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net.py
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#!/usr/bin/env python
# coding: utf-8
from chainer import Chain
import chainer.functions as F
import chainer.links as L
from chainer import Variable
import numpy as np
class MLP_MNIST_bbp(Chain):
def __init__(self, n_in = 784, n_hidden1 = 1200, n_hidden2 = 1200, n_out = 10, lr = 1e-4, prior_ratio = 0.5, prior_sigma_1 = np.exp(-1), prior_sigma_2 = np.exp(-7), prior_pho_var = .05):
super(MLP_MNIST_bbp, self).__init__(
mu1 = L.Linear(n_in, n_hidden1),
mu2 = L.Linear(n_hidden1, n_hidden2),
mu3 = L.Linear(n_hidden2, n_out),
pho1 = L.Linear(n_in, n_hidden1),
pho2 = L.Linear(n_hidden1, n_hidden2),
pho3 = L.Linear(n_hidden2, n_out),
w1 = L.Linear(n_in, n_hidden1),
w2 = L.Linear(n_hidden1, n_hidden2),
w3 = L.Linear(n_hidden2, n_out),
)
self.n_in = n_in
self.n_hidden1 = n_hidden1
self.n_hidden2 = n_hidden2
self.n_out = n_out
self.lr = lr
#mu1 = prior_ratio * np.random.normal(0,prior_sigma_1,n_in * n_hidden1) + (1 - prior_ratio) * np.random.normal(0,prior_sigma_1,n_in * n_hidden1)
#mu1 = mu1.reshape((n_in, n_hidden1)).astype(np.float32)
#mu2 = prior_ratio * np.random.normal(0,prior_sigma_1,n_hidden1 * n_hidden2) + (1 - prior_ratio) * np.random.normal(0,prior_sigma_1,n_hidden1 * n_hidden2)
#mu2 = mu2.reshape((n_hidden1, n_hidden2)).astype(np.float32),
#mu3 = prior_ratio * np.random.normal(0,prior_sigma_1,n_hidden2 * n_out) + (1 - prior_ratio) * np.random.normal(0,prior_sigma_1,n_hidden2 * n_out)
#mu3 = mu3.reshape((n_hidden2, 10)).astype(np.float32)
for i,m in enumerate([self.mu1,self.mu2,self.mu3]):
tmp_w = prior_ratio * np.random.normal(0,prior_sigma_1,m.W.shape[1] * m.W.shape[0]) + (1 - prior_ratio) * np.random.normal(0,prior_sigma_2,m.W.shape[1] * m.W.shape[0])
m.W = Variable(tmp_w.reshape(m.W.shape).astype(np.float32))
tmp_b = prior_ratio * np.random.normal(0,prior_sigma_1,m.b.shape[0]) + (1 - prior_ratio) * np.random.normal(0,prior_sigma_2,m.b.shape[0])
m.b = Variable(tmp_b.reshape(m.b.shape).astype(np.float32))
for i,m in enumerate([self.pho1,self.pho2,self.pho3]):
tmp_w = np.random.normal(0,prior_pho_var,m.W.shape[1] * m.W.shape[0])
m.W = Variable(tmp_w.reshape(m.W.shape).astype(np.float32))
tmp_b = np.random.normal(0,prior_pho_var,m.b.shape[0])
m.b = Variable(tmp_b.reshape(m.b.shape).astype(np.float32))
#for i,w in enumerate([self.w1,self.w2,self.w3]):
"""
self.mu1 = Variable(mu1.astype(np.float32))
self.mu2 = Variable(mu2.astype(np.float32))
self.mu3 = Variable(mu3.astype(np.float32))
pho1 = np.random.normal(0,prior_pho_var,n_in * n_hidden1)
#pho1 = Variable(pho1.reshape((n_in, n_hidden1)).astype(np.float32))
self.pho1 = Variable(pho1.astype(np.float32))
pho2 = np.random.normal(0,prior_pho_var,n_hidden1 * n_hidden2)
#pho2 = Variable(pho2.reshape((n_hidden1, n_hidden2)).astype(np.float32))
self.pho2 = Variable(pho2.astype(np.float32))
pho3 = np.random.normal(0,prior_pho_var,n_hidden2 * n_out)
#pho3 = Variable(pho3.reshape((n_hidden2, 10)).astype(np.float32))
self.pho3 = Variable(pho3.astype(np.float32))
#L.add_parames("pho1",)
"""
def __call__(self,x,t):
self.eps1_w = np.random.normal(0,1,self.mu1.W.shape).astype(np.float32)
self.eps2_w = np.random.normal(0,1,self.mu2.W.shape).astype(np.float32)
self.eps3_w = np.random.normal(0,1,self.mu3.W.shape).astype(np.float32)
#self.eps1_w = eps1_w.reshape(self.m1.W.shape)
