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run_inference_PRIMAL_HLS.py
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run_inference_PRIMAL_HLS.py
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import tensorflow as tf
from model_forward_KL_HLS_inference import Inference_Model_HLS
from GMM_para import GaussianMixture_data
from GMM_para import GaussianMixture_data_diag_cov
from GMM_para import cal_KL_symm_diag
from GMM_para import cal_KL_symm
import scipy.io as sio
import numpy as np
import time
import h5py
class Inferencer(object):
def optimize_adam(self, loss, train_vars=None, lr=1e-2):
optimizer = tf.train.AdamOptimizer(lr)
if train_vars is None:
train_op = optimizer.minimize(loss, global_step=self.global_step,
gate_gradients=optimizer.GATE_NONE)
else:
train_op = optimizer.minimize(loss, var_list=train_vars,
global_step=self.global_step,
gate_gradients=optimizer.GATE_NONE)
return train_op
def optimize_adagrad(self, loss, train_vars=None, lr=1e-2):
optimizer = tf.train.RMSPropOptimizer(learning_rate=self.learning_rate, decay=0.9) # adagrad with momentum
if train_vars is None:
train_op = optimizer.minimize(loss, global_step=self.global_step,
gate_gradients=optimizer.GATE_NONE)
else:
train_op = optimizer.minimize(loss, var_list=train_vars,
global_step=self.global_step,
gate_gradients=optimizer.GATE_NONE)
return train_op
def __init__(self, config, PML_para, session,mean_p,Sigma_p,weight_p,KL_m_norm,trial_i):
self.config = config
self.session = session
dim=mean_p.shape[0]
n_gmm=mean_p.shape[2]
K_b=mean_p.shape[1]
self.trial_i=trial_i
# --- create model ---
# self.p_target = GaussianMixture_data_diag_cov(mean_p,Sigma_p,weight_p,KL_m_norm)
self.p_target=GaussianMixture_data(mean_p,Sigma_p,weight_p,KL_m_norm)
# self.p_target = GaussianMixture_data_diag_cov(mean_p,Sigma_p,weight_p,KL_m_norm)
# [mu_data,Sigma_data,weight_data]=self.session.run([self.p_target.mean,self.p_target.Sigma,self.p_target.weight])
# Prcs_data = np.zeros((n_gmm,dim,K_b), dtype=np.float32)
# for i in range(n_gmm):
# for j in range(K_b):
# Prcs_data[i,:,j] = np.linalg.inv(Sigma_data[i,:,j])
# KL_mat=cal_KL_symm_diag(mu_data,Sigma_data,weight_data,Prcs_data)
#initialize psi_var
#self.n_temp=tf.Variable(2.0,name='temp')
#dim, n_gmm, K_r, K_b, d_w, d_y, d_z, p_model, psi_var
self.n_vs = config.n_vs
# sess, PML_para,config, dim,n_gmm, K_r, K_b, d_v, n_v, p_model
# self.model = Model_KHLS(self.session,self.config,dim, n_gmm, self.config.K_r, K_b, self.config.d_v,self.config.d_w, self.config.d_y, self.config.d_z, self.n_vs,self.config.n_ps_v,self.p_target)
self.model = Inference_Model_HLS(self.session,PML_para,self.config,dim, n_gmm, self.config.K_r, K_b, self.config.d_v, self.n_vs,self.p_target)
# self.model = Model_HLS_diag_cov(self.session,self.config,dim, n_gmm, self.config.K_r, K_b, self.config.d_v,self.config.d_w, self.config.d_y, self.config.d_z,
# self.config.d_C_1, self.n_vs,self.p_target)
# --- optimizer ---
self.global_step = tf.Variable(0, name="global_step")
self.learning_rate = config.learning_rate
if config.lr_weight_decay:
self.learning_rate = tf.train.exponential_decay(
self.learning_rate,
global_step=self.global_step,
decay_steps=10000,
decay_rate=0.1,
