D +-----------------+ | Boshlanish | +-----------------+ | v +-----------------+ | int n; | +-----------------+ | v +----------------------+ | cout << "n = "; | +----------------------+ | v +----------------------+ | cin >> n; | +----------------------+ | v +----------------------+ | n > 0 ? | +----------------------+ / / v v +---------+ +---------------------+ | n++; | | n < 0 ? | +---------+ +---------------------+ / / v v +-------+ +-----------+ | n = n | | n = 10; | | - 2; | +-----------+ +-------+ | v +----------------------+ | cout << "natija = " << n; | +----------------------+ | v +-----------------+ | Tugash | +-----------------+D
# Sun'iy neyron tarmog'ini yaratish uchun TensorFlow kutubxonasidan foydalanamiz.
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
# 1. Ma'lumotlar tayyorlash
# "make_moons" funksiyasi sun'iy ma'lumotlar to'plamini yaratadi
X, y = make_moons(n_samples=1000, noise=0.2, random_state=42)
# Ma'lumotlarni trening va test to'plamlariga ajratish
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Ma'lumotlarni standartlashtirish
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# 2. Model yaratish
model = Sequential([
Dense(16, activation='relu', input_shape=(2,)), # 1-qavat: 16 neyron, ReLU faollashtirish funksiyasi
Dense(16, activation='relu'), # 2-qavat: 16 neyron, ReLU
Dense(1, activation='sigmoid') # 3-qavat (chiqish qavati): 1 neyron, Sigmoid funksiyasi
])
# Modelni kompilyatsiya qilish
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
# 3. Modelni o'qitish
history = model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2, verbose=1)
# 4. Modelni baholash
test_loss, test_accuracy = model.evaluate(X_test, y_test)
print(f"Test to'plamidagi aniqlik: {test_accuracy:.2f}")
# 5. Natijalarni vizualizatsiya qilish
# O'qitish jarayonidagi yo'qotish va aniqlikni chizish
plt.figure(figsize=(12, 5))
# Yo'qotish grafigi
plt.subplot(1, 2, 1)
plt.plot(history.history['loss'], label='Trening yo\'qotish')
plt.plot(history.history['val_loss'], label='Validatsiya yo\'qotish')
plt.title('Yo\'qotish')
plt.xlabel('Epochs')
plt.ylabel('Yo\'qotish')
plt.legend()
# Aniqlik grafigi
plt.subplot(1, 2, 2)
plt.plot(history.history['accura
𝐌 N 𝐌
Endi freelancerlikni boshlaganlarga maslahat😁(o'zimning qovunchilik tajribdan kelib chiqib)
- Klientga hech qachon 1-bo'lib narxni aytmang, aytsangiz ham siz qancha summaga qilib bera olsangiz anashunda 10 marta katta summa ayting keyin uni budgetiga qarab ozaytiring.Avansni ham unitmang o'rtacha 30-40% oling. Agar proyekt atmen bo'b ketsayu siz bitirgan bo'lsangiz sizga 50% pulni tashlashini ayting
- Deadline ni iloji boricha soring lekin o'ta uzoq vaqtga emas. Shunda siz projectni ancha erta topshirasiz va mijozni sizga bo'lgan ishonchi ortadi(koproq pul berishi ham mumkun)
- Qilayotga ishlaringizni mijozga tez-tez tashlab turing(ishonch uchun)
- Agar proyektni olib yoki olmasdan turib yasay olmasligingizni bilsangiz qimmatroq narxga oling va boshqa developer topib anashu ishlarni qildiring(va bitkizgandan so'ng shu postni unga jo'nating )
Shu mani olgan tajribam sizlarga ham ulashay dedim. Omad hammaga
#oqishkerak #python #roadmap #coding #python@pythonNews_Uzbekistan
6 · 1.2K ·