2-dastur kodi va natijasi
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_iris
from keras.models import Sequential
from keras.layers import Dense
# Ma'lumotlarni yuklash
iris = load_iris()
X = iris.data
y = iris.target
# Qiymatlarni binar kategoriyaga aylantiramiz
y = pd.get_dummies(y).values
# Ma'lumotlarni o'qitish va sinov uchun ajratamiz
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Ma'lumotlarni standartlashtiramiz
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# Modelni tuzamiz
model = Sequential()
model.add(Dense(4, input_dim=4, activation='relu'))
model.add(Dense(3, activation='softmax'))
# Modelni tuzamiz
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# Modelni o'qitamiz
model.fit(X_train, y_train, epochs=50, batch_size=8, validation_data=(X_test, y_test))
import matplotlib.pyplot as plt
# Modelning o'qitilishi davriga oid ma'lumotlarni olamiz
history = model.history.history
# O'qitish natijalarni grafikga chizamiz
plt.plot(history['accuracy'], label='Training Accuracy')
plt.plot(history['val_accuracy'], label='Validation Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
# Test ma'lumotlaridan bashqa ma'lumotlarni standartlashtiramiz
X_new = scaler.transform(X_test)
# Natijalarni olish
predictions = model.predict(X_new)
# Baholarni chiqaramiz
print(predictions)
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