staircase=True,
name='decaying_learning_rate'
)
self.summary_op = tf.summary.merge_all()
# self.summary_writer = tf.summary.FileWriter('./graph',tf.get_default_graph())
self.saver = tf.train.Saver()
# self.checkpoint_secs = 10 # 5 min
self.train_lv = self.optimize_adam(self.model.lv_loss,
train_vars=self.model.lv_train, lr=1e-2)
tf.global_variables_initializer().run()
# init_Prcs=session.run(self.model.q_approx.Prcs)
# sio.savemat('./train_data_HLS/Prcs_init.mat', {'Prcs_init': init_Prcs})
#
# v_g=tf.gradients(self.model.lv_loss,self.model.v)
# v_g_val=self.session.run(v_g)
# print(v_g_val)
def train(self):
# training q
it=0
MIN_KL=1e10
#q_v_val=self.session.run(self.model.q_v)
#sio.savemat('./train_data_HLS/q_init.mat', {'q': q_v_val})
while it<=self.config.MAXIT:
start=time.time()
kl_loss=0
for n_updates in range(1, 1 + self.config.max_steps):
step, summary, loss = self.run_single_step(self.model.lv_loss,self.train_lv)
kl_loss=loss
finish=time.time()
print('iteration time: ', str(finish-start))
if it>=self.config.start_update_q:
self.model.update_q_v()
if it>=self.config.start_MC_q and it%2==0:
#q_var_before = self.session.run(self.model.q_v)
#sio.savemat('./train_data_HLS/q_before.mat', {'q_before': q_var_before})
sample, kl_arr_val, q_sample_before = self.session.run(self.model.MH_sample())
K_b=q_sample_before.shape[1]
q_sample_shuffle=np.zeros([1,K_b,self.config.K_r],dtype=np.float32)
for j_b in range(K_b):
q_sample_shuffle[0,j_b,:]=np.random.permutation(q_sample_before[0,j_b,:])
# # print(sample)
self.session.run(self.model.MH_sample_update(sample, kl_arr_val, q_sample_shuffle,it))
#q_var_after = self.session.run(self.model.q_v)
#sio.savemat('./train_data_HLS/q_after.mat', {'q_after': q_var_after})
# break
if kl_loss<MIN_KL:
MIN_KL = kl_loss
weight_hat = self.session.run(self.model.q_approx.weight)
sio.savemat('./inference_data_HLS/weight_hat_' + str(self.trial_i) + '_min.mat', {'weight_hat': weight_hat})
mean_hat = self.session.run(self.model.q_approx.mean)
sio.savemat('./inference_data_HLS/mu_hat_' + str(self.trial_i) + '_min.mat', {'mean_hat': mean_hat})
Prcs_hat = self.session.run(self.model.q_approx.Prcs)
sio.savemat('./inference_data_HLS/Prcs_hat_' + str(self.trial_i) + '_min.mat', {'Prcs_hat': Prcs_hat})
v_var = self.session.run(self.model.v)
sio.savemat('./inference_data_HLS/Inference_v_'+str(self.trial_i)+'_min.mat', {'v': v_var})
weight_hat = self.session.run(self.model.q_approx.weight)
sio.savemat('./inference_data_HLS/weight_hat.mat', {'weight_hat': weight_hat})
mean_hat = self.session.run(self.model.q_approx.mean)
sio.savemat('./inference_data_HLS/mu_hat.mat', {'mean_hat': mean_hat})
Prcs_hat = self.session.run(self.model.q_approx.Prcs)
sio.savemat('./inference_data_HLS/Prcs_hat.mat', {'Prcs_hat': Prcs_hat})
q_var = self.session.run(self.model.q_v)
sio.savemat('./inference_data_HLS/q_var.mat', {'q': q_var})
v_var = self.session.run(self.model.v)
sio.savemat('./inference_data_HLS/Inference_v.mat', {'v': v_var})
print('iteration: ', str(it))
print('loss: ', kl_loss / self.config.n_gmm)
it += 1
weight_hat = self.session.run(self.model.q_approx.weight)
sio.savemat('./inference_data_HLS/weight_hat_'+str(self.trial_i)+'.mat', {'weight_hat': weight_hat})
