GAN_1000.py

def highdim_syn_data(batch_size, num_components, num_features,**kwargs):
    shape=(num_features)
    shape_cat=(batch_size,num_components)
    cat = ds.Categorical(tf.zeros(num_components, dtype=float32))
    
    mus = [-1*tf.ones(shape, dtype=float32),-.5*tf.ones(shape, dtype=float32),
          0*tf.ones(shape, dtype=float32),.5*tf.ones(shape, dtype=float32),
          -2*tf.ones(shape, dtype=float32),-2.5*tf.ones(shape, dtype=float32),
          10*tf.ones(shape, dtype=float32),.25*tf.ones(shape, dtype=float32),
          -13*tf.ones(shape, dtype=float32),-5.5*tf.ones(shape, dtype=float32)]
    
    s=tf.concat([1*tf.ones((num_features-500), dtype=float32),
                np.zeros((500), dtype=float32)],0)
    
    sigmas = [s for i in range(num_components)]
    components = list((ds.MultivariateNormalDiag(mu, sigma, **kwargs) 
                       for (mu, sigma) in zip(mus, sigmas)))
    return ds.Mixture(cat, components).sample(batch_size)