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def spectral_normalization(W, num_iters=1, name='spectral_normalization'):
    with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
        
        def l2_normalize(v, eps=1e-12):
            return v / (tf.reduce_sum(v ** 2) ** 0.5 + eps)

        # Usually num_iters = 1 will be enough
        W_shape    = W.shape.as_list()
        W_reshaped = tf.reshape(W, [-1, W_shape[-1]])
        

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{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',

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He_gain             = 2.0
He_n                = (fan_in * (kernel_size ** 2))

# Most stable performance in my experience.
He_c_trunc_norm     = np.sqrt(1.3 * He_gain / He_n)
He_normal_init      = tf.initializers.truncated_normal(stddev=He_c_trunc_norm)

He_c_uniform        = np.sqrt(3.0 * He_gain / He_n)
He_uniform_init     = tf.initializers.random_uniform(minval=-He_c_uniform, maxval=He_c_uniform)

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from IPython.lib.backgroundjobs import BackgroundJobManager
from IPython.core.magic         import register_line_magic
from IPython.display            import display
from IPython                    import get_ipython
from itertools                  import islice, chain
from ipywidgets                 import IntProgress, HTML, VBox

# Iterate through a generator without peaking, yeilding a list of elements
#
def chunks(iterable, size):

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"""
This is a module to convert between one dimensional distance along a
`Hilbert curve`_, :math:`h`, and N-dimensional coordinates,
:math:`(x_0, x_1, ... x_N)`.  The two important parameters are :math:`N`
(the number of dimensions, must be > 0) and :math:`p` (the number of
iterations used in constructing the Hilbert curve, must be > 0).

We consider an N-dimensional `hypercube`_ of side length :math:`2^p`.
This hypercube contains :math:`2^{N p}` unit hypercubes (:math:`2^p` along
each dimension).  The number of unit hypercubes determine the possible

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