LAMP.py

#!/usr/bin/python
from __future__ import division
from __future__ import print_function
"""
This file serves as an example of how to 
a) select a problem to be solved 
b) select a network type
c) train the network to minimize recovery MSE

"""
import numpy as np
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # BE QUIET!!!!
os.environ['KMP_DUPLICATE_LIB_OK']='True'
import tensorflow as tf

np.random.seed(1) # numpy is good about making repeatable output
tf.set_random_seed(1) # on the other hand, this is basically useless (see issue 9171)

# import our problems, networks and training modules
from tools import problems,networks,train

L=10000
M=250
N=500
SNR=20
pnz=.1
untied=False
T=8
shrink='bg'


# Create the basic problem structure.
prob = problems.bernoulli_gaussian_trial(kappa=None,M=M,N=N,L=L,pnz=pnz,SNR=SNR) #a Bernoulli-Gaussian x, noisily observed through a random matrix
#prob = problems.random_access_problem(2) # 1 or 2 for compressive random access or massive MIMO
print('Problem created ...')
print('A is:')
print(prob.A)

# build a LAMP network to solve the problem and get the intermediate results so we can greedily extend and then refine(fine-tune)
layers = networks.build_LAMP(prob,T=T,shrink=shrink,untied=False)
print('Building layers ... done')

# plan the learning
training_stages = train.setup_training(layers,prob,trinit=1e-3,refinements=(.5,.1,.01) )
print('Plan the learning ... done')

# do the learning (takes a while)
print('Do the learning (takes a while)')
# sess = train.do_training(training_stages,prob,'LAMP_bg_giid.npz')
sess = train.do_training(training_stages,prob,'LAMP_bg_giid.npz',10,10,50)

# train.plot_estimate_to_test_message(sess, training_stages, prob, 'LAMP_bg_giid.npz' )
# train.test_vector_sizes(sess, training_stages, prob, 'LAMP_bg_giid.npz' )
print('Evaluating network on test data ...')
train.evaluate_nmse(sess, training_stages, prob, 'LAMP_bg_giid.npz',SNR=SNR, L=L)

train_vars = train.get_train_variables(sess)




stop = 1;