In [2]:

    

import numpy as np
import tensorflow as tf


    

In [3]:

    

num_components = 2
num_latent = 2
num_inducing = 3


    

In [5]:

    

raw_covariance = tf.Variable(tf.zeros([num_components, 
                                       num_latent,
                                       num_inducing]))


    

In [12]:

    

tf.zeros([num_components, num_latent, num_inducing])


    

    
Out[12]:





<tf.Tensor 'zeros_1:0' shape=(2, 2, 3) dtype=float32>

In [17]:

    

covars_list = [None] * num_components


    

In [18]:

    

covars_list


    

    
Out[18]:





[None, None]

In [21]:

    

tri_vec_shape = [num_inducing*(num_inducing+1)/2]


    

In [23]:

    

init_vec = np.zeros([num_components, num_latent] +
                    tri_vec_shape,
                    dtype=np.float32)


    

In [24]:

    

init_vec


    

    
Out[24]:





array([[[ 0.,  0.,  0.,  0.,  0.,  0.],
        [ 0.,  0.,  0.,  0.,  0.,  0.]],

       [[ 0.,  0.,  0.,  0.,  0.,  0.],
        [ 0.,  0.,  0.,  0.,  0.,  0.]]], dtype=float32)

In [25]:

    

xrange(1,2)


    

    
Out[25]:





xrange(1, 2)

In [28]:

    

for i in xrange(1,3):
    print(i)


    

    




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