mean_hat = self.session.run(self.model.q_approx.mean)
sio.savemat('./inference_data_HLS/mu_hat_'+str(self.trial_i)+'.mat', {'mean_hat': mean_hat})
Prcs_hat = self.session.run(self.model.q_approx.Prcs)
sio.savemat('./inference_data_HLS/Prcs_hat_'+str(self.trial_i)+'.mat', {'Prcs_hat': Prcs_hat})
v_var = self.session.run(self.model.v)
sio.savemat('./inference_data_HLS/Inference_v_'+str(self.trial_i)+'.mat', {'v': v_var})
# v_var = self.session.run(self.model.v)
# H_w_var = self.session.run(self.model.H_w)
# H_y_var = self.session.run(self.model.H_y)
# H_z_var = self.session.run(self.model.H_z)
# b_var=self.session.run(self.model.b)
# C_b_1_var = self.session.run(self.model.C_b_1)
# C_1_var = self.session.run(self.model.C_1)
# C_out_b_var = self.session.run(self.model.C_out_b)
# C_out_var = self.session.run(self.model.C_out)
# mu_var = self.session.run(self.model.mu)
# xi_var = self.session.run(self.model.xi)
# q_var=self.session.run(self.model.q_v)
# sio.savemat('./train_data_HLS/PML_para_'+str(self.trial_i)+'.mat', {'v': v_var, 'H_w':H_w_var, 'H_y':H_y_var,
# 'H_z':H_z_var, 'b':b_var, 'C_1':C_1_var,'C_b_1':C_b_1_var,
# 'C_out':C_out_var, 'C_out_b':C_out_b_var,'mu':mu_var, 'xi':xi_var, 'q':q_var,
# 'config':self.config})
def run_single_step(self,min_loss,train_model):
fetch = [self.global_step, self.summary_op, min_loss, train_model]
fetch_values = self.session.run(fetch)
[step, summary, loss] = fetch_values[:3]
return step, summary, loss
# def update_q_v(self):
# kl_trace = 0.5 * tf.reduce_sum(
# tf.multiply(tf.expand_dims(self.model.q_approx.Prcs, axis=3), tf.expand_dims(self.model.p_model.Sigma, axis=4)), [1, 2])
# # mu diff n_gmm*dim*K_b*K_r
# mu_diff = tf.subtract(tf.expand_dims(self.model.q_approx.mean, axis=2), tf.expand_dims(self.model.p_model.mean, axis=3))
#
# # n_gmm * dim *dim* K_b * K_r
# mu_diff_dot = tf.multiply(tf.expand_dims(mu_diff, axis=1), tf.expand_dims(mu_diff, axis=2))
# expd_Prcs = tf.tile(tf.expand_dims(self.model.q_approx.Prcs, axis=3), [1, 1, 1, self.model.K_b, 1])
#
# # mu^T*Prcs*mu term: n_gmm*K_b*K_r
# mu_diff_prcs = 0.5 * tf.reduce_sum(tf.multiply(mu_diff_dot, expd_Prcs), [1, 2])
#
# # logdet term n_gmm*K_b*K_r logdet_p=n_gmm*K_b
# logdet_prcs = 0.5 * self.model.q_approx.logdet_q
#
# clip_q_weight = tf.clip_by_value(self.model.q_approx.weight, 1e-30, 1.0)
# log_pi_hat = tf.tile(tf.expand_dims(tf.log(clip_q_weight), axis=1), [1, self.model.K_b, 1])
#
# log_term=log_pi_hat+tf.multiply(self.n_vs,(logdet_prcs-mu_diff_prcs-kl_trace))
# self.model.q_v=tf.nn.softmax(tf.stop_gradient(log_term),axis=2)
#
# log_term_var=self.session.run(log_term)
# q_var=self.session.run(self.model.q_v)
#sio.savemat('./train_data_HLS/log_term_q.mat', {'log_term_q': log_term_var})
#sio.savemat('./train_data_HLS/q_var.mat', {'q_var': q_var})
def main():
import argparse
# filename='data/bbc_wiki_100d_50K/train_gmm_bbc.mat'
# filename='data/bbc_wiki_100d_gmm_K_5_cov_val/gmm_train_test_K_5_wiki_100d_categ_3'
#filename='eye_fixation_K_3_new'
filename='Flow_Cytometry_Data/AML_60_K_2_bal_6_mark'
# filename='data/music_genre_gmm_data/gmm_train_K_6_dim_42_no_hiphop'
try:
gmm_data = sio.loadmat(filename+'.mat')
