##### https://github.com/ShikamaruZhang/MANN

Revision

**914f5ddb06a3853f2f60a09d156702154b084cb3**authored by HE ZHANG on**27 July 2018, 20:03:31 UTC**, committed by HE ZHANG on**27 July 2018, 20:03:31 UTC****1 parent**3ce8055

Tip revision:

**914f5ddb06a3853f2f60a09d156702154b084cb3**authored by**HE ZHANG**on**27 July 2018, 20:03:31 UTC****change-index** Tip revision:

**914f5dd** ExpertWeights.py

```
"""
Class of ExpertWeights
"""
import numpy as np
import tensorflow as tf
class ExpertWeights(object):
def __init__(self, rng, shape , name):
"""rng"""
self.initialRNG = rng
"""shape"""
self.weight_shape = shape #4/8 * out * in
self.bias_shape = (shape[0],shape[1],1) #4/8 * out * 1
"""alpha and beta"""
self.alpha = tf.Variable(self.initial_alpha(), name=name+'alpha')
self.beta = tf.Variable(self.initial_beta(), name=name+'beta')
"""initialize parameters for experts i.e. alpha and beta"""
def initial_alpha_np(self):
shape = self.weight_shape
rng = self.initialRNG
alpha_bound = np.sqrt(6. / np.prod(shape[-2:]))
alpha = np.asarray(
rng.uniform(low=-alpha_bound, high=alpha_bound, size=shape),
dtype=np.float32)
return alpha
def initial_alpha(self):
alpha = self.initial_alpha_np()
return tf.convert_to_tensor(alpha, dtype = tf.float32)
def initial_beta(self):
return tf.zeros(self.bias_shape, tf.float32)
def get_NNweight(self, controlweights, batch_size):
a = tf.expand_dims(self.alpha, 1) #4*out*in -> 4*1*out*in
a = tf.tile(a, [1,batch_size,1,1]) #4*1*out*in -> 4*?*out*in
w = tf.expand_dims(tf.expand_dims(controlweights, -1), -1) #4*? -> 4*?*1*1
r = w * a #4*?*1*1 m 4*?*out*in
return tf.reduce_sum(r , axis = 0) #?*out*in
def get_NNbias(self, controlweights, batch_size):
b = tf.expand_dims(self.beta, 1) #4*out*1 -> 4*1*out*1
b = tf.tile(b, [1,batch_size,1,1]) #4*1*out*1 -> 4*?*out*1
w = tf.expand_dims(tf.expand_dims(controlweights, -1), -1) #4*? -> 4*?*1*1
r = w * b #4*?*1*1 m 4*?*out*1
return tf.reduce_sum(r , axis = 0) #?*out*1
def save_EP(alpha, beta, filename, num_experts):
for i in range(len(alpha)):
for j in range(num_experts):
a = alpha[i][j]
b = beta[i][j]
a.tofile(filename+'/cp%0i_a%0i.bin' % (i,j))
b.tofile(filename+'/cp%0i_b%0i.bin' % (i,j))
"""
def regularization_penalty(alpha, gamma):
number_alpha = len(alpha)
penalty = 0
for i in range(number_alpha):
penalty += tf.reduce_mean(tf.abs(alpha[i]))
return gamma * penalty / number_alpha
"""
```

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