def train(regularizations, dataset='mnist', fname='results_softmax_regression_mnist'):
train_test_ratio = 0.5
if dataset == 'mnist':
x, y, x_test, y_test = get_mnist(train_test_ratio)
elif dataset == 'iris':
x, y, x_test, y_test = get_iris(train_test_ratio)
results = []
for reg_i in xrange(0, len(regularizations)):
intermediate_results = []
print "reg:", reg_i
for i in xrange(0, 10):
print " i:", i
reg = regularizations[reg_i]
r = tf_softmax_regression.train_softmax(x, y, x_test, y_test, learning_rate=0.005,
max_iterations=1000000, regularization=reg,
w_diff_term_crit=0.0001, verbose=True)
intermediate_results.append(r)
results.append(intermediate_results)
# print results
for el_i in xrange(len(results)):
print el_i
el = results[el_i]
for e in el:
print e
import pickle
pickle.dump(results, open(fname, 'wb'))
def full_failure_recovery(dataset, model_type, regularizations):
train_test_ratio = 0.5
if dataset == 'mnist':
x, y, x_test, y_test = get_mnist(train_test_ratio)
elif dataset == 'iris':
x, y, x_test, y_test = get_iris(train_test_ratio)
elif dataset == 'diabetes':
x, y, x_test, y_test = get_diabetes(train_test_ratio)
elif dataset == 'covertype':
x, y, x_test, y_test = get_covertype(train_test_ratio)
elif dataset == 'boston':
x, y, x_test, y_test = get_boston(train_test_ratio)
else:
print "warning dataset:", dataset, "not found\nexiting!"
try:
sys.stdout.flush()
except IOError as e:
if e.errno == errno.EPIPE:
print "broken pipe?"
return
f_name_train = 'results_' + model_type + '_' + dataset
f_name_warmstart = 'results_warmstart_' + model_type + '_' + dataset
train_all_parallel(x,
y,
x_test,
y_test,
fname=f_name_train,
model_type=model_type,
w_diff_term_crit=w_diff_term_crit,
learning_rate=learning_rate,
regularizations=regularizations)
warmstart_all_parallel(x,
y,
x_test,
y_test,
fname_in=f_name_train,
fname_out=f_name_warmstart,
model_type=model_type,
w_diff_term_crit=w_diff_term_crit,
learning_rate=learning_rate,
regularizations=regularizations)
# # warmstart_all_parallel(fname_in='/home/nikste/workspace-python/parallel-failure-recovery/experiments_results/mnist/results_softmax_regression_mnist', dataset='mnist', fname_out='results_softmax_regression_warmstart_mnist')
print "training", dataset, model_type, "full failure"
try:
sys.stdout.flush()
except IOError as e:
if e.errno == errno.EPIPE:
print "broken pipe?"
visualize_training_result_from_parallel(f_name_train)
visualize_warmstart_result_from_parallel(f_name_warmstart, f_name_train)
print "great success!"
try:
sys.stdout.flush()
except IOError as e:
if e.errno == errno.EPIPE:
print "broken pipe?"
def warmstart(fname_in='results_softmax_regression_mnist',
dataset='mnist',
fname_out='results_softmax_regression_warmstart_mnist'):
train_test_ratio = 0.5
if dataset == 'mnist':
x, y, x_test, y_test = get_mnist(train_test_ratio)
elif dataset == 'iris':
x, y, x_test, y_test = get_iris(train_test_ratio)
regularizations = [100., 10., 1., 0.1, 0.01, 0.001, 0.0001]
import pickle
pretrained_models = pickle.load(open(fname_in, 'rb'))
results = []
for reg_i in xrange(0, len(pretrained_models)):
# all have same regularization parameter
intermediate_results = []
print "reg:", reg_i
for i in xrange(0, len(pretrained_models[reg_i])):
print " i:", i
r_int = []
reg_warmstart = pretrained_models[reg_i][i]['regularization']
model = pretrained_models[reg_i][i]['model:']
for j in xrange(0, len(regularizations)):
reg = regularizations[j]
print "----training:", reg, "with initialization of:", reg_warmstart
# r = tf_linear_regression.train_single_output(x, y, x_test, y_test, learning_rate=0.0001, max_iterations=1000000, regularization=reg, w_diff_term_crit=0.000001, verbose=True)
r = tf_logistic_regression.train_softmax_output(
x,
y,
x_test,
y_test,
learning_rate=0.1,
max_iterations=1000000,
regularization=reg,
w_diff_term_crit=0.000001,
verbose=True,
model=model)
r['initialized_with_regularization'] = reg_warmstart
r_int.append(r)
intermediate_results.append(r_int)
results.append(intermediate_results)
pickle.dump(results, open(fname_out, 'wb'))
def partial_failure_recovery(dataset, model_type, regularizations):
train_test_ratio = 0.5
if dataset == 'mnist':
x, y, x_test, y_test = get_mnist(train_test_ratio)
elif dataset == 'iris':
x, y, x_test, y_test = get_iris(train_test_ratio)
elif dataset == 'diabetes':
x, y, x_test, y_test = get_diabetes(train_test_ratio)
elif dataset == 'covertype':
x, y, x_test, y_test = get_covertype(train_test_ratio)
elif dataset == 'boston':
x, y, x_test, y_test = get_boston(train_test_ratio)
else:
print "warning dataset:", dataset, "not found\nexiting!"