mean_p = np.float32(gmm_data['mu_test'])
print(mean_p.shape)
Sigma_p = np.float32(gmm_data['Sigma_test'])
print(Sigma_p.shape)
weight_p = np.float32(gmm_data['weight_test'])
print(weight_p.shape)
try:
KL_data=sio.loadmat(filename+'_test_KL_M.mat')
KL_mat_norm=KL_data['KL_M_norm_py']
except:
#diagonal
[dim, K_b, n_gmm] = mean_p.shape
Prcs_data = np.zeros((dim, K_b, n_gmm), dtype=np.float32)
Sigma_data = np.zeros((dim, K_b, n_gmm), dtype=np.float32)
for i in range(n_gmm):
for j in range(K_b):
Sigma_data[:, j, i] = np.diag(Sigma_p[:, :, j, i])
Prcs_data[:, j, i] = 1 / (Sigma_data[:, j, i])
KL_mat = np.zeros((n_gmm, n_gmm), dtype=np.float32)
for i in range(n_gmm):
for j in range(i, n_gmm):
KL_mat[i, j] = cal_KL_symm_diag(mean_p[:, :, i], Sigma_data[:, :, i], Prcs_data[:, :, i],
weight_p[:, i], mean_p[:, :, j], Prcs_data[:, :, j],
weight_p[:, j]) + \
cal_KL_symm_diag(mean_p[:, :, j], Sigma_data[:, :, j], Prcs_data[:, :, j],
weight_p[:, j], mean_p[:, :, i], Prcs_data[:, :, i], weight_p[:, i])
print('finish ' + str(i))
# full
# [dim, K_b, n_gmm] = mean_p.shape
# Prcs_data = np.zeros((dim, dim, K_b, n_gmm), dtype=np.float32)
# for i in range(n_gmm):
# for j in range(K_b):
# Prcs_data[:, :, j, i] = np.linalg.inv(Sigma_p[:, :, j, i])
# KL_mat = np.zeros((n_gmm, n_gmm), dtype=np.float32)
# for i in range(n_gmm):
# for j in range(i, n_gmm):
# KL_mat[i, j] = cal_KL_symm(mean_p[:, :, i], Sigma_p[:, :, :, i], Prcs_data[:, :, :, i],
# weight_p[:, i], mean_p[:, :, j], Prcs_data[:, :, :, j], weight_p[:, j]) + \
# cal_KL_symm(mean_p[:, :, j], Sigma_p[:, :, :, j], Prcs_data[:, :, :, j],
# weight_p[:, j], mean_p[:, :, i], Prcs_data[:, :, :, i], weight_p[:, i])
# print('finish ' + str(i))
KL_mat_sym = KL_mat + np.transpose(KL_mat)
# sio.savemat('eye_KL_mat.mat',{'KL_M_py':KL_mat_sym})
KL_mat_norm = KL_mat_sym/np.max(KL_mat_sym)
sio.savemat(filename + '_test_KL_M.mat', {'KL_M_norm_py': KL_mat_norm})
except:
gmm_data = h5py.File(filename+'.mat')
mean_p = np.transpose(np.float32(gmm_data['mu_test']), (2, 1, 0))
print(mean_p.shape)
Sigma_p = np.transpose(np.float32(gmm_data['Sigma_test']), (2, 3, 1, 0))
print(Sigma_p.shape)
weight_p = np.transpose(np.float32(gmm_data['weight_test']), (1, 0))
print(weight_p.shape)
try:
KL_data=sio.loadmat(filename+'_test_KL_M.mat')
KL_mat_norm=KL_data['KL_M_norm_py']
except:
[dim, K_b, n_gmm] = mean_p.shape
Prcs_data = np.zeros((dim, K_b, n_gmm), dtype=np.float32)
Sigma_data = np.zeros((dim, K_b, n_gmm), dtype=np.float32)
for i in range(n_gmm):
for j in range(K_b):
Sigma_data[:, j, i] = np.diag(Sigma_p[:, :, j, i])
Prcs_data[:, j, i] = 1 / (Sigma_data[:, j, i])
KL_mat = np.zeros((n_gmm, n_gmm), dtype=np.float32)
for i in range(n_gmm):
for j in range(i, n_gmm):
KL_mat[i, j] = cal_KL_symm_diag(mean_p[:, :, i], Sigma_data[:, :, i], Prcs_data[:, :, i],
weight_p[:, i], mean_p[:, :, j], Prcs_data[:, :, j],
weight_p[:, j]) + \
cal_KL_symm_diag(mean_p[:, :, j], Sigma_data[:, :, j], Prcs_data[:, :, j],
weight_p[:, j], mean_p[:, :, i], Prcs_data[:, :, i], weight_p[:, i])
print('finish ' + str(i))
KL_mat_sym = KL_mat + np.transpose(KL_mat)
KL_mat_norm = KL_mat_sym/np.max(KL_mat_sym)
sio.savemat(filename + '_test_KL_M.mat', {'KL_M_norm_py': KL_mat_norm})
