Batch Gradient Descent(python)
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import numpy as npimport tensorflow as tfdef GradientDescent(x,y,theta): m, n = x.shape # m is #training example,while n is #feature for j in range(n): #learning rate:0.03 theta[j] = theta[j] + 0.03/m * np.sum(([(y[i] -np.matmul(x[i,:],theta))*x[i,j] for i in range(m)])) return thetax = np.array([[1],[2],[3],[4],[5],[6]])y = np.array([5,7,9,11,13,15])
#the stop conditonepison = 0.01#add x0=1 to the datax1 = np.hstack((np.ones((6,1)), x))theta = np.zeros((2,1))# m,n = x1.shapeprint(x1.shape)while(True): theta = GradientDescent(x1, y, theta) prediction = np.matmul(x1, theta) loss = np.sum((prediction.T - y)**2) if loss < epison: breakprint('prediction=',prediction.T)print('y=',y)print('loss=',loss)
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