#self.eps2_w = eps2_w.reshape(self.m2.W.shape)
#self.eps3_w = eps3_w.reshape(self.m3.W.shape)
self.w1.W = self.mu1.W + F.log(1 + F.exp(self.pho1.W))*Variable(self.eps1_w)
self.w2.W = self.mu2.W + F.log(1 + F.exp(self.pho2.W))*Variable(self.eps2_w)
self.w3.W = self.mu3.W + F.log(1 + F.exp(self.pho3.W))*Variable(self.eps3_w)
self.eps1_b = np.random.normal(0,1,self.mu1.b.shape).astype(np.float32)
self.eps2_b = np.random.normal(0,1,self.mu2.b.shape).astype(np.float32)
self.eps3_b = np.random.normal(0,1,self.mu3.b.shape).astype(np.float32)
self.w1.b = self.mu1.b + F.log(1 + F.exp(self.pho1.b))*Variable(self.eps1_b)
self.w2.b = self.mu2.b + F.log(1 + F.exp(self.pho2.b))*Variable(self.eps2_b)
self.w3.b = self.mu3.b + F.log(1 + F.exp(self.pho3.b))*Variable(self.eps3_b)
h1 = F.relu(self.w1(x))
h2 = F.relu(self.w2(h1))
h3 = self.w3(h2)
self.eps1 = [self.eps1_w,self.eps1_b]
self.eps2 = [self.eps2_w,self.eps2_b]
self.eps3 = [self.eps3_w,self.eps3_b]
#print("w1_shape:{}".format(w1.shape))
"""
w1 = F.reshape(w1,(self.n_in,self.n_hidden1))
w2 = F.reshape(w2,(self.n_hidden1,self.n_hidden2))
w3 = F.reshape(w3,(self.n_hidden2,self.n_out))
"""
#return w1,w2,w3
#print h3.shape,t.shape
#h3 = F.reshape(h3,(h3.shape[0],))
#print h3.shape,t.shape
return F.softmax_cross_entropy(h3,t)
def mu_hstack(self):
return F.hstack([F.flatten(self.mu1.W),F.flatten(self.mu1.b),F.flatten(self.mu2.W),F.flatten(self.mu2.b),F.flatten(self.mu3.W),F.flatten(self.mu3.b)])
def w_hstack(self):
return F.hstack([F.flatten(self.w1.W),F.flatten(self.w1.b),F.flatten(self.w2.W),F.flatten(self.w2.b),F.flatten(self.w3.W),F.flatten(self.w3.b)])
def sigma_hstack(self):
return F.log(1 + F.exp(F.hstack([F.flatten(self.pho1.W),F.flatten(self.pho1.b),F.flatten(self.pho2.W),F.flatten(self.pho2.b),F.flatten(self.pho3.W),F.flatten(self.pho3.b)])))
def update(self,model_num):
"""
print("update:{}".format(self.mu1.W.grad.shape))
print("update:{}".format(self.mu2.W.grad.shape))
print("update:{}".format(self.mu3.W.grad.shape))
print("update:{}".format(self.pho1.W.grad.shape))
"""
for m,w in zip([self.mu1,self.mu2,self.mu3],[self.w1,self.w2,self.w3]):
delta_w = m.W.grad + w.W.grad
#delta_w = m.W.grad# + w.W.grad
m.W = m.W - self.lr * delta_w
delta_b = m.b.grad + w.b.grad
#delta_b = m.b.grad# + w.b.grad
m.b = m.b - self.lr * delta_b
for pho,w,eps in zip([self.pho1,self.pho2,self.pho3],[self.w1,self.w2,self.w3],[self.eps1,self.eps2,self.eps3]):
delta_w = pho.W.grad + w.W.grad * eps[0] / (1 + F.exp(-1*pho.W))
#delta_w = pho.W.grad# + w.W.grad * eps[0] / (1 + F.exp(-1*pho.W))
#pho.W = pho.W - self.lr * delta_w / np.float32(model_num)
pho.W = pho.W - self.lr * delta_w
delta_b = pho.b.grad + w.b.grad * eps[1] / (1 + F.exp(-1*pho.b))
#delta_b = pho.b.grad# + w.b.grad * eps[1] / (1 + F.exp(-1*pho.b))
#pho.b = pho.b - self.lr * delta_b / np.float32(model_num)
pho.b = pho.b - self.lr * delta_b
#print("update:{}".format(self.mu1.grad.shape))
#print("mu1_shape:{}".format(self.mu1.shape))
class MLP_MNIST_bbp_(object):
def __init__(self, n_in = 784, n_hidden1 = 1200, n_hidden2 = 1200, n_out = 10, lr = 1e-4, prior_ratio = 0.5, prior_sigma_1 = np.exp(-1), prior_sigma_2 = np.exp(-7), prior_pho_var = .05):
super(MLP_MNIST_bbp, self).__init__()
self.n_in = n_in
self.n_hidden1 = n_hidden1
self.n_hidden2 = n_hidden2
self.n_out = n_out
self.lr = lr