PML_para=sio.loadmat('train_data_HLS/PML_para_26.mat')
#print(PML_para['beta'].shape)
parser = argparse.ArgumentParser()
parser.add_argument('--max_steps', type=int, default=PML_para['config']['max_steps'][0,0][0,0], required=False)
parser.add_argument('--MAXIT', type=int, default=PML_para['config']['MAXIT'][0,0][0,0], required=False)
parser.add_argument('--learning_rate', type=float, default=PML_para['config']['learning_rate'][0,0][0,0], required=False)
parser.add_argument('--lr_weight_decay', action='store_true', default=True)
parser.add_argument('--K_r', type=int, default=PML_para['config']['K_r'][0,0][0,0], required=False)
parser.add_argument('--d_v', type=int, default=PML_para['config']['d_v'][0,0][0,0], required=False)
parser.add_argument('--d_w', type=int, default=PML_para['config']['d_w'][0,0][0,0], required=False)
parser.add_argument('--d_y', type=int, default=PML_para['config']['d_y'][0,0][0,0], required=False)
parser.add_argument('--d_z', type=int, default=PML_para['config']['d_z'][0,0][0,0], required=False)
parser.add_argument('--d_C_1', type=int, default=PML_para['config']['d_C_1'][0,0][0,0], required=False)
parser.add_argument('--d_mu_1', type=int, default=PML_para['config']['d_mu_1'][0,0][0,0], required=False)
parser.add_argument('--reg_v', type=float, default=PML_para['config']['reg_v'][0,0][0,0], required=False)
parser.add_argument('--reg_wyz', type=float, default=PML_para['config']['reg_wyz'][0,0][0,0], required=False)
parser.add_argument('--reg_kl', type=float, default=PML_para['config']['reg_kl'][0,0][0,0], required=False)
#parser.add_argument('--n_ps_v',type=int,default=PML_para['config']['n_ps_v'][0,0][0,0],required=False)
parser.add_argument('--lamda_kern',type=float, default=PML_para['config']['lamda_kern'][0,0][0,0],required=False)
parser.add_argument('--gamma_width',type=float,default=PML_para['config']['gamma_width'][0,0][0,0],required=False)
parser.add_argument('--gamma_scale',type=float,default=PML_para['config']['gamma_scale'][0,0][0,0],required=False)
parser.add_argument('--poly_scale',type=float,default=PML_para['config']['poly_scale'][0,0][0,0],required=False)
parser.add_argument('--T_0', type=float, default=PML_para['config']['T_0'][0,0][0,0], required=False)
parser.add_argument('--T_coeff', type=float, default=PML_para['config']['T_coeff'][0,0][0,0], required=False)
parser.add_argument('--start_update_q', type=int, default=PML_para['config']['start_update_q'][0,0][0,0], required=False)
parser.add_argument('--start_MC_q', type=int, default=PML_para['config']['start_MC_q'][0,0][0,0], required=False)
parser.add_argument('--n_gmm', type=int, default=mean_p.shape[2], required=False)
parser.add_argument('--n_vs',type=float,default=PML_para['config']['n_vs'][0,0][0,0],required=False)
config = parser.parse_args()
session_config = tf.ConfigProto(
allow_soft_placement=True,
gpu_options=tf.GPUOptions(allow_growth=True),
)
for i in range(30):
with tf.Graph().as_default(), tf.Session(config=session_config) as sess:
# with tf.device('/device:GPU:0'):
with tf.device('/cpu:0'):
trainer = Inferencer(config, PML_para,sess, mean_p, Sigma_p, weight_p,KL_mat_norm, i)
print('finish initialization')
trainer.train()
if __name__ == '__main__':
main()