from MLlib.models import PolynomialRegression from MLlib.optimizers import Adam from MLlib.loss_func import MeanSquaredError from MLlib.utils.misc_utils import read_data, printmat X, Y = read_data('datasets/Polynomial_reg.txt') polynomial_model = PolynomialRegression(3) # degree as user's choice optimizer = Adam(0.01, MeanSquaredError) polynomial_model.fit(X, Y, optimizer=optimizer, epochs=200, zeros=True) printmat('predictions', polynomial_model.predict(X)) Z = polynomial_model.predict(X) polynomial_model.save('test') polynomial_model.plot(X, Y, Z, optimizer=optimizer, epochs=200, zeros=True)
from MLlib.models import LinearRegression from MLlib.optimizers import Adam from MLlib.loss_func import MeanSquaredError from MLlib.utils.misc_utils import read_data, printmat X, Y = read_data('datasets/linear_reg_00.txt') linear_model = LinearRegression() optimizer = Adam(0.01, MeanSquaredError) linear_model.fit(X, Y, optimizer=optimizer, epochs=200, zeros=False) printmat('predictions', linear_model.predict(X)) linear_model.save('test')
from MLlib import Tensor from MLlib.regularizer import LinearRegWith_Regularization from MLlib.regularizer import L1_Regularizer from MLlib.optim import SGDWithMomentum from MLlib.utils.misc_utils import printmat import numpy as np np.random.seed(5322) x = Tensor.randn(10, 8) # (batch_size, features) y = Tensor.randn(10, 1) reg = LinearRegWith_Regularization(8, L1_Regularizer, optimizer=SGDWithMomentum, Lambda=7) # Regularizer,optimizer and Lambda as per user's choice printmat("Total Loss", reg.fit(x, y, 800))