def __init__(self, name, shape, initial_stdev = 2.0, initial_prec_a = 5.0, initial_prec_b = 1.0, a0 = 1.0, b0 = 1.0, fixed_prec = False, mean_init_std = None):
if mean_init_std is None:
mean_init_std = 1.0 / np.sqrt(shape[-1])
with tf.variable_scope(name) as scope:
#self.mean = tf.get_variable(name="mean", shape=shape, initializer=tf.contrib.layers.xavier_initializer(), dtype = tf.float32)
#self.var = tf.Variable(initial_var * np.ones(shape), name = name + ".var", dtype = tf.float32)
self.mean = tf.Variable(tf.random_uniform(shape, minval=-mean_init_std, maxval=mean_init_std))
self.logvar = tf.Variable(np.log(initial_stdev**2.0) * np.ones(shape), name = "logvar", dtype = tf.float32)
if fixed_prec:
self.prec_a = tf.constant(initial_prec_a * np.ones(shape[-1]), name = "prec_a", dtype = tf.float32)
self.prec_b = tf.constant(initial_prec_b * np.ones(shape[-1]), name = "prec_b", dtype = tf.float32)
else:
self.prec_a = tf.Variable(initial_prec_a * np.ones(shape[-1]), name = "prec_a", dtype = tf.float32)
self.prec_b = tf.Variable(initial_prec_b * np.ones(shape[-1]), name = "prec_b", dtype = tf.float32)
self.prec = tf.div(self.prec_a, self.prec_b, name = "prec")
self.var = tf.exp(self.logvar, name = "var")
self.a0 = a0
self.b0 = b0
self.shape = shape
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