yenethiwekhi ye-neural kuwumkhakha othakazelisayo ekufundeni komshini, ikakhulukazi ePython. Inikeza ububanzi obuningi bokuhlaziya, ukuqagela, kanye nezinqubo zokuthatha izinqumo ezizenzakalelayo. Ngaphambi kokuthi singene ku-nitty-gritty yokwakha inethiwekhi ye-neural yesakhiwo, kubalulekile ukuqonda ukuthi iyini inethiwekhi ye-neural. Empeleni kuwuhlelo lwama-algorithms olusondeza ukwakheka kobuchopho bomuntu, ngaleyo ndlela kwakheka inethiwekhi ye-neural yokwenziwa okuthi, ngenqubo yokuhlaziya ihumushe idatha yezinzwa, icoshe ama-nuances 'angabonakali' ngedatha eluhlaza, njengoba kwenza ubuchopho bethu.

Inethiwekhi ye-neural ibalulekile ezinqubweni zokumbiwa kwedatha, lapho ihlonza amaphethini namathrendi abeyinkimbinkimbi kakhulu kubantu noma amanye amasu ekhompyutha. Manje, ake singene enhliziyweni yendabaโ€” sisebenzisa iPython ukwakha nokuhlela inethiwekhi ye-neural.

Ukwakha amanethiwekhi we-neural ePython

# Importing libraries
import numpy as np     
import matplotlib.pyplot as plt     
from sklearn.datasets import make_blobs 

# Create a sample dataset
dataset=make_blobs(n_samples=800, centers=2, n_features=2, cluster_std=1.6, random_state=50)

# Split into input (X) and output (y)
X, y = dataset

# Plot the sample data
plt.scatter(X[:,0], X[:,1], c=y)
plt.show()

Masiqonde le khodi:

  • Emigqeni emine yokuqala, singenisa imitapo yolwazi edingekayo njenge-numpy, matplotlib njll.
  • Okulandelayo, sisebenzisa umsebenzi we-'make_blobs' kusuka ku-sklearn, sakha idathasethi.
  • Bese isethi yedatha ihlukaniswa okokufaka (X) kanye nokuphumayo (y).
  • Umugqa wokugcina uhlela u-X kanye no-y futhi usinika umbono wedatha kusetshenziswa umsebenzi we-scatter kusuka kulabhulali ye-matplotlib.

Ukuqonda imitapo yolwazi yenethiwekhi ye-neural

Ukuqonda imitapo yolwazi yePython kulo mongo kubalulekile. Umtapo wezincwadi we-numpy uvumela ukusebenza kwezibalo, i-matplotlib isetshenziselwa ukuhlela igrafu ye-2D kusukela kudatha eku-Python kanye nokufunda komshini we-sklearn spearheads ku-Python.

Ikhodi yesinyathelo ngesinyathelo

Isinyathelo ngesinyathelo inqubo yekhodi isivumela ukuthi sithole ukuqonda okujulile:

# Import necessary modules
from keras.models import Sequential
from keras.layers import Dense

# Create the model
model = Sequential()

# Add input layer with 2 inputs neurons
model.add(Dense(input_dim=2, output_dim=1, init='uniform', activation='sigmoid'))

# Compile model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Fit the model
history = model.fit(X, y, epochs=100, batch_size=10)

Kulesi siqephu sekhodi,

  • Sakha imodeli sisebenzisa umsebenzi weSequential() kusuka kumojula ye-keras.models.
  • Okulandelayo, isendlalelo sokufakwayo singezwa ngama-neurons okufakwayo angu-2. Lapha, i-'Dense' iwuhlobo lwesendlalelo olusebenza ezimweni eziningi. Kusendlalelo esiminyene, wonke ama-node akusendlalelo sangaphambilini axhuma kuma-node kusendlalelo samanje.
  • 'Ukuhlanganisa' kulungiselela imodeli yokuqeqeshwa.
  • Ingxenye yokugcina, 'ukufaka imodeli' yilapho inethiwekhi ye-neural iqeqeshwa khona. 'Ama-Epoch' abonisa inani lokuphasa kwedathasethi yokuqeqeshwa. Imodeli ifunda futhi ibuyekeze amapharamitha emodeli phakathi nenkathi ngayinye. Usayizi wenqwaba uyisethi engaphansi yedathasethi.

Ngala makhodi, sakha isisekelo sokwakha inethiwekhi ye-neural yesakhiwo sisebenzisa iPython. Ngemitapo yolwazi ebanzi ye-Python namandla anamandla, amanethiwekhi we-neural angasetshenziswa futhi abonwe ngempumelelo. Kumayelana nokuqonda izimpande, futhi kuhle ukuthi ukhule kulo mkhakha oguquguqukayo wokufunda ngomshini.

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