import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # BE QUIET!!!!
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
# Create the basic problem structure.
prob = problems.bernoulli_gaussian_trial(
kappa=None, M=250, N=500, L=1000, pnz=.1,
SNR=40) #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
# 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=6, shrink='bg', untied=False)
# plan the learning
training_stages = train.setup_training(layers,
prob,
trinit=1e-3,
refinements=(.5, .1, .01))
# do the learning (takes a while)
sess = train.do_training(training_stages, prob, 'LAMP_bg_giid.npz')
"""
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['CUDA_VISIBLE_DEVICES'] = '7'
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)
from tools import problems, networks, train
A = np.load('A_250_500.npy')
prob = problems.bernoulli_gaussian_trial(
A=A, M=250, N=500, L=10000, is_train=False
) #a Bernoulli-Gaussian x, noisily observed through a random matrix
T = 14
layers = networks.build_LAMP(prob, T=T, shrink='soft', untied=False)
train.do_testing_another(layers, prob, T,
'LAMP-soft T=' + str(T) + ' trainrate=0.001.npz')
prob = problems.bernoulli_gaussian_trial(M=parsed.M,
N=parsed.N,
L=parsed.L,
pnz=parsed.pnz,
kappa=parsed.kappa,
SNR=parsed.SNR)
# save problems
base = parsed.dest_probs + "/problem_k{0:04.1f}_{1}".format(
parsed.kappa, parsed.id)
problems.save_problem(base, prob)
if parsed.model == 'LVAMP':
layers = networks.build_LVAMP(prob, T=parsed.T, shrink=parsed.shrink)
elif parsed.model == 'LAMP':
layers = networks.build_LAMP(prob,
T=parsed.T,
shrink=parsed.shrink,
untied=False)
elif parsed.model == 'LISTA':
layers = networks.build_LISTA(prob, T=parsed.T, untied=False)
else:
raise ValueError('Wrong model designated')
# plan the training
training_stages = train.setup_training(layers,
prob,
trinit=1e-3,
refinements=(.5, .1, .01))
# get saved model file name as 'models/LAMP_bg_k05.1_2.npz'
model_name = parsed.dest_models + '/' + parsed.model + '_' + parsed.shrink + \
'_k{0:04.1f}_{1}.npz'.format(parsed.kappa, parsed.id)