mu1 = prior_ratio * np.random.normal(0,prior_sigma_1,n_in * n_hidden1) + (1 - prior_ratio) * np.random.normal(0,prior_sigma_1,n_in * n_hidden1)
#mu1 = mu1.reshape((n_in, n_hidden1)).astype(np.float32)
mu2 = prior_ratio * np.random.normal(0,prior_sigma_1,n_hidden1 * n_hidden2) + (1 - prior_ratio) * np.random.normal(0,prior_sigma_1,n_hidden1 * n_hidden2)
#mu2 = mu2.reshape((n_hidden1, n_hidden2)).astype(np.float32),
mu3 = prior_ratio * np.random.normal(0,prior_sigma_1,n_hidden2 * n_out) + (1 - prior_ratio) * np.random.normal(0,prior_sigma_1,n_hidden2 * n_out)
#mu3 = mu3.reshape((n_hidden2, 10)).astype(np.float32)
self.mu1 = Variable(mu1.astype(np.float32))
self.mu2 = Variable(mu2.astype(np.float32))
self.mu3 = Variable(mu3.astype(np.float32))
pho1 = np.random.normal(0,prior_pho_var,n_in * n_hidden1)
#pho1 = Variable(pho1.reshape((n_in, n_hidden1)).astype(np.float32))
self.pho1 = Variable(pho1.astype(np.float32))
pho2 = np.random.normal(0,prior_pho_var,n_hidden1 * n_hidden2)
#pho2 = Variable(pho2.reshape((n_hidden1, n_hidden2)).astype(np.float32))
self.pho2 = Variable(pho2.astype(np.float32))
pho3 = np.random.normal(0,prior_pho_var,n_hidden2 * n_out)
#pho3 = Variable(pho3.reshape((n_hidden2, 10)).astype(np.float32))
self.pho3 = Variable(pho3.astype(np.float32))
"""
mu1 = L.Linear(n_in, n_hidden1),
mu2 = L.Linear(n_hidden1, n_hidden2),
mu3 = L.Linear(n_hidden2, 10),
"""
#bnorm1 = L.BatchNormalization(n_hidden1),
#bnorm2 = L.BatchNormalization(n_hidden2)
def __call__(self):
eps1 = np.random.normal(0,1,self.n_in*self.n_hidden1).astype(np.float32)
eps2 = np.random.normal(0,1,self.n_hidden1*self.n_hidden2).astype(np.float32)
eps3 = np.random.normal(0,1,self.n_hidden2*self.n_out).astype(np.float32)
w1 = self.mu1 + F.log(1 + F.exp(self.pho1))*Variable(eps1)
w2 = self.mu2 + F.log(1 + F.exp(self.pho2))*Variable(eps2)
w3 = self.mu3 + F.log(1 + F.exp(self.pho3))*Variable(eps3)
#print("w1_shape:{}".format(w1.shape))
"""
w1 = F.reshape(w1,(self.n_in,self.n_hidden1))
w2 = F.reshape(w2,(self.n_hidden1,self.n_hidden2))
w3 = F.reshape(w3,(self.n_hidden2,self.n_out))
"""
return w1,w2,w3
def mu_hstack(self):
return F.hstack([self.mu1,self.mu2,self.mu3])
def sigma_hstack(self):
return F.log(1 + F.exp(F.hstack([self.pho1,self.pho2,self.pho3])))
def update(self):
print("update:{}".format(self.mu1.grad.shape))
print("update:{}".format(self.mu2.grad.shape))
print("update:{}".format(self.mu3.grad.shape))
print("update:{}".format(self.pho1.grad.shape))
self.mu1 = self.mu1 - self.lr * self.mu1.grad
self.mu2 = self.mu2 - self.lr * self.mu2.grad
self.mu3 = self.mu3 - self.lr * self.mu3.grad
self.pho1 = self.pho1 - self.lr * self.pho1.grad
self.pho2 = self.pho2 - self.lr * self.pho2.grad
self.pho3 = self.pho3 - self.lr * self.pho3.grad
#print("update:{}".format(self.mu1.grad.shape))
#print("mu1_shape:{}".format(self.mu1.shape))
class MLP_MNIST_dropput(Chain):
def __init__(self, n_in = 784, n_hidden1 = 1200, n_hidden2 = 1200):
super(MLPListNet, self).__init__(
l1 = L.Linear(n_in, n_hidden1),
l2 = L.Linear(n_hidden1, n_hidden2),
l3 = L.Linear(n_hidden2, 10),
#bnorm1 = L.BatchNormalization(n_hidden1),
#bnorm2 = L.BatchNormalization(n_hidden2)
)
def __call__(self, x):
h1 = F.relu(self.l1(x))
h1 = F.dropout(h1)
h2 = F.relu(self.l2(h1))
h2 = F.dropout(h2)
#h1 = F.relu(self.bnorm1(self.l1(x)))
#h1 = F.dropout(h1)
#h2 = F.relu(self.bnorm2(self.l2(h1)))
#h2 = F.dropout(h2)
return self.l3(h2)