INNER CODE UNIT · Python

data_preparation

drawbridge/keras-mmoe · synthetic_demo.py:34

def data_preparation():
    # Synthetic data parameters
    num_dimension = 100
    num_row = 12000
    c = 0.3
    rho = 0.8
    m = 5

    # Initialize vectors u1, u2, w1, and w2 according to the paper
    mu1 = np.random.normal(size=num_dimension)
    mu1 = (mu1 - np.mean(mu1)) / (np.std(mu1) * np.sqrt(num_dimension))
    mu2 = np.random.normal(size=num_dimension)
    mu2 -= mu2.dot(mu1) * mu1
    mu2 /= np.linalg.norm(mu2)
    w1 = c * mu1
    w2 = c * (rho * mu1 + np.sqrt(1. - rho ** 2) * mu2)

    # Feature and label generation

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