try:
sys.stdout.flush()
except IOError as e:
if e.errno == errno.EPIPE:
print "broken pipe?"
return
f_name_train = 'results_' + model_type + '_' + dataset
f_name_warmstart = 'results_warmstart_corruption_' + model_type + '_' + dataset
# train_all_parallel(x, y, x_test, y_test,fname=f_name_train, model_type=model_type, w_diff_term_crit=w_diff_term_crit, learning_rate=learning_rate, regularizations=regularizations)
warmstart_partial_all_parallel(x, y, x_test, y_test, fname_in=f_name_train, fname_out=f_name_warmstart, model_type=model_type, w_diff_term_crit=w_diff_term_crit, learning_rate=learning_rate, regularizations=regularizations, corruption_ratio=0.5)
# # warmstart_all_parallel(fname_in='/home/nikste/workspace-python/parallel-failure-recovery/experiments_results/mnist/results_softmax_regression_mnist', dataset='mnist', fname_out='results_softmax_regression_warmstart_mnist')
print "training", dataset, model_type, "partial failure"
try:
sys.stdout.flush()
except IOError as e:
if e.errno == errno.EPIPE:
print "broken pipe?"
visualize_training_result_from_parallel(f_name_train)
visualize_warmstart_result_from_parallel(f_name_warmstart, f_name_train)
print "great success!"
try:
sys.stdout.flush()
except IOError as e:
if e.errno == errno.EPIPE:
print "broken pipe?"
def warmstart(fname_in='results_softmax_regression_mnist', dataset='mnist', fname_out='results_softmax_regression_warmstart_mnist'):
train_test_ratio = 0.5
if dataset == 'mnist':
x, y, x_test, y_test = get_mnist(train_test_ratio)
elif dataset == 'iris':
x, y, x_test, y_test = get_iris(train_test_ratio)
regularizations = [100., 10., 1., 0.1, 0.01, 0.001, 0.0001]
import pickle
pretrained_models = pickle.load(open(fname_in, 'rb'))
results = []
for reg_i in xrange(0, len(pretrained_models)):
# all have same regularization parameter
intermediate_results = []
print "reg:", reg_i
for i in xrange(0, len(pretrained_models[reg_i])):
print " i:", i
r_int = []
reg_warmstart = pretrained_models[reg_i][i]['regularization']
model = pretrained_models[reg_i][i]['model:']
for j in xrange(0, len(regularizations)):
reg = regularizations[j]
print "----training:", reg, "with initialization of:", reg_warmstart
# r = tf_linear_regression.train_single_output(x, y, x_test, y_test, learning_rate=0.0001, max_iterations=1000000, regularization=reg, w_diff_term_crit=0.000001, verbose=True)
r = tf_logistic_regression.train_softmax_output(x, y, x_test, y_test, learning_rate=0.1,
max_iterations=1000000, regularization=reg,
w_diff_term_crit=0.000001, verbose=True, model=model)
r['initialized_with_regularization'] = reg_warmstart
r_int.append(r)
intermediate_results.append(r_int)
results.append(intermediate_results)
pickle.dump(results, open(fname_out, 'wb'))