A = np.load('A_250_500.npy')
tao = 10
eps = 0.01 #this value will not influence the traning results, it's used to see the layers that different signals use, the average layer is reduced if eps increase
for i in range(16, 17):
prob = problems.bernoulli_gaussian_trial(A=A,
M=250,
N=500,
L=1000,
is_train=True)
layers = networks.build_LAMP_act(prob,
tao,
T=i,
eps=eps,
shrink='soft',
untied=False)
training_stages = train.setup_training(layers,
i,
prob,
tao=tao,
trinit=1e-4,
type='LAMP')
sess = train.do_training(training_stages,
prob,
'T_' + str(i) + '_tao_' + str(tao) + '.txt',
restorefile='LAMP-soft T=16 trainrate=0.001.npz',
maxit=400000,
better_wait=10000)
sess.close()
tf.reset_default_graph()
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 LISTA network to solve the problem and get the intermediate results so we can greedily extend and then refine(fine-tune)
layers = networks.build_LISTA(prob, T=6, initial_lambda=.1, 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, 'LISTA_bg_giid.npz')
initial_lambda=.1,
untied=False)
else:
layers = networks.build_LISTA(prob,
T=1,
initial_lambda=.1,
untied=False)
# plan the learning
training_stages = train.setup_training(layers,
prob,
i,
trinit=1e-4,
refinements=(.1, .01, .001),
start=i)
# do the learning (takes a while)
if i >= 2:
sess = train.do_training(
training_stages,
prob,
'LISTA T=' + str(i - 1) + ' trainrate=0.001.npz',
printfile='layer_' + str(i) +
'.txt') #'LISTA T='+str(i-1)+' trainrate=0.001.npz'
else:
sess = train.do_training(training_stages,
prob,
'Linear trainrate=0.001.npz',
printfile='layer_' + str(i) + '.txt')
tf.reset_default_graph()
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
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=8, shrink='soft', 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', 10, 3000,
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' )
train.evaluate_nmse(sess, training_stages, prob, 'LAMP_bg_giid.npz')
train_vars = train.get_train_variables(sess)
stop = 1
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 an LVAMP network to solve the problem and get the intermediate results so we can greedily extend and then refine(fine-tune)
layers = networks.build_LVAMP(prob, T=6, shrink='bg')
#layers = networks.build_LVAMP_dense(prob,T=3,shrink='pwgrid')
# plan the learning
training_stages = train.setup_training(layers,
prob,
trinit=1e-4,
refinements=(.5, .1, .01))
# do the learning (takes a while)
sess = train.do_training(training_stages, prob, 'LVAMP_bg_giid.npz')
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.bernoulli_gaussian_trial(
kappa=5, M=50, N=100, L=500, 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_kappa5.npz')
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)
# do the learning (takes a while)
sess = train.do_training(training_stages, prob, model_name)
